VOLTAGE UNBALANCE IMPACT ON MOTORS

Voltage unbalance and its most severe form – single phasing cause up to 14% of motor failures. It's important to understand that maximum current unbalance isn't defined in standards for motors in the field . Voltage unbalance, which has direct impact on the operating life, reliability and characteristics of an electric motor, is defined. The National Electrical Manufacturers Association (NEMA) standard MG-1 defines the maximum voltage unbalance to be applied to an electric motor as 5%, and notes that a derating factor must be applied to a motor operating with a voltage unbalance. Voltage unbalance is relatively simple to calculate. It requires measurement of the phase-to-phase voltage of the supply to a three-phase motor. The first step is to take all three measured voltages, add them together and divide by three. This will be your Vave. Next, subtract the voltage furthest from the Vave. Change it to a positive value, then divide by Vave., and multiply by 100%. With the data, you can contruct a graph or chart of voltage unbalance derating factor from 0.0 – 1.0 and voltage unbalance (%). Applying the chart, you obtain the optimum voltage unbalance derating factor and voltage unbalance (%) This will help to determine and select a suitable motor, operating conditions and its critical parts. The voltage unbalance derating factor can be determined with the help of Technological Inheritance Coefficient through component quality factor. This coefficient is also used to find quality growth and degradation. The control limit of both quality growth and degradation is set with Technological Inheritance Coefficient, which is used to determine the voltage unbalance, failure, life and reliability threshold points. With the help of Technological Inheritance Model, you can identify the cause of failure, unbalance and misalignment as well as variations from set-points. At this point, if you see current unbalance with low-voltage unbalance, try rotating the phases, then recheck current. (Rotate phases by moving the lead from phase A to phase B, phase B to phase C and phase C to phase A. This doesn't change the direction of rotation). If upon rechecking current, the unbalance moves, it's motor, part and process-related. If the unbalance stays in the same location or disappears, it's supply-related.

FAILURE ANALYSIS OF MACHINE SHAFTS

Whether related to motors, pumps or any other types of industrial machinery, shaft failure analysis is frequently misunderstood, and often being perceived as difficult and expensive. Analysis should be relatively straight forward in most most machine shafts, however, since the failure typically provides strong clues to the type and magnitude of forces acting on the shaft and the direction they acted in. The failed part can therefore tell exactly what happened. At this point, it's also necessary to say that caution on interpreting the clues needs to be taken, while the oldest part of a fatigue failure typically has the smoothest surface on at least 98% of the time – it's still crucial to look carefully at the failed part in the area of the origin. The shaft surface will describe the force. A crack always grows perpendicular to the plane of maximum stress. Therefore, if we can determine the plane of maximum and minimum quality conditions of the shaft surface, we then can evaluate the most and least resistance to failure, which can be used to determine the reliability and life of the shaft as well as its original and final shaft surface conditions. Many times, we've seen shafts where the originating force was torsion with a sort of angular cracks, but the majority of crack propagation was in bending – fooling inspectors into thinking that bending was primary force. At this point, it is interesting to note that, it is a good thing to determine the maximum and minimum shaft surface conditions, but quite better to determine the conditions in between, which will help to know the reliability/life growth and degradation of shaft. The knowledge of the maximum, minimum and real-time points of shaft conditions will help to determine control limits, threshold , and failure points. Technological Inheritance Coefficients can be used to assess the operating conditions and performance of shafts, since technological inheritance is the transfer of the properties of object (e.g the shaft quality and process effectiveness parameters) from its initial to final operation of a technological system. Technological Inheritance Model can therefore be used to determine maximum and minimum reliability/life, reliability/life growth and degradation, threshold points and failures of shafts as well as to evaluate the effectiveness/efficiency of the process. The Knowledge of Reliability growth can be used to assess the optimum reliability of shaft for the best selection and purchase of shafts and equipment as well as knowing the Reliability degradation can be used to assess the failures of shafts and equipment. Real-Time Reliability/Effectiveness growth and degradation test during operation will definitely lead to a sustainable development in design, manufacturing, operation and maintenance.

MOVING FROM PHYSICAL TO VIRTUAL ASSESSMENT OF PROCESSES AND PARTS

The challenge for durability of part remains how to accelerate the analysis process. The minimum acceptable period for a “full” analysis is about 12 hours, which is equivalent to running the analysis over night. Operating computing together with parallel processing are ideally suited to meet the computational challenges because part fatigue life calculations are usually similar at each nodes or element (e.g part quality and process condition parameters). Selecting which part quality and process conditions are to be analyzed more intelligently as well as optimizing time history, quality parameters, process operating conditions and time reduction methods will go a long way towards meeting the challenge. Real-Time data acquisition of part quality and process condition parameters will add intelligence to fatigue life analysis within software application. The software should be able to choose the most cost effective fatigue life method for components and systems with regards to its development and service conditions. It will also help to keep records that explains why such cost effective selections are made. At this point, it is worthy to note that the change to a virtual development process has been driven by a number of factors, particularly reduced development time, increased model designs, increased complexity and the need to optimize performance and costs. These multiple factors have led the introduction of new methods that can address the actual durability and reliability problems. Since fatigue results are usually expressed in terms of the number of cycles or repeats of a particular loading sequence, required to reach a specific criterion at a location. The fact is that these results are sometimes associated with physical quantities such as hours, miles, fracture of a durability route. At the same time, it must be noted that, these results are of course sensitive to each of the major inputs, summed up in load, geometry and material that produce component and system life and reliability. The new failure mode analysis method should therefore take into consideration the compound resultant factor of load, geometry and material characteristics. The compound factor can be determined and assessed during development stages, which includes the initial, surface finish operations of parts and throughout the operations of a technological system as well as during the service operation of equipment. Analyzing more intelligently equipment part and process conditions with a technological inheritance assessment technique from the initial to final operation will help to meet the challenges of durability and reliability of component and system. Technological Inheritance is the transfer of the properties of part and process from the initial to final operation in a particular technological system. Technological Inheritance technique can be used to predict and determine the outcome of development and service operations. The outcome of a particular part surface finish can be optimized to produce maximum durability and life at minimum cost, which in turn is used to set up optimum safety controls and process conditions as well as determine optimum reliability of equipment parts and systems. The optimization results of the surface finish can also be used to determine the optimum points or levels of the operating service conditions. The sensitivity of these outcomes is assessed with reference to these optimum values and their variability. With the help of Technological Inheritance Model, it is possible to assess the sensitivity and variability of part surface quality and process condition parameters like reliability growth, degradation, failures and life of components/systems. Any variation from the optimum values can be captured, measured and monitored. Sensitivity to variation in loading magnitude is particularly acute due to logarithm relationship between load and life or reliability. 10% in load variation for example can alter predicted life or reliability by a factor of two. Overloading should not be permitted and should be controlled, wherever possible. From designer's point of view, variation in loading conditions are largely the results of variability in customer usage. To a large extent, this variability is said to be beyond the control of designers. Other than through the provision of adequate safety factors With Technological Inheritance Technique, Designers can now be equipped with software programs, testing devices and measuring instruments that can help to control the variability of parts and process conditions. If operators are given the right instruments and data acquisition devices that can read the condition variations from the initial to final operation, equipment life and reliability can be controlled. At this point, it is worth knowing that, the material behavior and impact of geometry on the other hand can usually be defined more precisely and the condition variability is usually more less than the associated with load, but the material and geometrical conditions must also be controlled to prevent and eradicate hidden failures with the help of Technological inheritance model through the technological and service operations. Technological Inheritance Model, Yi = aYi-1b, where Yi – the final quality parameter of component (material and Part geometry), derived through physical measurement, Yi-1 – the initial quality parameter of component, derived through Regression equation of a part surface finish condition for a particular purpose and its physical measurement, a - technological inheritance coefficient for the transfer of component quality parameters from one point to the other and b - technological inheritance coefficient for the transfer of process condition parameters from one operation to operation. These coefficients can be used to convert physical to virtual measurements, integrate multiple parameters into a single factor,, used to set up threshold points and control limits. Technological inheritance coefficients “a” and “b” range between 0.0 and 1.0, where 0.0 represents minimum, and 1.0 as maximum values of the physical parameters in the model. The real-time data of the inheritance model can be used to design reliability/life software programs, measuring instruments for reliability/life, and for inspecting, testing and monitoring devices of reliability/life.

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Bearings

Technological Inheritance Technique can be used to select the best costs maintenance strategies, operating conditions, reliability and life of equipment.

Temperature Monitor

Technological Inheritance Technique can be used to measure and estimate the optimum costs, maintenance strategies, temperature monitor, equipment and operating conditions.

Digital Multimeter

With Technological Inheritance Technique, Optimum Costs, Maintenance Strategies, Instruments, Equipments and Operating Conditions can be selected.

Hammerdrill

COMPONENT QUALITY FACTOR AND RELIABILITY MEASUREMENT

Quality factor and reliability measurement can be characterized into three areas - detection, diagnosis and prognosis. Detection uses the most basic form of quality and reliability measurement, where overall reliability or quality is measured on a broadband basis, within a range of 0 - 100% or 0 to 1.0. In machines with low quality or reliability of components, the signal produced by low quality factor (Qi) or reliability coefficient (Ri) may imply incipient defects/failure, which is also proportional to high energy level/age indicating severe defects. This type of measurement information can be useful when used for trending, where an increasing level of quality or reliability is an indicator of the growth of the machine condition and the decrease in quality and reliability is an indication of degradation of machine condition, which eventually leads to failure. Trend analysis involves plotting the quality factor or reliability coefficient values as a function of time, frequency and technological inheritance coefficient, using these to predict when the machine must be taken out of service for repairs. Another way of using the measurement is to compare the optimal condition levels with the real-time data event conditions for different type of equipment. Although broadband measurements provide a good starting point for fault/failure detection in terms of time, it has limited diagnostic capability and although a fault/failure can be identified, it may not give a reliable indication of where the fault/failure is (e.g deterioration, misalignment and unbalance). Where an improved diagnostic capability is required, frequency and technological inheritance analysis can be used. This gives a much earlier indication of the development of fault/failure and also the source or root cause of the fault/failure. Under this condition, the component quality condition parameters (Yi) as well as its corresponding technological inheritance coefficients (ai)/(bi) must be known and analyzed to find out how each quality parameter degrades with time from its optimal value. The real-time data of each quality parameters helps to diagnose the root-cause of faults/failure. The reliability coefficient of the different component can be calculated with the help of technological inheritance coefficients of the different quality parameters and the quality factor of components. Technological inheritance coefficients are used to set threshold set points of the time for failures, apart from for determining the maximum and minimum quality/reliability, which makes measurements to be trended over time and in terms frequency and technological inheritance coefficients. Frequency spectrum analysis plays an important role in detecting and diagnosing machine faults/failures.
When a component starts to deteriorates, the resulting time signal often exhibits characteristics features, which can be used to detect a fault/failure. Also, component condition can rapidly progress from a very small defect to complete failure in a relatively short period of time; so early detection during manufacturing processes requires sensitivity to very small changes in the quality/reliability signatures. The knowledge of quality/reliability growth during manufacturing processes will help to obtain maximum achievable quality/reliability, with multivariate regression model. The results are used as the initial inputs for technological inheritance model, used to monitor and maintain components/equipment. With Technological Inheritance Model, it is possible to carry out quality factor and reliability measurements, for a cost effective component/system reliability monitoring and maintenance under a single platform.

DESIGNING RELIABILITY INTO COMPONENTS WITH QUALITY FACTOR

Design for reliability is an emerging discipline that refers to the process of designing reliability into components. The process encompasses several tools and practices and describes the order of their deployment that an organization needs to have in place in order to drive reliability into components and operations. Typically, the first step in reliability process is to set the system’s reliability requirements. Reliability must therefore be "designed in" to a particular system. During system design, top-level reliability requirements are then allocated to subsystems by design engineers working together with a specific plan and model.

A reliability plan and model that uses block diagrams to provide a graphical or mathematical model means of evaluating the relationships between different components of the system. The models incorporate prediction based on parts/components count resistance to failure and degradation taken from real time data. The design technique and component physics of failure relies on understanding the physical processes of stress, strength, and failure at a very detailed level as well as statistical methods. Then the component and system can then be redesigned to reduce the probability of failure. The reliability of components in this case is of course governed by various mechanisms through which they may fail. These mechanisms are therefore mainly dependent on the design, manufacturing processes and operational environment of devices. In order to reduce the incidence of failure, it is necessary to determine and study the modes of failure and feed the information back to design and production teams for the required corrective action. In addition to the feedback, it is important that information be supplied to the user concerning the overall reliability of the devices under normal operating conditions. It is however often necessary to represent such conditions with some other more convenient reliability test conditions (eg thermal stress) and it is consequently essential that these tests be related to operating conditions in a known manner.

These latter tests are the accelerated life tests carried out to evaluate device reliability with the help of quality factor analysis. The correlation between such tests (quality factor tests) and operating conditions may be determined by the wear out mechanisms for the devices, where the activation energies for these mechanisms are obtained from a series of steps like stress and over stress tests. The distribution plots from these type of tests are frequently deviated by random or consistent failures and may even be swamped by the dominant failure modes. In addition, new failure modes may be introduced by the stress of life, which means several failure modes can be considered and determined with the help of quality factor metric, through the use of Technological Inheritance Coefficient.

In this case, a combination of stress with other surface quality parameters like surface roughness, surface wavelength, surface hardness, surface concentration factor and others are integrated to form a single quality factor metric. Actually, the type of mechanism with quality factor will give straight lines on normal distribution plots. Therefore it is necessary and now possible that, the physics of failure mechanisms and their levels of activity be considered together during real time operations. It is common practice to employ screening procedures such as microscopic examination and "burn-in" programs, to enable immediate rejection of defective components or to accelerate gross failure potential in a short time. The use of component quality factor analysis with technological inheritance model is cost effective to combine stress tests, microscopic tests and physics of failure tests under a single platform for reliability design, as well as for life time, life cycle costs evaluation and assessment of components.



MULTIVARIATE REGRESSION OF COMPONENTS AND OPERATIONS

Multivariate linear regression analysis for components and operations is not widely used in practice, instead most researchers use multiple linear regression procedures with one dependent variable at a time. However, simulation studies by prominent researchers suggest that this type of practice is ill advised. If the primary goal of a study is prediction accuracy, they recommend the use of multivariate linear regression analysis. Multivariate procedures take into account the correlations among the component surface quality variables, which is ignored by univariate analysis. This is also the case for multivariate procedures used to select the best subset of variables when building multivariate models.

Multivariate linear regression procedures are used in practice to explain variation in a vector of dependent variables (component surface quality conditions) by employing a set of independent variables (operating conditions ) using observational data (e.g manufacturing data). In our settings, an integrated reliability of components and process operations with Technological Inheritance Model is applied to select a subset or all of the operating conditions that accounts for the variation of the component surface qualities and its control parameters. The control parameter for operating conditions, - "b" is known as technological inheritance coefficient for operating condition control, while "a" is Technological Inheritance Coefficient for surface quality control. These parameters are used to determine the initial set of predictor variables. In such cases, the goal is to discover the relationship between component surface quality conditions and the best subset of operating conditions (variables).

Multivariate linear regression procedure are also used with experimental data. In these situations, the regression coefficients are employed to evaluate the marginal or partial effect of surface finish conditions (component surface quality conditions and operating conditions) in the model. In both cases, one is concerned with estimation of the model parameters, model specifications and variable selection or in general mode calibration and sample model fit.

Regression models are also developed to predict some outcome variable such as job performances. In our situation, operating conditions are selected to maximize the predictive power of the linear model. Technological Inheritance Model uses the maximum outcome data of the multivariate regression to predict time of failure, find the root cause of failure and detect the failures at any point in time as well as determine component quality factor, component/system reliability and its lifetime.

RELIABILITY AND LIFE CYCLE SIMULATION OF SYSTEM COMPONENTS WITH TECHNOLOGICAL INHERITANCE MODEL

Technological Inheritance Parameters (coefficients) that best fit failure mode behaviors needs to be determined and made available, so that they can be used to simulate performance over extended periods. With the author’s mathematical model for the surface finish of hard coated cylindrical shaft, used in rotating equipment part or any other mathematical model of an equipment part, is possible to predict its future as well as determine it real-time operating data. Provided the part is treated, the same in the future as it was in the past. Technological Inheritance Model-based Simulation packages involve simulation engine that generates random effects in accordance with historic inheritance parameters over a specified system lifetime as well as from one operating data event to the other. It will mimic what will happen to the coated surface part in service, if its future were to remain the same as its past. Apart this, it is possible to transfer the surface quality characteristics from its initial to final operation with the help of technological inheritance model towards maximum achievable reliability. With this technique, the process of selecting maintenance and inspection intervals becomes a process of playing "what if with technological inheritance model based software used to compare the probabilistic effects of different reliability strategies". This of course will help to adjust the maintenance by bringing it to the most benefits for your business.
On this note, it will be worth while to carry out a quality and time line distribution before doing a technological inheritance model based failure/reliability analysis. The data collected at the different intervals and data events are used to calculate the reliability growth and degradation of components. Technological inheritance analysis is a means of identifying whether the failure mode was an early life failure, a randomly induced failure or due to wear-out and ageing. At the same time the reliability level at any point in time can be known. Technological inheritance parameters provides the owners, users and maintenance of equipment with a tool to know the failure history and real-time data of their operating plant and predict the behavior of components. The analysis is used to select effective equipment maintenance strategies and design out effects to reduce parts failure as well as maximize the reliability of component/system. It can be used for fitting equipment life data and used in the aviation industry to optimize maintenance intervention and select maintenance strategies.
Technological inheritance model can be used to mimic the behavior of a combination of other statistical distributions, which were each of limited use, by changing its shape. It represents all the zones of the bath tub and reliability curves by using technological inheritance coefficient for quality control, "a", and technological inheritance coefficient for process control "b", which must be optimal.
Where 0.1 < aflat < 0.9, implies random failures that are independent of time, where an old part is as good as a new part. Maintenance overhauls are not appropriate. Condition monitoring and inspection are strategies used to detect the onset of failure, and reduce the consequences of failure. This zone is affected by random incidents and accidents. It reflects poor operating procedures, poor risk management and poor materials selection at design.
At the zone, where 0 < afall < 0.1 implies early wear-out. You would not expect this type of failure within the design life. Failure mechanisms such as corrosion, erosion, low cycle fatigue and bearing failures fall in this range. Maintenance often involves a periodic rework or life extension task. The shape can be altered by better materials selection, production processes, growth/degradation management and good control of operating practices.
With zone at 0.9 < a-rise < 1 are wear-out or end of life failures. They should not appear in the design life. Age related failures includes stress corrosion, cracking, creep, high cycle fatigue and erosion. Appropriate maintenance is often renewal of the item with new ones.
A profile for an equipment is to have a negligible failure probability throughout its operating life followed by steep rise of "a" that predicts the replacement age is possible by integrating reliability, condition monitoring and maintenance strategy of manufacturing processes and operating equipment. Integrated reliability monitoring and maintenance strategy of component system is carried out with technological inheritance model-based software that is used to transfer of component positive desired quality from its initial to final operation of the manufacturing process and in turn into the equipment as well as remove its negative undesired quality towards maximum achievable reliability.

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DETERMINATION OF OVERALL EQUIPMENT EFFECTIVENESS WITH TECHNOLOGICAL INHERITANCE MODEL

Overall Equipment Effectiveness (OEE) is being used increasingly in industry because it takes the most common sources of manufacturing productivity losses to improve manufacturing and maintenance operations. OEE metric is always the first to be determined and after which is the need to develop a loss map for OEE main factors (uptime, speed and quality).
OEE is a hierarchy of metrics that can be used at the equipment, department, line and facility levels. Improving manufacturing and maintenance operations, OEE helps to uncover inefficiencies in production processes by showing how well a production line is functioning overall in terms of uptime, speed and quality. At this point, it is important to say that, technological inheritance model provides a single metric to integrate all the factors of OEE. Most companies have fairly good systems for capturing and tracking uptime and quality data, but determining design rate can be somewhat contentious. Even at best robust designs that meet desired requirements, still produce sub-optimal reliability results, hence the need to consider technological inheritance model. In order to develop a loss reliability map by taking the inverse of uptime, speed and quality percentage, reliability coefficient map can be developed real-time in terms uptime, speed and quality with technological inheritance coefficient ranging from "0 to 1", where 0 is the minimum reliability level and 1 is maximum reliability level. The next step will be to categorize the reliability growth and degradation distribution into distinct levels (minimum, medium and maximum). The reliability coefficient growth/degradation map is categorized into idle downtime, changeover downtime, early life, useful life and wear-out life.
Although, most companies do a fairly good job of capturing uptime and quality losses, but on the other hand, speed losses are seldom captured, which can vary often to produce major loss of productivity through gradual slow down whcih sometimes goes un notice. When a plant goes down, people notice it, but when it slows down, there isn,t that sense of urgency. However, the extent of lost production due to speed loss came as a surprise to management. More also reliability based on MTBF is not feasible for certain zero tolerance critical equipment, hence the need for automated control system that can capture/track speed, uptime and quality with a single metric during real-time operation in a systematic manner.
Technological Inheritance Model - based Software can now be used to capture and monitor reliability growth, degradation, OEE and other economic data, in order to uncover the hidden nuggets in repetitive small delays, production slow downs, component quality and other essential performance indexes. At the same time, it is used to build a better foundation for improving the business process of equipment maintenance, process control, data acquisition, monitoring, integration and selection of components.

QUALITY DATA IN RELIABILITY AND MAINTENANCE OF EQUIPMENT

Quality and readily available data is needed in order to cost-effectively carryout maintenance activities. This data helps us to take informed decisions that offer optimal results, which in turns affects the maintenance and manufacturing organizations in varying degrees. These organizations are being challenged by factors such as increasing competition, demands of share holders for greater returns on investment, societal expectations for efficient resource utilization with little or no environmental impacts and safety of employees. The evolution of the maintenance function can also be seen in various tools/systems like computerized maintenance management system (CMMS) used to make it more effective. Even at this, the maintenance industry is still plagued with the following problems:

-          Human data repository and employee turnover,

-          Poor utilization of the CMMS tools in review of previous maintenance activities as a way of understanding equipment failures and thereby in a position to fine-tune maintenance strategies that lead to increased equipment availability and reliability,

-           Poor basis of determining maintenance budgets in budgetary allocation,

-          Continuous appropriation of using CMMS Tool to demonstrate prudent maintenance practices with the opportunities to negotiate reductions in engineering biased and or fine insurance premiums.

The role of quality data in reliability and maintenance of equipment is great. Various equipment that are under the care of the maintenance function are themselves produced through the compilation of necessary data that were eventually used in equipment design and manufacturing. It has been ascertained by researchers and industrialists that such data collection in equipment design is less than optimal, which results in low inherent equipment reliability, poor availability and maintainability. While it is now standard practice to involve the maintenance function in the design of new plants and equipment in a bid to further assure maintainability.

Some of the main significant areas of quality data in the maintenance and reliability function are as follows:

1.      Effectiveness of Maintenance Strategies/Repairs – Maintenance strategies are normally put in place to ensure installed equipment is ever ready to render the services for which they are acquired for. Also to assure installed equipment in its operations will comply with all relevant regulatory and environmental requirements. It is inevitable that equipment in the course of usage will break down and necessary repairs need to be carried out to bring them back to service as quickly as possible. For effective coordination of maintenance activities, it is essential that all works and repairs that are carried out on installed equipment be captured appropriately.

Using the CMMS, the effectiveness can be reviewed and analyzed with a view of identifying areas in need of further optimization or improvement of maintenance practices. Frequent repairs can easily be spotted and subject to further improvement efforts.

It is therefore necessary to select the relevant maintenance data that define the work process, capture the data, and enter the data in the CMMS at the right time and in the right format, with the help of Technological Inheritance Technique.

2.      Budgetary Allocation – Activity Based Costing: Business organization exist for the sole reason of maximizing the returns of those who have put their earn income in the business. It is important for maintenance activities and the associated costs be accurately captured by maintenance department. At this point, it is necessary to mention that, maintenance data and its associate costs can be captured accurately, entered into CMMS and used for maintenance function with the help of Technological Inheritance Technique.

3.      Equipment Replacement Program – Maintenance and Engineering Organizations are constantly seeking equipment that delivers best service and value for their outfits. Therefore, it is relevant to tract or monitor data such as:

-          Spare consumption with associate costs,

-          Repairs and associate costs,

-          Down Time,

-          Mean To Between Failures (MTBF),

-          Mean Time To Repairs (MTTR),

-          Training Costs

The main reason for this program is to determine the equipment total cost of ownership. Such data should be handy in the economic analysis of replacing old equipment and a new replacement at a future data. The determination of the equipment total cost of ownership and its economic analysis is possible with Technological Inheritance Technique.

4.      Insurance Premiums – Evidence of good maintenance practices supported by good maintenance records/reports based on factual data or real-time data with technological inheritance technique can be used to demonstrate insurance risks managers how low the risk is and hence its attractiveness to rather conservative premium payout.

5.      Regulatory Compliance – Industrial organizations are increasingly being placed under the radar with respect to the environmental impact of their activities. Legislation in most countries require that certain installed equipment be subject to periodic inspection, recalibration and monitoring to assure their technical integrity, while in operation, hence the need for technological inheritance technique.

In conclusion, all the above significant areas of quality data in the maintenance and reliability function can be integrated with a single – “Technological Inheritance Coefficient”, which can be used to evaluate the overall equipment effectiveness

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REAL-TIME RELIABILITY MONITORING WITH SURFACE QUALITY CYCLING TECHNIQUE

Tools that can help identify real-time reliability risks earlier and throughout development as well as during the service life of components are becoming increasingly important. One such tool is a real-time wear and its competing failure cycle reliability/surface quality test in which the module is monitored with a technological inheritance coefficient-chain methodology during surface quality cycling. This test provides immediate real-time failure feedback, enhanced failure signature information, failure resistant mechanism and maximum achievable surface quality/reliability. Actually, today’s electronic module packages can integrate several active and passive components within one device, providing simpler board design, reduced component inventory, maintenance and reduced repair costs. Choices in the module material set, coating deposition conditions, component surface quality variables and substrate considerations all play substantial roles in determining the overall package reliability, its real-time monitoring and they ultimately determine which test methodology would be best to verify component reliability.

Over the past decade, the industry has moved from relying on pass/fail test methods toward using design-for-reliability tests that focus on uncovering and analyzing potential problems at an early stage. A facet of this is the increased emphasis on the test-to-failure approach for certain related tests, such as temperature, pressure, speed, surface hardness, surface roughness, surface wavelength, surface stress cycling and others.

Component Surface Quality and Process Factor Cycling

From the perspective of reliability test, component surface and process stress testing has traditionally been a “pass or fail” test where the stress is induced and functional units are subsequently removed from such stress for verification of post electrical-test functionality. In general, a component surface and process stress profile involves the following parameters:

-          High extreme component surface quality condition, (Ymax) and Process condition (Xmax);

-          Low extreme component surface quality condition, (Ymin) and Process condition (Xmin);

-          Component surface quality condition change (Y), where  Process condition (X), where,

-          Ramp rates;

-          Dwell times at extreme component and process conditions,

-          Technological Inheritance Coefficients “a – for component surface quality control” and “b – for process condition control”

The lifetime and reliability of a component module can be significantly affected by these parameters tests can be used to characterize product capability and accelerate failure modes (eg die crack, via crack) during initial technology development, product design verification and product qualification.

The Monitoring of Component Surface Quality and Process Factor Cycling

As an enhancement to existing component and process stress methodologies, implementing monitored daisy-chain parameters in real time during component and process condition stresses can help determine if any intermittent failures are beginning to occur. As a result, designers can be alerted to a failure immediately without the need for post electrical test. A monitoring test includes:

-          Event Detection: In order to capture a significant component quality and process condition change in real-time, the event-detection capability called for in monitoring failure testing. The failure mechanism associated with condition stresses tend to be very slow to develop and will not change quickly from passing to failing and back to passing. A switch matrix that can be used for all of the modules under test on the scale of one to maximum (success/pass) and 0 to minimum (failure/fail).

-          TC Profile: Any type of component/process condition cycle profile can be used in conjunction with monitored condition testing.

-          Reliability Growth/Degradation: The component surface quality and reliability grows to maximum as measured with technological inheritance coefficient form point to point (from 0 to 1), degradation moves from 1 to 0.

Monitored Component Surface Quality and Process Factor Data Analyzed

Once reliability growth and degradation is captured, raw data (channel, resistance, count, time and so forth) can be exported, analyzed and graphed within a software package such as LABVIEW, SAS and others. By capturing failure resistance or reliability data throughout the component life cycle, it is possible to detect failures that first develop at extreme conditions of component/process. In contrast, reliability growth/degradation cycles may need to be measured to detect failures and eliminate it with the help of technological inheritance technique and cost effective maintenance procedures. Reliability monitoring and maintenance with technological inheritance technique is useful during development phases when designers must establish confidence in new packaging technologies and throughout its life-time cycle. The ability to detect a failure at the precise cycle count at which it begins to occur is an advantage for earlier detection as well as for accurate statistical analysis. Detailed data regarding the signature of failure and progression of the failure can be valuable in assisting failure-mode determination and reducing failure-analysis time as well as for the attainment of maximum reliability. Technological Inheritance Coefficients can be used to determine failure, time to failure, life-cycle cost, reliability growth and degradation as well as for the selection of cost effective components and processes.

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WEAR AND ITS COMPETING FAILURE MODE FACTORS OF COMPONENTS

The analysis of all the desired contributing factors is necessary to predict the result of a single wear and its competing failure mechanism, but several sources of deterioration may combine to complicate what might otherwise be a straightforward solution. The main multiple factors that influence wear and its competing failure modes are as follows:

-          Metallurgical variable (Hardness, toughness, constitution and microstructure, chemical composition)

-          Service Variables (Contacting materials, pressure, speed, temperature, surface finish)

-          Other factors (Lubrication, corrosion, vibration)

It is actually impossible to generalize about a material’s suitability, such as to say a certain composition is the most wear and its competing failure resistant, without considering factors such as service conditions. For example, two materials, one of which is clearly superior to the other for a specific application, may undergo a complete reversal in wear resistance from only slight changes in service conditions. Although strength, hardness and ductility or toughness are important, wear resistance cannot always be associated with any one property. Wear resistance generally does not increase directly with tensile strength or hardness; however, if other factors are relatively constant, hardness values provide an approximate guide to relative wear behavior among different metal. This is especially true for metal – to – metal sliding and abrasive applications.

In the cases, where other factors may vary, it will be necessary to consider other factors such as service, environmental and manufacturing conditions that combines to produce maximum achievable wear and competing failure resistance (like corrosion, temperature, fatigue, vibration and temperature resistance).  As long as these conditions continue to vary, wear resistance and its competing failure resistance is also bound to vary. It will therefore be necessary to optimize all the operating conditions of the technological system in order to make the wear and its competing failure resistant values constant for a specific period of time. The technological system condition of this case includes the followings:

-          Metallurgical Conditions

-          Surface Coating Deposition Conditions

-          Surface Finish Conditions

-          Service Conditions

-          Monitoring Conditions

-          Maintenance Conditions

Multiple regression models is used to relate the technological operating conditions with component surface quality conditions, which helps to select the significant surface quality parameters, manufacturing processes and to determine maximum achievable reliability and life-time. The highest surface quality factor value by any of the technological process is selected for the optimization of the system.

The selected optimization process will ultimately serve as the optimum baseline for all the operating conditions of the technological system. The optimization of a component surface finish condition provides the optimum surface quality, optimum process condition, selects the desired number of parameters and factors as well as determines their optimum values. The multiple surface quality parameters of hard-alloy coating are selected as surface hardness, surface roughness, surface wavelength, surface stress and kinematic coefficient for maximum achievable reliability. The optimum multiple quality values can be integrated together to form a single quality and reliability factor, which can be used to construct wear failure/wear resistant mechanism and other  competing failure mechanism or its failure resistance mechanism. Technological inheritance technique is used to plot the mechanism diagram, predict the time of failure, diagnose failures, and determine maximum achievable reliability, reliability growth/degradation, life-time and life cycle cost.

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SELF-FLUXING ALLOY POWDERS FOR EXTREME CONDITIONS AND APPLICATIONS

Self-fluxing powders based on nickel are hard alloy materials having a wide application for hardening and reconditioning surfaces of rapid-wearing parts working in aggressive media and wear conditions. This hard alloy powder is similar in chemical composition to those of the granulated spheroidized alloy powders. The self-fluxing powder is slightly different with a lower content of oxygen and other impurities, although classified according to composition. These coatings have complex structures, which consist of minutely dispersed super hard phases of carbides, borides and carboborides with micro-hardness up to 40.10³ MPa, uniformly distributed in a hard metallic matrix based on solid solutions and complex nickel-based eutectic.

These hard coatings are chemically resistant to different active media. On the basis of research works, it was ascertained that, within the range of specific pressure 1.5 x 10⁶ – 7 x 10⁶ Pa and sliding velocity up to 8m/s, it is possible to recommend nickel-based self-fluxing powder materials for friction pairs in water and in solutions of NaOH and CH₃COOH of 10 – 50% concentration. The wear resistance of this type of hard coating in frictional contact with steel (friction of boundary lubrication) is 4 – 9 times higher than the wear resistance of steel in steel – steel pairs and steel p iron pairs under specific loads of 1.5 x 10⁶ – 10 x 10⁶ Pa and sliding velocities of 8 – 54 m/min; within this range, the amount of wear of steels working in pairs with hard coatings is reduced by a factor of 1.5 – 2.5 times. The friction coefficient in pairs of hard coating – steel with initial surface roughness of Ra = 0.16 – 0.3  is 1.8 – 2.2 times lower than in pairs of steel – steel, but with constant surface roughness, the amount of wear is 2 – 10 times reduced.

These hard coatings are high wear and corrosion resistant, capable of withstanding impact and hard conditions under abrasive wear at temperatures up to 600^oC. They are cost effectively used for reconditioning and hardening valves, camshafts, blades, fans, rotors, shafts and sleeves of hydraulic pumps, parts of agricultural machines etc. At this juncture, it is worth knowing that, there are several contributing factors that have combined to determine the results of high wear, corrosion and temperature resistance of these hard alloy coatings. The knowledge of these multiple factors will definitely help to predict the determinant result of a single failure resistance mechanism (wear and its competing failure resistance mechanism) with the help of technological inheritance model that integrates the factors. This single failure resistance mechanism of a particular component can be used to determine maximum achievable wear, corrosion and temperature resistance for extreme industrial conditions.  

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The Reliability of Parts and Equipment

NICKEL-BASE ALLOYS CHARACTERISTICS AND APPLICATIONS IN THE INDUSTRIES

The term “Nickel-Base Alloys” is used for those alloys wherein, with very few exceptions, nickel is the principal element. These nickel alloys are used where corrosion resistance is of the greatest importance, and metal is exposed to cyclical heating conditions and electrolytic attack. Most commercially available nickel-base hard facing alloys can be divided into three groups: boride containing alloys, carbide containing alloys and laves phase containing alloys. The largest numbers of nickel alloys are those containing boron. These alloys were first commercially produced as sprayed and fused powders. Currently they are marketed by most manufacturers as other hard facing products as well. This group of alloys is primarily composed of Ni-Cr-B-Si-C, the boron content typically ranging from 1.5% to 3.5% depending on chromium content which varies from 0 to 15%. High chromium containing alloys generally contain a large amount of boron which forms very hard chromium borides with a hardness of 1800kg/mm². Usually other borides high in nickel with a low melting point are also present to facilitate the fusing operation subsequent to spraying. The abrasion resistance of those alloys is a function of the amount of hard borides present. Alloys containing Ni – 14 Cr – 3.4 B – 4.0 Si are extremely abrasion resistant but very poor in resisting impact. Since most of these alloys do not contain high amounts of solid solution strengtheners, considerable loss of hardness occur at elevated temperatures.

The laves phase alloys contain an inter-metallic compound of nickel – molybdenum – silicon in a nickel chromium – molybdenum matrix. The cobalt laves phase alloys are similar in that the cobalt replaces the nickel in the system. Due to the fact that laves are inter-metallic compounds, they are believed to be less abrasive to mating materials than carbide or boride containing alloys. These alloys like Nickel 200. Monel alloy 400, Inconel alloy 800 etc are most likely to found in Agriculture, the Chemical, Petroleum, Space and Aircraft industries. Specifically for Agriculture, certain powder materials of nickel-base alloy composition have been produced by Castoline Company in Switzerland, for hardening new parts and reconditioning worn parts of machine by gas-thermal spraying. The company produces granulated powders (GP), which have been classified into three groups, base on their deposition techniques and service applications.

At this point it will be worth while to consider some of the characteristics of these powder alloy materials.

GP – 10 Ni – 01 is a spheroidized powder alloy based on the system Ni – Cr – B – Si – C – Fe. It is usually deposited on mild stainless steels and irons. This alloy has a low melting point and high yield point, which provides smooth surfaces after deposition. The coated layer is characterized by a very low friction coefficient and high resistance to wear, corrosion and oxidation at normal and high temperatures. Hardness of the coated layer is HRC 55 – 62 and it offers a wear resistance performance, even at a high temperature of about 700^oC. It’s available in coating thickness up to 2mm, and can usually be finished by grinding. This alloy material is commonly used for hardening and reconditioning of tools, crank pins, eccentrics, ratchet and pawl gear, transporting chains, mixers, pushers and various spots.

PM – 12 NiWC – 01 is a composite powder material (PM) consisting of a mechanical mixture of the spheroidized powder GP – 10Ni – 01 of the system Ni – Cr – B Si – C – Fe and the very small particles of tungsten carbide WC (35%). It is deposited on mild and stainless steel and irons. It has a high resistance to wear at normal and high temperatures. Coating hardness is HRC 57 – 64, capable of operating under a high temperature up to 700^oC. The coating thickness is usually up to 2mm while the surface finish is done by grinding. It is used for hardening and reconditioning of transporting chains, mixer blades and scrapers, fan blades, cutting tools, cutting auger conveyors etc.

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SELECTION OF HARD COATINGS FOR METAL-TO-METAL SLIDING AND ABRASIVE APPLICATIONS

The selection of the proper metal to be used for a job is the most important step in doing a job successfully. Each metal has different characteristics of strength, shrinkage, hardness etc so that there is a metal for almost every purpose. When a metallic wire is sprayed, it forms an alloy different from the original one, which is why when choosing a metal it is necessary to always consider the properties of metal deposition and not just the properties of the original metallic wires. All sprayed metal is porous and brittle as compared with the wire from which it was sprayed as a result of “spraying”. When metal is sprayed it tends to shrink slightly and hence set up internal stresses. The real strength of any sprayed coating is the difference between its original inherent strength and these internal stresses. Since the stresses are produced by shrinkage, those metals which shrink least will have the least internal stresses. Sprayed metals which have high tensile strengths and low shrinkage factors will obviously give the strongest coatings which are least likely to crack or lift. These points are very important, and explain why wires for spraying should be chosen on the basis of the properties of the metallic coatings and not on the basis of imitation and consideration of the parent material.

In general, the maintenance welder will be required to weld according to absolute specification, in which case the filler metal and welding procedures are spelled for him. In the absence of a specification, if the welder can identify the parent metal, he may match the filler metal to the parent metal as closely as possible. If he cannot identify the metal, other than that it is a high nickel alloy (for example), the best practice is to stay with the higher nickel content and choose the other alloying elements according to his knowledge of what those elements might contribute to the weld deposit under the welding in-service environment. When depositing comparatively thin coating on known parent metal, any metal can successfully be sprayed, since the metal is chosen with respect to its hardness and wear resistance characteristics or machinability or its corrosion resistance properties. This type of coating is usually carried out on the majority of parts for thin coatings (up to 1.5mm).

Surface roughening is generally necessary for coatings over 1.5mm thickness wherever there is an edge, such as at a key way. In this case, high shrinkage metals, such as SPRASTEEL 10 or METCOLOY 1 should be used. They should also be used for coatings over 3.0mm thickness wherever there are edges and for coatings that do not have edges (such as continuous coatings on cylindrical surfaces, where the conditions of the service are extremely severe, where there is the danger of layer separations in the coating or where the coatings are heavy and high shrink metals so that danger of cracking exists). In the cases where coating metals are chosen for wear resistance and hardness, steels are not machinable on lathes, and where grinding equipment is available, it is recommended to use METCOLOY 2, SPRASTEEL 80, Nickel-Base Alloys and Self-Fluxing Alloys for heavy coatings with the highest shrinkage.

At this juncture, it is worthy to note that, grinding heavy coatings of hard alloy materials do produce tensile stresses, which will definitely reduce the hardness and wear resistance of components. In order to obtain maximum hardness and wear resistance of components at minimum cost, there is therefore the need to apply technological inheritance technique.

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