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.

http://www.integrated-consultancy.com/