Reliability assessment of functional unit based on accelerated degradation data

Reliability assessment method based on performance degradation data has been proven to be useful when dealing with products characterized as long-life and highly reliable. However, this method can hardly be applied when measurement of performance degradation is technically difficult or costly. To tackle this problem, this paper proposes to utilize the measurement data which are related to the performance degradation, namely, marker data. It is assumed that the degradation process is modeled with Wiener process, and that the marker is the integral of the performance degradation. We first estimate the parameters of the Wiener process using marker data and then conduct the reliability assessment. A simulated example is presented to demonstrate the proposed method.

The characteristics of performance degradation data are analyzed. The disciplinarians of numerical control machine tool performance degradation data with time are analyzed, the reliability assessment methods with performance degradation data based on performance degradation distribution is presented. The performance gradation data contain abundant numerical control machine tool reliability information, the reliability and lifetime of numerical control machine tool can be evaluated with performance degradation data availably, even if the failure time cannot be obtained by life testing.

Traditional life evaluation theory established on the basis of mass failure data, the phenomena of little or naught failure put forward challenges for existing life evaluation theory. The performance degradation data provides useful information for products' life and reliability, and gives feasible way for life evaluation. The limitations of existing stochastic process models for performance degradation are analyzed; a new approach based on performance degradation amplitude is brought forward, the calculation methods based on degradation amplitude analysis are given out. For degradation product with large amplitude size, by way of fitting the reliability of every moments, the performance degradation rule can be got and the products' life can be forecast; for small amplitude size, the degradation path is selected in accordance to engineering experience and products' failure mechanism, the products' expected life and reliability is evaluated based on appropriate distribution model. An example of laser device with performance degradation data is given out to validate the effectiveness of the method.

Space electronic products usually should meet the mission requirements of long life and low cost. Because of insufficient failure data, it is hard to assess the reliability precisely by using traditional reliability testing and statistical method. In this paper, a method of reliability assessment based on integration of highly accelerated life testing (HALT) and accelerated degradation testing (ADT) is proposed. Firstly, based on performance analysis of electronic products, the degradation of performance is modeled. Moreover, the data of drift performance is extracted, fitted and fused into the observation data in ADT. Lastly, the reliability assessment for electronic products is studied based on degradation data. This research provides an effective way for accelerated reliability testing of space electronic products, and it has been applied into practice.

As the rapid development of modern technology, industrial companies have to manufacture high-reliability and long-lifetime products to satisfy the changing needs of the customers. In such cases, it is difficult to assess the reliability and lifetime of the product with traditional life testing. Thus how to evaluate the lifetime and reliability index of these products is an urgent problem to be solved. For some products, it is possible to obtain the performance degradation information by the accelerated degradation testing (ADT). Therefore, to utilize the product degradation information may be an effective way to resolve this issue. However, most of lifetime prediction methods in use are mainly based on single prediction model with the disadvantage of low robustness and inaccuracy, so it cannot get the high credible lifetime of the product. This paper will adopt the prediction thought of combining two single models to establish a new prediction model, also propose a combined life prediction method based on the performance degradation data by using the particle swarm optimization (PSO) with immunity algorithms (IA). The author selects two better prediction models, which are time series model and the model of Brownian motion with drift, to predict the degradation path of the product separately. As the degradation process of the product is stochastic, and these two models are also well suited for this process. So each of them is good for long-term prediction. Then the objective function of the sum of the absolute errors is established with weight coefficients which are used to combine these prediction methods and integrate the prediction results. And the objective function is minimized with the IA-PSO, which has the characteristics of fast global optimization and uses to determine the weight coefficients for each of the prediction models. This approach can reduce the blindness of calculation and improve the accuracy of the prediction model. To verify this combined prediction method, the application of SLD's performance degradation data is demonstrated in this paper, the result shows that the combined prediction method based on the IA-PSO is effective and reasonable, and evidently has less prediction errors than each of single model. Therefore, this method plays an instructive way in engineering practice and research field.

In order to solve the problem of predicting the residual life of mechanical products accurately based on small-sample data, this paper proposes a small-sample adaptive residual life prediction model of mechanical products based on feature matching preprocessor-LSTM. First, aiming at the problem of low accuracy of remaining life prediction for small samples of mechanical products caused by multiple time scales and multiple fault states, the failure time data and performance degradation data are fused, and the failure rate and standard deviation are used as the remaining life prediction criteria to intuitively reflect The possibility of failure of a component or system at a certain point in time. Considering the demand of adaptive small-sample residual life prediction data, this paper establishes the adaptive matching pre-processor model of life characteristics. On this basis, the LSTM neural network is used to establish a small-sample adaptive residual life prediction model. Then, the XJTU-SY bearing life data set and the test data of the small-sample life characteristics measured by the RV reducer are used as the research objects, and a small amount of the data set is randomly selected. The remaining life expectancy is predicted from the sample data and compared with its standard remaining life, respectively. The comparison results show that the overall prediction error is small. This study shows that the remaining life prediction model established can better predict the remaining life of mechanical product sub-sample data and provides a feasible method for predicting the remaining life of mechanical product sub-samples.

As few or no failures occur during life test and accelerated life test for long-lifetime products, FR-4 printed circuit boards (PCBs) are employed as research objects to conduct performance degradation test under high temperature and humidity. Based on the analysis of the failure mode, failure mechanism and the study on the degradation parameters of PCB, the degradation failure model of PCB is built which is related to bias voltage in high temperature and humidity. Based on the statistical analysis method on performance degradation data, a reliability evaluation method of degradation paths is developed for reliability inference of PCB. Finally, the statistical result about pseudo-failure lifetime data under Weibull distribution is given, which confirms its validity and effectiveness.

Traditional reliability assessment methods are generally on the basis of failure lifetime data. The failure lifetime data of high reliability and long lifetime product are obtained difficultly by life test and accelerated life test. Degradation data can provide useful information about the reliability assessment for these products. Aiming at these problems, a method of reliability prediction is presented based on failure data's reliability assessment, accelerated performance degradation and information fusion theory. Finally, this paper gives a case of accelerated performance degradation testing of 24V/2A power supply board to steady voltage to apply the method, whose results demonstrate that this method is correct and valid for reliability assessment of high reliability and long life electronic equipments.

Traditionally, reliability assessment of devices has been based on (accelerated) life tests. However, for highly reliable products, little information about reliability is provided by life tests in which few or no failures are typically observed. Since most failures arise from a degradation mechanism at work for which there are characteristics that degrade over time, one alternative is monitor the device for a period of time and assess its reliability from the changes in performance (degradation) observed during that period. The goal of this article is to illustrate how degradation data can be modeled and analyzed by using "classical" and Bayesian approaches. Four methods of data analysis based on classical inference are presented. Next we show how Bayesian methods can also be used to provide a natural approach to analyzing degradation data. The approaches are applied to a real data set regarding train wheels degradation.

The environmental factor (such as temperature, etc.) is an important error sources of product performance degradation data. In practice, the raw degradation data usually contain some noise terms caused by the fluctuation of environmental factor. Therefore, it is necessary to extract the real degradation trend before degradation modeling and life prediction. In this research, the Grey relational analysis method is utilized to quantitatively describe the correlational relationship between temperature fluctuation and raw degradation data. Then a novel temperature compensation model based on least squares support vector machine (LS-SVM) is proposed, which can be used to compensate the influence of environmental fluctuation on degradation data. An engineering case study on the degradation data of a super-luminescent diode (SLD) is employed to verify the effectiveness of the proposed method.

In the area of prognostics and health management, data-driven methods increasingly show the superiority against model-based method due to the complex relationships and learn trends available in the data captured without the need for specific failure models. This paper uses Empirical Mode Decomposition (EMD) and Support Vector Machine (SVM) to build a model for non-stationary time series prediction. And it proves that the EMD-SVR method can solve the problem of few training samples in modeling the path of performance degradation. Then when the threshold is given, we can forecast the lifetime of engineering systems based on the performance degradation data.

A 20kHz signal board of radar is the research object and the circuit board reliability analysis is made by using performance degradation reliability theory. The rule of radar circuit board performance degradation in the electric impact environment was concluded with the testing data. Aiming at the insufficient of the multiple performance degradation parameters fault prediction method existing, a new method based on resemblance coefficient is present. The new method combine the signal processing technology with the performance degradation theory. The resemblance coefficient fused the multiple performance parameters degradation characteristics, so the multi-feature extraction difficulties of multiple performance parameters has been solved. Then, the Gamma process is used to describe resemblance coefficient degradation for modeling, extrapolated life information of the product, and finally, validity of this method was proved by the testing data.

Existing joint maintenance decision research typically ignores remaining useful lifetime (RUL) predictions for the accelerated degradation of equipment. A joint maintenance decision method for the replacement and spare-parts ordering strategy based on RUL prediction using accelerated degradation data for equipment is proposed in this paper. First, an RUL prediction model under accelerated stress is built by considering the proportional relationship between the drift coefficient and diffusion coefficient in the Wiener process. Second, based on the principle of step-by-step estimation, accelerated degradation test (ADT) data of the equipment are used to estimate the a priori unknown parameters. Finally, based on the RUL prediction results, a joint optimization model for the replacement and spare-parts ordering strategy is developed. Through example verification and cost parameter sensitivity analysis, the proposed method is shown to effectively improve the accuracy of RUL prediction and the scientific value of the joint optimization plan for equipment replacement and spare-part ordering, which is important to many engineering applications.

Metallized film capacitor is a type of product with a long lifetime and high reliability. It is difficult to assess the lifetime and reliability using the traditional statistical inference method which is based on the large number of testing data. This paper presents a new testing methodology, called T‐performance degradation test, by dividing the test process into several stages. In each stage, the sample size of working capacitors under test decreases stage by stage until the test lasts enough time with few survival capacitors. Leveraging the T‐performance degradation data, this paper further presents a reliability assessment model to predict the lifetime of the high‐performance capacitors. Finally, the reliability assessment model is demonstrated on a type of high‐performance metallized film capacitors used in the energy module of the laser facility. Copyright © 2012 John Wiley & Sons, Ltd.

Accelerated degradation testing (ADT) is an efficient tool to conduct material service reliability and safety evaluations by analyzing performance degradation data. Traditional stochastic process models are mainly for linear or linearization degradation paths. However, those methods are not applicable for the situations where the degradation processes cannot be linearized. Hence, in this paper, a general ADT model based on the Wiener process is proposed to solve the problem for accelerated degradation data analysis. The general model can consider the unit-to-unit variation and temporal variation of the degradation process, and is suitable for both linear and nonlinear ADT analyses with single or multiple acceleration variables. The statistical inference is given to estimate the unknown parameters in both constant stress and step stress ADT. The simulation example and two real applications demonstrate that the proposed method can yield reliable lifetime evaluation results compared with the existing linear and time-scale transformation Wiener processes in both linear and nonlinear ADT analyses.