Reliability assessment for product with Wiener process degradation based on marker data

This paper describes reliability assessment from measured data of double-stress accelerated degradation test which taking functional unit as research object. Most of performance degradation reliability analyses pay attention to components. Thereby assessments are relatively easy because they have simple function and clear failure mechanism. However, in reality, functional units compose system, and the reliability of a functional unit is affected by all of the jointing components synthetically which puts up a unitary degradation process. On this consideration, double-stress uniformly orthogonal accelerated test is putting on the whole board of functional units under temperature and relative humidity stresses (80 °C,55%), (100 °C,25%), (90 °C,85%), (110 °C,65%). Accelerated test design is introduced in this paper and the collected measured degradation data are shown by figures. According to statistical analysis reliability information is inferred from test data and assessment result is compared to results educed by other methods. Conclusion is result gain by this present way and is basically consistent with other studies. Functional unit reliability assessment based on performance degradation data is feasible and reliable.

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.

Reliability assessment has been widely used and studied as one of the important roles in equipment operation and maintenance. Multivariate competing degradation models have been extensively studied, but some non-competing multivariate degradation processes still exist in reality. In this paper, we present a method of reliability assessment based on Wiener process which considering non-competing relationship. The multivariate degradation process studied in this paper consists of two degradation indicators. The Wiener process was applied to model two degradation indicators separately. Bayesian method is used for parameter estimation of unknown parameters of degradation models and quantification of uncertainty. A case study of spool valve is used for the demonstration and validation of the model presented in this paper.

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.

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.

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 this paper, a novel approach is proposed to determine target reliability index based on modeling performance degradation of concrete structures as a stochastic process. First, the Wiener process with drift is used to simulate the degradation process of relative dynamic elastic modulus of concrete, and the drift coefficient and diffusion coefficient of Wiener process are estimated according to the experimental data of degradation of relative dynamic elastic modulus. Then, based on the relations between concrete compressive strength, dynamic elastic modulus, and relative dynamic elastic modulus, the relationship between compressive strength and the amount of degradation of relative dynamic elastic modulus are deduced, and then, the statistical parameters of concrete compressive strength under different service time can be calculated. Finally, based on the established limit state equation of flexural capacity of the weak section and the statistical parameters of compressive strength at different times, the reliability index of a structure under different service life is calculated. On the other hand, according to the drift Wiener process model of relative dynamic elastic modulus degradation of concrete, the time‐varying failure probability of structural durability degradation is calculated based on the first‐passage failure probability formula of drift Wiener process. Taking a concrete plant structure as an example, the time‐varying reliability index calculated based on the limit state equation and the time‐varying failure probability obtained based on the first‐passage failure probability analysis of drift Wiener process are compared, and then the recommended values of target reliability index of structural bearing capacity are comprehensively determined.

The assessment of rolling bearing reliability is vital for ensuring mechanical operational safety and minimizing maintenance costs. Due to the difficulty in obtaining data on the performance degradation and failure time of rolling bearings, traditional methods for reliability assessment are challenged. This paper introduces a novel hybrid method for the reliability assessment of rolling bearings, combining the convolutional neural network (CNN)‐convolutional block attention module (CBAM)‐ bidirectional long short‐term memory (BiLSTM) network with the Wiener process. The approach comprises three distinct stages: Initially, it involves acquiring two‐dimensional time‐frequency representations of bearings at various operational phases using Continuous Wavelet Transform. Subsequently, the CNN‐CBAM‐BiLSTM network is employed to establish health index (HI) for the bearings and to facilitate the extraction of deep features, serving as input for the Wiener process. The final stage applies the Wiener process to evaluate the bearings’ reliability, characterizing the HI and quantifying uncertainties. The experiment is performed on bearing degradation data and the results indicate the effectiveness and superiority of the proposed hybrid method.

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.

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.

Accurately assessing reliability and predicting the life of products is an important part of Prediction and Prognostics and Health Management. According to historical degradation data of products, the degradation model of two-stage nonlinear phase-dependent Wiener process is proposed to evaluate the dependability and forecast the lifetime of individual products. Firstly, a two-stage phase-dependent degradation model based on a nonlinear Wiener process was developed to characterize nonlinear nature of the degradation of product performance by simultaneously taking the correlation of degradation stages and the degradation heterogeneity of products into account; Secondly, the model parameters are estimated using EM algorithm based on historical degradation data of products; Finally, simulation studies are used to demonstrate the superiority of the approach for assessing offline reliability, and the validity of the method proposed in this paper is verified through an example of high-voltage pulse capacitors. The results show that compared with the degradation model based on phase-independent nonlinear Wiener processes, the two-stage nonlinear phase-dependent Wiener process degradation model can better capture actual degradation paths of products and provide a more reasonable assessment of lifetime reliability of products.

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.

Remaining useful life (RUL) prediction is a core technology for equipment operation and health management, and the degradation process of most equipment generally has a long-term correlation. To address this issue, we propose a method to quantify the uncertainty of RUL prediction based on a temporal convolutional network (TCN)-transformer- Wiener Process (WP) degradation model. We give a new generalized RUL prediction framework that employs TCN-Transformer to fit device degradation trends, which can adaptively adjust the fitting of degradation trends. WP is introduced, and the concept of first hitting time (FHT) gives an approximate expression for the probability density function (PDF) of the remaining lifetime of the model, further quantifying the uncertainty of the RUL prediction results. Finally, verification and analysis on the bearing dataset and the tool holder power head degradation dataset, and by comparing with other existing popular methods demonstrated the accuracy and stability of the proposed RUL prediction method. This innovative method provides a robust solution for RUL prediction, offering significant contributions to improving maintenance strategies and operational efficiency.

A generalized system reliability model based on survival signature and multiple competing failure processes

Existing strategies for forecasting the remaining lifespan of deteriorating equipment are not suitable for situations where the measurement intervals for the deteriorating equipment are unevenly distributed, and the monitoring data's measurement frequency does not align with historical data. Moreover, they have not taken into account the fluctuation of adaptive drift parameters during the period of decline. In light of this, a nonlinear prediction model for the remaining lifespan of deteriorating equipment that incorporates historical observational data, based on adaptive drift parameters, is proposed. Initially, the degradation process is described using a real-time state-space model with drift parameters adjusted using historical data to establish a nonlinear degradation model following the Wiener process. Subsequently, an analytical expression for the remaining life in terms of first-passage time is derived, and the parameters of the model are determined through the expectation maximization algorithm. Finally, the proposed method's efficacy and applicability are confirmed using degradation data from a reset spring under impact.