Failure Inference and Optimization for Step Stress Model Based on Bivariate Wiener Model

In this paper, we consider the situation under a life test, in which the failure time of the test units are not related deterministically to an observable stochastic time varying covariate. In such a case, the joint distribution of failure time and a marker value would be useful for modeling the step stress life test. The problem of accelerating such an experiment is considered as the main aim of this paper. We present a step stress accelerated model based on a bivariate Wiener process with one component as the latent (unobservable) degradation process, which determines the failure times and the other as a marker process, the degradation values of which are recorded at times of failure. Parametric inference based on the proposed model is discussed and the optimization procedure for obtaining the optimal time for changing the stress level is presented. The optimization criterion is to minimize the approximate variance of the maximum likelihood estimator of a percentile of the products' lifetime distribution

Integrated Degradation Models in R Using iDEMO

Degradation models are widely used to assess the lifetime information for highly reliable products with quality characteristics whose degradation over time can be related to reliability. The performance of a degradation model largely depends on an appropriate model description of the product's degradation path. The cross-platform package iDEMO (integrated degradation models) is developed in R and the interface is built using the Tcl/Tk bindings provided by the tcltk and tcltk2 packages included with R. It is a tool to build a linear degradation model which can simultaneously consider the unit-to-unit variation, time-dependent structure and measurement error in the degradation paths. The package iDEMO provides the maximum likelihood estimates of the unknown parameters, mean-time-to-failure and q-th quantile, and their corresponding confidence intervals based on the different information matrices. In addition, degradation model selection and goodness-of-fit tests are provided to determine and diagnose the degradation model for the user's current data by the commonly used criteria. By only enabling user interface elements when necessary, input errors are minimized

Statistical Process Control for Monitoring a Diffusion Process

[[abstract]]This study presents a new statistical process control (SPC) procedure for a process together with degradation and diffusion effects. One of such examples is the initial cool-down process of high-pressure hose production. The air temperature readings during the initial cool-down process often exhibit a non-increasing trend with a diffusion effect in that profiles generated from cycle to cycle deviates from each other more over time. A new charting procedure using the Wiener diffusion model is developed in this article. A real data set, generated from the cool-down process of high-pressure hose production, is used to demonstrate the application of proposed method.[[journaltype]]國外[[incitationindex]]EI[[ispeerreviewed]]Y[[booktype]]紙本[[countrycodes]]JP

Accelerated degradation modeling considering long-range dependence and unit-to-unit variability

Accelerated degradation testing (ADT) is an effective way to evaluate the reliability and lifetime of highly reliable products. Existing studies have shown that the degradation processes of some products are non-Markovian with long-range dependence due to the interaction with environments. Besides, the degradation processes of products from the same population generally vary from each other due to various uncertainties. These two aspects bring great difficulty for ADT modeling. In this paper, we propose an improved ADT model considering both long-range dependence and unit-to-unit variability. To be specific, fractional Brownian motion (FBM) is utilized to capture the long-range dependence in the degradation process. The unit-to-unit variability among multiple products is captured by a random variable in the degradation rate function. To ensure the accuracy of the parameter estimations, a novel statistical inference method based on expectation maximization (EM) algorithm is proposed, in which the maximization of the overall likelihood function is achieved. The effectiveness of the proposed method is fully verified by a simulation case and a microwave case. The results show that the proposed model is more suitable for ADT modeling and analysis than existing ADT models

Reliability modeling and analysis of load-sharing systems with continuously degrading components

This paper presents a reliability modeling and analysis framework for load-sharing systems with identical components subject to continuous degradation. It is assumed that the components in the system suffer from degradation through an additive impact under increased workload caused by consecutive failures. A log-linear link function is used to describe the relationship between the degradation rate and load stress levels. By assuming that the component degradation is well modeled by a step-wise drifted Wiener process, we construct maximum likelihood estimates (MLEs) for unknown parameters and related reliability characteristics by combining analytical and numerical methods. Approximate initial guesses are proposed to lessen the computational burden in numerical estimation. The estimated distribution of MLE is given in the form of multivariate normal distribution with the aid of Fisher information. Alternative confidence intervals are provided by bootstrapping methods. A simulation study with various sample sizes and inspection intervals is presented to analyze the estimation accuracy. Finally, the proposed approach is illustrated by track degradation data from an application example

Modeling multivariate degradation processes with time‐variant covariates and imperfect maintenance effects

International audienceThis article proposes two types of degradation models that are suitable for describing multivariate degrading systems subject to time‐variant covariates and imperfect maintenance activities. A multivariate Wiener process is constructed as a baseline model, on top of which two types of models are developed to meaningfully characterize the time‐variant covariates and imperfect maintenance effects. The underlying difference between the two models lies in the way of capturing the influences of covariates and maintenance: The first model reflects these impacts in the degradation rates/paths directly, whereas the second one describes the impacts by modifying the time scales governing the degradation processes. In each model, two particular imperfect maintenance models are presented, which differ in the extent of reduction in degradation level or virtual age. The two degradation models are then compared in certain special cases. The proposed multivariate degradation models pertain to complex industrial systems whose health deterioration can be characterized by multiple performance characteristics and can be altered or affected by maintenance activities and operating/environmental conditions

Model selection for degradation modeling and prognosis with health monitoring data

Health monitoring data are increasingly collected and widely used for reliability assessment and lifetime pre- diction. They not only provide information about degradation state but also could trace failure mechanisms of assets. The selection of a deterioration model that optimally fits in with health monitoring data is an important issue. It can enable a more precise asset health prognostic and help reducing operation and maintenance costs. Therefore, this paper aims to address the problem of degradation model selection including goals, procedure and evaluation criteria. Focusing on continuous degradation modeling including some currently used Lévy processes, the performance of classical and prognostic criteria are discussed through numerous numerical examples. We also investigate in what circumstances which methods perform better than others. The efficiency of a new hybrid criterion is highlighted that allows to take into account the information of goodness-of-fit of observation data when evaluating prognostic measure

Remaining useful life and fault detection models for high voltage electrical connectors focused on predictive maintenance

In recent years, industries have chosen to invest in technology with the aim of making their processes more efficient and thus offering market products of higher quality. Nowadays it is very common for companies to use special systems to predict failures and avoid unexpected delays, reduction of costs, etc. SBI Connectors, along with the Universitat Politècnica de Catalunya, have been collaborating to develop research projects for more than 10 years. As a result of the collaboration with the university, patents and international publications have been generated, which have helped to situate and reinforce SBI Connectors leadership in the international market while offering an image of scientific-technical credibility. This research is carried out, with the collaboration of SBI connectors and Universitat Politècnica de Catalunya, in order to develop the Smartconnector project (within the Retos de Colaboración Spanish research frame). The thesis proposed by the author is dedicated to develop and validate RUL (Remaining Useful Life) and fault detection approaches for electrical substation connectors. The RUL approach proposed in this work is based on a simple and accurate model of the degradation with time of the electrical resistance of the connector (main health indicator), which has two parameters, whose values are identified from on-line acquired data. Next, the fault detection chapter is divided into two parts. The first part presents an on-line condition monitoring method to predict early failures in power connectors from on-line acquired data in conjunction with another parametric degradation model of the resistance of the connector, whose parameters are identified by means of the Markov chain Monte Carlo stochastic method. The second part presents, analyzes and compares the performance of three simple and effective methods for online determination of the State of Health (SoH) of power connectors with low computational requirements. The proposed approaches are based on monitoring the evolution of the connectors’ electrical resistance, which determines the degradation trajectory. Furthermore, this work includes an in-depth study of the temperature dependence of the electrical contact resistance (ECR). To analyze and validate results presented in this work, data is acquired in real time, including main parameters such as the electrical current and voltage drop across the terminals of the connector, conductor and connector temperature, thus estimating the phase shift between voltage drop and electrical current waveforms and the contact resistance by means of accelerated aging tests (ADT).En los últimos años, la industria ha optado por invertir en tecnología con el objetivo de hacer más eficientes sus procesos y así ofrecer al mercado productos de mayor calidad. Hoy en día es muy habitual que las empresas utilicen sistemas especiales para predecir fallos y evitar retrasos inesperados, reducción de costes, etc. SBI connectors, junto con la Universitat Politècnica de Catalunya, colaboran para desarrollar proyectos de investigación desde hace más de 10 años. Fruto de la colaboración con la universidad se han generado patentes y publicaciones internacionales, que han ayudado a situar y reforzar el liderazgo de SBI Connectors en el mercado internacional, al tiempo que ofrece una imagen de credibilidad científico-técnica. Esta tesis doctoral se realiza con la colaboración de SBI connectors y la Universitat Politècnica de Catalunya, para desarrollar el proyecto Smartconnector (dentro del marco de investigación Retos de Colaboración). La tesis propuesta por el autor está dedicada a desarrollar y validar modelos de RUL (Remaining Useful Life) y detección de fallos para conectores de subestaciones eléctricas enfocador al mantemiento predictive. El enfoque RUL propuesto en este trabajo se basa en un modelo simple y preciso de la degradación de la resistencia eléctrica del conector respecto al tiempo (indicador principal de salud), el cual tiene dos parámetros cuyos valores se identifican a partir de datos adquiridos en línea. A continuación, el capítulo de detección de fallos se divide en dos partes. En la primera parte se presenta un método de monitoreo en línea de condición para predecir fallos tempranos en conectores de potencia a partir de datos adquiridos en línea en conjunto con otro modelo paramétrico de degradación de la resistencia del conector, cuyos parámetros son identificados por medio del algoritmo de Markov Chain Monte Carlo. La segunda parte presenta, analiza y compara las prestaciones de tres métodos simples y efectivos para la determinación en línea del Estado de Salud (SoH) de conectores de potencia con bajos requerimientos computacionales. Los enfoques propuestos se basan en el seguimiento de la evolución de la resistencia eléctrica de los conectores, que determina la trayectoria de degradación. Además, este trabajo incluye un estudio en profundidad de la dependencia de la temperatura de la resistencia eléctrica de contacto (ECR). Para analizar y validar todo el trabajo presentado, se adquieren datos en tiempo real, incluyendo parámetros principales como la corriente eléctrica y la caída de tensión en los terminales del conector, la temperatura del conductor y del conector, estimando así el desfase entre la caída de tensión y la tensión eléctrica, forma de onda de corriente y la resistencia de contacto por medio de ensayos de envejecimiento acelerado (ADT).Postprint (published version

Optimal Replacement Strategies for Wind Energy Systems

Motivated by rising energy prices, global climate change, escalating demand for electricity and global energy supply uncertainties, the U.S. government has established an ambitious goal of generating 80% of its electricity supply from clean, renewable sources by 2035. Wind energy is poised to play a prominent role in achieving this goal as it is estimated that 20% of the total domestic electricity supply can be reliably generated by land-based and offshore wind turbines by 2030. However, the cost of producing wind energy remains a significant barrier with operating and maintenance (O&M) costs contributing 20 to 47.5% of the total cost of energy. Given the urgent need for clean, renewable energy sources, and the widespread appeal of wind energy as a viable alternative, it is imperative to develop effective techniques to reduce the O&M costs of wind energy. This dissertation presents a framework within which real-time, condition-based data can be exploited to optimally time the replacement of critical wind turbine components. First, hybrid analytical-statistical tools are developed to estimate the current health of the component and approximate the expected time at which it will fail by observing a surrogate signal of degradation. The signal is assumed to evolve as a switching diffusion process, and its parameters are estimated via a novel Markov chain Monte Carlo procedure. Next, the problem of optimally replacing a critical component that resides in a partially-observable environment is addressed. Two models are formulated using a partially-observed Markov decision process (POMDP) framework. The first model ignores the cost of turbine downtime, while the second includes this cost explicitly. For both models, it is shown that a threshold replacement policy is optimal with respect to the cumulative level of component degradation. A third model is presented that considers cases in which the environment is partially observed and degradation measurements are uncertain. A threshold policy is shown to be optimal for a special case of this model. Several numerical examples will illustrate the main results and the value of including environmental observations in the wind energy setting