Multiple-Phase Modeling of Degradation Signal for Condition Monitoring and Remaining Useful Life Prediction

Remaining useful life prediction plays an important role in ensuring the safety, availability, and efficiency of various engineering systems. In this paper, we propose a flexible Bayesian multiple-phase modeling approach to characterize degradation signals for prognosis. The priors are specified with a novel stochastic process and the multiple-phase model is formulated to a novel state-space model to facilitate online monitoring and prediction. A particle filtering algorithm with stratified sampling and partial Gibbs resample-move strategy is developed for online model updating and residual life prediction. The advantages of the proposed method are demonstrated through extensive numerical studies and real case studies

A critical review of online battery remaining useful lifetime prediction methods.

Lithium-ion batteries play an important role in our daily lives. The prediction of the remaining service life of lithium-ion batteries has become an important issue. This article reviews the methods for predicting the remaining service life of lithium-ion batteries from three aspects: machine learning, adaptive filtering, and random processes. The purpose of this study is to review, classify and compare different methods proposed in the literature to predict the remaining service life of lithium-ion batteries. This article first summarizes and classifies various methods for predicting the remaining service life of lithium-ion batteries that have been proposed in recent years. On this basis, by selecting specific criteria to evaluate and compare the accuracy of different models, find the most suitable method. Finally, summarize the development of various methods. According to the research in this article, the average accuracy of machine learning is 32.02% higher than the average of the other two methods, and the prediction cycle is 9.87% shorter than the average of the other two methods

Model-free non-invasive health assessment for battery energy storage assets

Increasing penetration of renewable energy generation in the modern power network introduces uncertainty about the energy available to maintain a balance between generation and demand due to its time-fluctuating output that is strongly dependent on the weather. With the development of energy storage technology, there is the potential for this technology to become a key element to help overcome this intermittency in a generation. However, the increasing penetration of battery energy storage within the power network introduces an additional challenge to asset owners on how to monitor and manage battery health. The accurate estimation of the health of this device is crucial in determining its reliability, power-delivering capability and ability to contribute to the operation of the whole power system. Generally, doing this requires invasive measurements or computationally expensive physics-based models, which do not scale up cost-effectively to a fleet of assets. As storage aggregation becomes more commonplace, there is a need for a health metric that will be able to predict battery health based only on the limited information available, eliminating the necessity of installation of extensive telemetry in the system. This work develops a solution to battery health prognostics by providing an alternative, a non-invasive approach to the estimation of battery health that estimates the extent to which a battery asset has been maloperated based only on the battery-operating regime imposed on the device. The model introduced in this work is based on the Hidden Markov Model, which stochastically models the battery limitations imposed by its chemistry as a combination of present and previous sequential charging actions, and articulates the preferred operating regime as a measure of health consequence. The resulting methodology is demonstrated on distribution network level electrical demand and generation data, accurately predicting maloperation under a number of battery technology scenarios. The effectiveness of the proposed battery maloperation model as a proxy for actual battery degradation for lithium-ion technology was also tested against lab tested battery degradation data, showing that the proposed health measure in terms of maloperation level reflected that measured in terms of capacity fade. The developed model can support condition monitoring and remaining useful life estimates, but in the wider context could also be used as the policy function in an automated scheduler to utilise assets while optimising their health.Increasing penetration of renewable energy generation in the modern power network introduces uncertainty about the energy available to maintain a balance between generation and demand due to its time-fluctuating output that is strongly dependent on the weather. With the development of energy storage technology, there is the potential for this technology to become a key element to help overcome this intermittency in a generation. However, the increasing penetration of battery energy storage within the power network introduces an additional challenge to asset owners on how to monitor and manage battery health. The accurate estimation of the health of this device is crucial in determining its reliability, power-delivering capability and ability to contribute to the operation of the whole power system. Generally, doing this requires invasive measurements or computationally expensive physics-based models, which do not scale up cost-effectively to a fleet of assets. As storage aggregation becomes more commonplace, there is a need for a health metric that will be able to predict battery health based only on the limited information available, eliminating the necessity of installation of extensive telemetry in the system. This work develops a solution to battery health prognostics by providing an alternative, a non-invasive approach to the estimation of battery health that estimates the extent to which a battery asset has been maloperated based only on the battery-operating regime imposed on the device. The model introduced in this work is based on the Hidden Markov Model, which stochastically models the battery limitations imposed by its chemistry as a combination of present and previous sequential charging actions, and articulates the preferred operating regime as a measure of health consequence. The resulting methodology is demonstrated on distribution network level electrical demand and generation data, accurately predicting maloperation under a number of battery technology scenarios. The effectiveness of the proposed battery maloperation model as a proxy for actual battery degradation for lithium-ion technology was also tested against lab tested battery degradation data, showing that the proposed health measure in terms of maloperation level reflected that measured in terms of capacity fade. The developed model can support condition monitoring and remaining useful life estimates, but in the wider context could also be used as the policy function in an automated scheduler to utilise assets while optimising their health

Nonlinear Stochastic Filtering for Online State of Charge and Remaining Useful Life Estimation of Lithium-ion Battery

Battery state monitoring is one of the key techniques in Battery Management System (BMS). Accurate estimation can help to improve the system performance and to prolong the battery lifetime. The main challenges for the state online estimation of Li-ion batteries are the flat characteristic of open circuit voltage (OCV) with the function of the state of charge. Hence, the focus of this thesis study is to estimation of the state of charge (SOC) of Li-ion with high accuracy, more robustness. A 2nd order RC equivalent circuit model is adapted to battery model for simulation, mathematical model analysis, and dynamics characteristic of battery study. Model parameters are identified with MATLAB battery model simulation. Although with more lumped RC loaders, the model is more accurate, high computation with a higher nonlinear function of output will be. So, a discrete state space model for the battery is developed. For a complex battery model with strong nonlinearity, Sequential Monte Carlo (SMC) method can be utilized to perform the on-line SOC estimation. An SMC integrates the Bayesian learning methods with sequential importance sampling. SMC approximate the posterior density function by a set of particles with associated weights, which is developed in MATLAB environment to estimate on-line SOC. A comparison is presented with Kalman Filtering and Extended Kalman Filtering to validated estimation results with SMC. Finally, the comparison results provide that SMC method is more accurate and robust then KF and EKF. Accurately prediction of Remaining Useful Life of Li-ion batteries is necessary to reliable system operation and monitoring the BMS. An empirical model for capacity degradation has been developed based on experimentally obtained capacity fade data. A nonlinear, non-Gaussian state space model is developed for empirical model. The obtained empirical model used in Sequential Monte Carlo (SMC) framework is to update the on-line state and model parameters to make a prediction of remaining useful life of a Li-ion battery at various lifecycle

Prognostics health management: perspectives in engineering systems reliability prognostics

The Prognostic Health Management (PHM) has been asserting itself as the most promising methodology to enhance the effective reliability and availability of a product or system during its life-cycle conditions by detecting current and approaching failures, thus, providing mitigation of the system risks with reduced logistics and support costs. However, PHM is at an early stage of development, it also expresses some concerns about possible shortcomings of its methods, tools, metrics and standardization. These factors have been severely restricting the applicability of PHM and its adoption by the industry. This paper presents a comprehensive literature review about the PHM main general weaknesses. Exploring the research opportunities present in some recent publications, are discussed and outlined the general guide-lines for finding the answer to these issues.(undefined

Prognostics and Health Management of Industrial Equipment

ISBN13: 9781466620957Prognostics and health management (PHM) is a field of research and application which aims at making use of past, present and future information on the environmental, operational and usage conditions of an equipment in order to detect its degradation, diagnose its faults, predict and proactively manage its failures. The present paper reviews the state of knowledge on the methods for PHM, placing these in context with the different information and data which may be available for performing the task and identifying the current challenges and open issues which must be addressed for achieving reliable deployment in practice. The focus is predominantly on the prognostic part of PHM, which addresses the prediction of equipment failure occurrence and associated residual useful life (RUL)

Multiple-Change-Point Modeling and Exact Bayesian Inference of Degradation Signal for Prognostic Improvement

Prognostics play an increasingly important role in modern engineering systems for smart maintenance decision-making. In parametric regression-based approaches, the parametric models are often too rigid to model degradation signals in many applications. In this paper, we propose a Bayesian multiple-change-point (CP) modeling framework to better capture the degradation path and improve the prognostics. At the offline modeling stage, a novel stochastic process is proposed to model the joint prior of CPs and positions. All hyperparameters are estimated through an empirical two-stage process. At the online monitoring and remaining useful life (RUL) prediction stage, a recursive updating algorithm is developed to exactly calculate the posterior distribution and RUL prediction sequentially. To control the computational cost, a fixed-support-size strategy in the online model updating and a partial Monte Carlo strategy in the RUL prediction are proposed. The effectiveness and advantages of the proposed method are demonstrated through thorough simulation and real case studies

Degradation Modeling and RUL Prediction Using Wiener Process Subject to Multiple Change Points and Unit Heterogeneity

Degradation modeling is critical for health condition monitoring and remaining useful life prediction (RUL). The prognostic accuracy highly depends on the capability of modeling the evolution of degradation signals. In many practical applications, however, the degradation signals show multiple phases, where the conventional degradation models are often inadequate. To better characterize the degradation signals of multiple-phase characteristics, we propose a multiple change-point Wiener process as a degradation model. To take into account the between-unit heterogeneity, a fully Bayesian approach is developed where all model parameters are assumed random. At the offline stage, an empirical two-stage process is proposed for model estimation, and a cross-validation approach is adopted for model selection. At the online stage, an exact recursive model updating algorithm is developed for online individual model estimation, and an effective Monte Carlo simulation approach is proposed for RUL prediction. The effectiveness of the proposed method is demonstrated through thorough simulation studies and real case study

Review of Health Prognostics and Condition Monitoring of Electronic Components

To meet the specifications of low cost, highly reliable electronic devices, fault diagnosis techniques play an essential role. It is vital to find flaws at an early stage in design, components, material, or manufacturing during the initial phase. This review paper attempts to summarize past development and recent advances in the areas about green manufacturing, maintenance, remaining useful life (RUL) prediction, and like. The current state of the art in reliability research for electronic components, mainly includes failure mechanisms, condition monitoring, and residual lifetime evaluation is explored. A critical analysis of reliability studies to identify their relative merits and usefulness of the outcome of these studies' vis-a-vis green manufacturing is presented. The wide array of statistical, empirical, and intelligent tools and techniques used in the literature are then identified and mapped. Finally, the findings are summarized, and the central research gap is highlighted