Prognostics and Health Management for Battery Remaining Useful Life Prediction Based on Electrochemistry Model: A Tutorial

Background: Remaining useful life (RUL) estimation is the central mission to the complex systems’ prognostics and health management. During last decades, numbers of developments and applications of the RUL estimation have proliferated. Objective: As one of the most popular approaches, stochastic process-based approach has been widely used for characterizing the degradation trajectories and estimating RULs. This paper aimed at reviewing the latest methods and patents on this topic. Methods: The review is concentrated on four common stochastic processes for degradation modelling and RUL estimation, i.e., Gamma process, Wiener process, inverse Gaussian process and Markov chain. Results: After a briefly review of these four models, we pointed out the pros and cons of them, as well as the improvement direction of each method. Conclusion: For better implementation, the applications of these four approaches on maintenance and decision-making are systematically introduced. Finally, the possible future trends are concluded tentatively.

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Application of Prognostics and Health Management (PHM) to Software System Fault and Remaining Useful Life (RUL) Prediction

10.1115/detc2021-70508 ◽

2021 ◽

Author(s):

Mohammad Rubyet Islam ◽

Peter Sandborn

Keyword(s):

Health Management ◽

Standard Test ◽

Remaining Useful Life ◽

Software Systems ◽

Prognostics And Health Management ◽

Performance Parameters ◽

Statistical Validation ◽

Useful Life ◽

Test Beds

Abstract Prognostics and Health Management (PHM) is an engineering discipline focused on predicting the point at which systems or components will no longer perform as intended. The prediction is often articulated as a Remaining Useful Life (RUL). RUL is an important decision-making tool for contingency mitigation, i.e., the prediction of an RUL (and its associated confidence) enables decisions to be made about how and when to maintain the system. PHM is generally applied to hardware systems in the electronics and non-electronics application domains. The application of PHM (and RUL) concepts has not been explored for application to software. Today, software (SW) health management is confined to diagnostic assessments that identify problems, whereas prognostic assessment potentially indicates when in the future a problem will become detrimental to the operation of the system. Relevant areas such as SW defect prediction, SW reliability prediction, predictive maintenance of SW, SW degradation, and SW performance prediction, exist, but all represent static models, built upon historical data — none of which can calculate an RUL. This paper addresses the application of PHM concepts to software systems for fault predictions and RUL estimation. Specifically, we wish to address how PHM can be used to make decisions for SW systems such as version update, module changes, rejuvenation, maintenance scheduling and abandonment. This paper presents a method to prognostically and continuously predict the RUL of a SW system based on usage parameters (e.g., numbers and categories of releases) and multiple performance parameters (e.g., response time). The model is validated based on actual data (on performance parameters), generated by the test beds versus predicted data, generated by a predictive model. Statistical validation (regression validation) has been carried out as well. The test beds replicate and validate faults, collected from a real application, in a controlled and standard test (staging) environment. A case study based on publicly available data on faults and enhancement requests for the open-source Bugzilla application is presented. This case study demonstrates that PHM concepts can be applied to SW systems and RUL can be calculated to make decisions on software version update or upgrade, module changes, rejuvenation, maintenance schedule and total abandonment.

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In-situ remaining useful life prediction of aircraft auxiliary power unit based on quantitative analysis of on-wing sensing data

Advances in Mechanical Engineering ◽

10.1177/1687814020911475 ◽

2020 ◽

Vol 12 (3) ◽

pp. 168781402091147

Author(s):

Liansheng Liu ◽

Qing Guo ◽

Lulu Wang ◽

Datong Liu

Keyword(s):

Power Unit ◽

Life Prediction ◽

Health Management ◽

Remaining Useful Life ◽

Maintenance And Repair ◽

Sensing Data ◽

Auxiliary Power Unit ◽

Auxiliary Power ◽

Useful Life

The in-situ prognostics and health management of aircraft auxiliary power unit faces difficulty using the sparse on-wing sensing data. As the key technology of prognostics and health management, remaining useful life prediction of in-situ aircraft auxiliary power unit is hard to achieve accurate results. To solve this problem, we propose one kind of quantitative analysis of its on-wing sensing data to implement remaining useful life prediction of auxiliary power unit. Except the most important performance parameter exhaust gas temperature, the other potential parameters are utilized based on mutual information, which can be used as the quantitative metric. In this way, the quantitative threshold of mutual information for enhancing remaining useful life prediction result can be determined. The implemented cross-validation experiments verify the effectiveness of the proposed method. The real on-wing sensing data of auxiliary power unit for experiment are from China Southern Airlines Company Limited Shenyang Maintenance Base, which spends over $6.5 million on auxiliary power unit maintenance and repair each year for the fleet of over 500 aircrafts. Although the relative improvement is not too large, it is helpful to reduce the maintenance and repair cost.

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Physics Based Deep Learning Technique for Prognostics

Annual Conference of the PHM Society ◽

10.36001/phmconf.2019.v11i1.916 ◽

2019 ◽

Vol 11 (1) ◽

Author(s):

Khaled Akkad

Keyword(s):

Pattern Recognition ◽

Deep Learning ◽

Health Management ◽

Recognition Performance ◽

Remaining Useful Life ◽

Data Driven ◽

Prognostics And Health Management ◽

Learning Technique ◽

Useful Life

Remaining useful life (RUL) estimation is one of the most important aspects of prognostics and health management (PHM). Various deep learning (DL) based techniques have been developed and applied for the purposes of RUL estimation. One limitation of DL is the lack of physical interpretations as they are purely data driven models. Another limitation is the need for an exceedingly large amount of data to arrive at an acceptable pattern recognition performance for the purposes of RUL estimation. This research is aimed to overcome these limitations by developing physics based DL techniques for RUL prediction and validate the method with real run-to-failure datasets. The contribution of the research relies on creating hybrid DL based techniques as well as combining physics based approaches with DL techniques for effective RUL prediction.

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Outlier Modeling in Gear Bearing Using Autoencoder for Remaining Useful Life Prediction

10.26434/chemrxiv.8378342 ◽

2019 ◽

Author(s):

Sunny Singh ◽

Praneet Shiv ◽

Atif Ahmed

Keyword(s):

Life Prediction ◽

Health Management ◽

Health Indicators ◽

Remaining Useful Life ◽

System Failure ◽

Mechanical Signal ◽

Long Time ◽

Data Points ◽

Useful Life ◽

Bearing System

In this paper, we introduce the Prognostics and Health Management of gear bearing system using autoencoder neural networks. Bearings and gears are the most common mechanical components in rotating machines, and their health conditions are of great concern in practice. This study presents an outlier modeling method for forecasting the gear bearing system failure using the health indicators constructed from mechanical signal processing and modeling. Outlier modeling aims to find patterns in data that are significantly different from what is defined as normal. In the unsupervised outlier modeling setting, prior labels about the anomalousness of data points are not available. In such cases, the most common techniques for scoring data points for outlyingness include distance-based methods density-based methods, and linear methods. The conventional outlier modeling methods have been used for a long time to detect anomalous observations in data. However, this paper shows that autoencoders are a very competitive technique compared to other existing methods. The developed method is demonstrated using the IMS bearing data from NASA Acoustics and Vibration Database.

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Remaining useful life prediction for power-shift steering transmission based on fusion of multiple oil spectra

Advances in Mechanical Engineering ◽

10.1177/1687814018784201 ◽

2018 ◽

Vol 10 (6) ◽

pp. 168781401878420 ◽

Cited By ~ 5

Author(s):

Shu-Fa Yan ◽

Biao Ma ◽

Chang-Song Zheng

Keyword(s):

Life Prediction ◽

Health Management ◽

Principal Component ◽

Remaining Useful Life ◽

Cumulative Distribution ◽

Kernel Principal Component Analysis ◽

Degradation Index ◽

Life Estimation ◽

Power Shift ◽

Useful Life

Remaining useful life prediction is a critical issue to fault diagnosis and health management of power-shift steering transmission. Power-shift steering transmission wear, which leads to the increase of wear particles and severe wear afterwards, is a slow degradation process, which can be monitored by oil spectral analysis, but the actual degree of the power-shift steering transmission degradation is often difficult to evaluate. The main purpose of this article is to provide a more accurate remaining useful life prediction methodology for power-shift steering transmission compared to relying solely on an individual spectral oil data. Our methodology includes multiple degradation data fusion, degradation index construction, degradation modelling and remaining useful life estimation procedures. First, the robust kernel principal component analysis is used to reduce the data dimension, and the state space model is utilized to construct the wear degradation index. Then, the Wiener process–based degradation model is established based on the constructed degradation index, and the explicit formulas for several important quantities for remaining useful life estimation such as the probability density function and cumulative distribution function are derived. Finally, a case study is presented to demonstrate the applicability of the proposed methodology. The results show that the proposed remaining useful life prediction methodology can objectively describe the power-shift steering transmission degradation law, and the predicted remaining useful life has been extended as 65 Mh (38.2%) compared with specified maintenance interval. This will reduce the maintenance times of power-shift steering transmission life cycle and finally save the maintenance costs.

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Enhancing Decisions in Prognostics and Health Management Framework

International Journal of Prognostics and Health Management ◽

10.36001/ijphm.2020.v11i1.2607 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Omar Bougacha ◽

Christophe Varnier

Keyword(s):

Decision Making ◽

Closed Loop ◽

Health Management ◽

Remaining Useful Life ◽

Prognostics And Health Management ◽

Management Framework ◽

Useful Life ◽

Research Studies

Prognostics and health management have become increasingly important in recent years. Many research studies focus on a crucial phase consisting of predicting the remaining useful life of equipment or a component. However, this step is often carried out without taking into account the decisions that will be taken later. This article aims to propose a modification of the existing PHM framework to combine the prognostics and decision-making phases in a closed loop. In this paper, the presented framework is described and some elements for its implementation are proposed. A simplifiedexample is developed to illustrate the presented methodology of post-prognostic decision enhancement.

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Outlier Modeling in Gear Bearing Using Autoencoder for Remaining Useful Life Prediction

10.20944/preprints201907.0112.v1 ◽

2019 ◽

Author(s):

Sunny Singh ◽

Praneet Shiv ◽

Atif Ahmed

Keyword(s):

Life Prediction ◽

Health Management ◽

Health Indicators ◽

Remaining Useful Life ◽

System Failure ◽

Mechanical Signal ◽

Long Time ◽

Data Points ◽

Useful Life ◽

Bearing System

In this paper, we introduce the Prognostics and Health Management of gear bearing system using autoencoder neural networks. Bearings and gears are the most common mechanical components in rotating machines, and their health conditions are of great concern in practice. This study presents an outlier modeling method for forecasting the gear bearing system failure using the health indicators constructed from mechanical signal processing and modeling. Outlier modeling aims to find patterns in data that are significantly different from what is defined as normal. In the unsupervised outlier modeling setting, prior labels about the anomalousness of data points are not available. In such cases, the most common techniques for scoring data points for outlyingness include distance-based methods density-based methods, and linear methods. The conventional outlier modeling methods have been used for a long time to detect anomalous observations in data. However, this paper shows that autoencoders are a very competitive technique compared to other existing methods. The developed method is demonstrated using the IMS bearing data from NASA Acoustics and Vibration Database.

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Identifying Parameters for Nonlinear Degradation-Based Prognostics

Advances in Computer and Electrical Engineering - Advanced Condition Monitoring and Fault Diagnosis of Electric Machines ◽

10.4018/978-1-5225-6989-3.ch012 ◽

2019 ◽

pp. 237-260

Author(s):

Feng Yang ◽

Mohamed Salahuddin

Keyword(s):

Parameter Identification ◽

Induction Motors ◽

Health Management ◽

Degradation Process ◽

Remaining Useful Life ◽

Prognostics And Health Management ◽

Health Index ◽

Identification Method ◽

Useful Life ◽

Parameter Values

Prognostics and health management (PHM) methodologies are increasingly playing active roles in improving the availability, reliability, efficiency, productivity, and safety of systems in many industries. In predicting the remaining useful life (RUL), this chapter introduces a prognostics framework with health index (HI) formulation, with specific emphasis on incorporating and validating nonlinear HI degradations. The key issue to the success of this framework is how to identify appropriate parameters in describing the behavior of the nonlinear HI degradations. Using exponential HI degradation as an example in predicting the RULs of induction motors, this chapter discusses three different explorations in verifying the existence of good parameter values as well as identifying the appropriate parameters automatically. Comprehensive experiments were carried out with degradation process (DP) data from eight induction motors, and it was discovered that good parameters can be automatically determined with the proposed parameter identification method.

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An Applicable Predictive Maintenance Framework for the Absence of Run-to-Failure Data

Applied Sciences ◽

10.3390/app11115180 ◽

2021 ◽

Vol 11 (11) ◽

pp. 5180

Author(s):

Donghwan Kim ◽

Seungchul Lee ◽

Daeyoung Kim

Keyword(s):

Health Management ◽

Real Life ◽

Remaining Useful Life ◽

Predictive Maintenance ◽

Prognostics And Health Management ◽

Health Index ◽

Computing Technology ◽

Simulation Data ◽

Failure Data ◽

Useful Life

As technology advances, the equipment becomes more complicated, and the importance of the Prognostics and Health Management (PHM) to monitor the condition of the equipment has risen. In recent years, various methodologies have emerged. With the development of computing technology, methodologies using machine learning and deep learning are gaining attention, in particular. As these algorithms become more advanced, the performance of detecting anomalies and predicting failures has improved dramatically. However, most of the studies are cases that depend on simulation data or assumed abnormal conditions. In addition, regardless of the existence of run-to-failure data, the methodologies are difficult to apply to the industrial site directly. To solve this problem, we propose a Predictive Maintenance (PdM) framework based on unsupervised learning in this paper, which can be applied directly in the industrial field regardless of run-to-failure data. The proposed framework consists of data acquisition, preprocessing data, constructing a Health Index, and predicting the remaining useful life. We propose a framework that can create and monitor models even when there are no accumulated run-to-failure data. The proposed framework was conducted in two different real-life cases, and the usefulness and applicability of the proposed methodology were verified.

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