Integration of Expert Judgment into Remaining Useful Lifetime Prediction of Components

AbstractAccurate estimates of renewal instances are necessary to avoid both machine breakdowns as well as unnecessary replacements. These instances are based upon a prognosis of the remaining useful lifetime of the components investigated but their usability is limited because of insufficient failure time data. Therefore the paper at hand deals with the integration of expert knowledge into lifetime estimation methods to enhance the prediction result. A model has been developed and simulated. Furthermore a parameter study analyzes the model's effectiveness, which produces promising results

RMS Based Health Indicators for Remaining Useful Lifetime Estimation of Bearings

Estimating the remaining useful life (RUL) of bearings from healthy to faulty is important for predictive maintenance. The bearing fault severity can be estimated based on the energy or root mean square (RMS) of vibration signals, and a stopping criterion can be set based on a threshold given by an ISO standard. However, the vibration RMS is often not monotonically increasing with damage, which renders a challenge for predicting the RUL. This study proposes a novel method for splitting the vibration signal into multiple frequency bands before RMS calculations to generate multiple health indicators. Monotonic health indicators are identified using the Spearman coefficient, and the RUL is afterward estimated for each indicator using a suitable model and parameter update scheme. Historical failure data is not required to set any parameters. The proposed method is tested with the Paris' law, where parameters are updated by particle filters. Experimental results from two test rigs validate the performance of the proposed method.publishedVersio

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

Domain Adaptation via Alignment of Operation Profile for Remaining Useful Lifetime Prediction

Effective Prognostics and Health Management (PHM) relies on accurate prediction of the Remaining Useful Life (RUL). Data-driven RUL prediction techniques rely heavily on the representativeness of the available time-to-failure trajectories. Therefore, these methods may not perform well when applied to data from new units of a fleet that follow different operating conditions than those they were trained on. This is also known as domain shifts. Domain adaptation (DA) methods aim to address the domain shift problem by extracting domain invariant features. However, DA methods do not distinguish between the different phases of operation, such as steady states or transient phases. This can result in misalignment due to under- or over-representation of different operation phases. This paper proposes two novel DA approaches for RUL prediction based on an adversarial domain adaptation framework that considers the different phases of the operation profiles separately. The proposed methodologies align the marginal distributions of each phase of the operation profile in the source domain with its counterpart in the target domain. The effectiveness of the proposed methods is evaluated using the New Commercial Modular Aero-Propulsion System (N-CMAPSS) dataset, where sub-fleets of turbofan engines operating in one of the three different flight classes (short, medium, and long) are treated as separate domains. The experimental results show that the proposed methods improve the accuracy of RUL predictions compared to current state-of-the-art DA methods.Comment: 18 pages,11 figure

Overview of Machine Learning Methods for Lithium-Ion Battery Remaining Useful Lifetime Prediction

Lithium-ion batteries play an indispensable role, from portable electronic devices to electric vehicles and home storage systems. Even though they are characterized by superior performance than most other storage technologies, their lifetime is not unlimited and has to be predicted to ensure the economic viability of the battery application. Furthermore, to ensure the optimal battery system operation, the remaining useful lifetime (RUL) prediction has become an essential feature of modern battery management systems (BMSs). Thus, the prediction of RUL of Lithium-ion batteries has become a hot topic for both industry and academia. The purpose of this work is to review, classify, and compare different machine learning (ML)-based methods for the prediction of the RUL of Lithium-ion batteries. First, this article summarizes and classifies various Lithium-ion battery RUL estimation methods that have been proposed in recent years. Secondly, an innovative method was selected for evaluation and compared in terms of accuracy and complexity. DNN is more suitable for RUL prediction due to its strong independent learning ability and generalization ability. In addition, the challenges and prospects of BMS and RUL prediction research are also put forward. Finally, the development of various methods is summarized

Predicting the Remaining Useful Lifetime of a Proton Exchange Membrane Fuel Cell using an Echo State Network

International audienceOne remaining technological bottleneck to develop industrial Fuel Cell (FC) applications resides in the system limited useful lifetime. Consequently, it's important to develop failure diagnostic and prognostic tools enabling the optimization of FC. The Prognostic and Heath Management (PHM) is a discipline involved in the process of industrial maintenance. The objective, in PHM, is to estimate the Remaining Useful Life (RUL) of a system by predicting its future behavior. The RUL enables to predict the moment when a fault could occur on a system. It also allows identifying the relevant part of the system where a fault could happen. Then, a preventive maintenance could be performed to avoid non-reversible degradations. Three main prognosis approaches can be distinguished: model-based, data-based and hybrid methods. Data-based methods such as Artificial Neural Network (ANN), aim to estimate the ageing behavior of the process without specific knowledges related to the physical system phenomenon. Nevertheless, the deployment of such an approach can be a tedious work, mainly due to the trial and error algorithm method, which represents a real problem for industrial applications where real-time complying algorithms must be developed. Among the various methods of this area, the tool chosen here is called Echo State Network (ESN). An ESN consists in the use of a dynamical neurons reservoir where the training step consists in performing a linear regression. The computation time of this algorithm is thus shorter while keeping the same modeling capability of a Recurrent Neural Network (RNN). Created in 2001 by H. Jaeger, an ESN proposes a better human brain paradigm than traditional ANN, and are based on a reservoir of neurons randomly connected to each other. The aim of this paper is to study the application of ESN as a prognostics system enabling the estimation of the Remaining Useful Life of a Proton Exchange Membrane Fuel Cell using an iterative predictive structure, which is the most common approach performing a one-step prediction. This estimation output value is used in the next step as one of the input regressor and these operations can be repeated until the desired prediction horizon. The results obtained thanks to this method exhibits good prediction and they will be detailed in this paper

Remaining Useful Lifetime Prognosis of Controlled Systems: A Case of Stochastically Deteriorating Actuator

This paper addresses the case of automatic controlled system which deteriorates during its operation because of components' wear or deterioration. Depending on its specific closed-loop structure, the controlled system has the ability to compensate for disturbances affecting the actuators which can remain partially hidden. The deterioration modeling and the Remaining Useful Lifetime (RUL) estimation for such closed-loop dynamic system have not been addressed extensively. In this paper, we consider a controlled system with Proportional-Integral-Derivative controller. It is assumed that the actuator is subject to shocks that occur randomly in time. An integrated model is proposed to jointly describe the state of the controlled process and the actuator deterioration. Only the output of the controlled system is available to assess its health condition. By considering a Piecewise Deterministic Markov Process, the RUL of the system can be estimated by a two-step approach. In the first step referred as the "Diagnosis" step, the system state is estimated online from the available monitoring observations by using a particle filtering method. In the second step referred as the "Prognosis" step, the RUL is estimated as a conditional reliability by Monte Carlo simulation. To illustrate the approach, a simulated tank level control system is used