Query: Degradation mechanism models RUL

title: Bi directional long short term memory recurrent neural network with attention for stack voltage degradation from proton exchange membrane fuel cells
abstract: Proton exchange membrane fuel cells (PEMFCs) have zero emissions and provide power to a variety of devices, such as automobiles and portable equipment. We propose a bi directional long short term memory recurrent neural network with an attention mechanism (BILSTM AT) model to predict the voltage degradation of the PEMFC stack. Random forest regression model is used to extract essential variables as inputs in the model. The prediction interval is derived by using the dropout method. Model parameters are determined by an optimization method. The test data of the two PEMFC stacks are used to compare the proposed model with some existing models. The prediction results show that BILSTM AT outperforms other models. Moreover, the proposed model with a sliding window method on remaining useful life (RUL) prediction can achieve more accurate results, with a relative error of about 0.09%~0.29%

title: Remaining Useful Life Model and Assessment of Mechanical Products: A Brief Review and a Note on the State Space Model Method
abstract: The remaining useful life (RUL) prediction of mechanical products has been widely studied for online system performance reliability, device remanufacturing, and product safety (safety awareness and safety improvement) These studies incorporated many different models, algorithms, and techniques for modeling and assessment. In this paper, methods of RUL assessment are summarized and expounded upon using two major methods: physics model based and data driven based methods. The advantages and disadvantages of each of these methods are deliberated and compared as well. Due to the intricacy of failure mechanism in system, and difficulty in physics degradation observation, RUL assessment based on observations of performance variables turns into a science in evaluating the degradation. A modeling method from control systems, the state space model (SSM) as a first order hidden Markov, is presented. In the context of non linear and non Gaussian systems, the SSM methodology is capable of performing remaining life assessment by using Bayesian estimation (sequential Monte Carlo) Being effective for non linear and non Gaussian dynamics, the methodology can perform the assessment recursively online for applications in CBM (condition based maintenance) PHM (prognostics and health management) remanufacturing, and system performance reliability. Finally, the discussion raises concerns regarding online sensing data for SSM modeling and assessment of RUL.

title: A Domain Adaptive Convolutional LSTM Model for Prognostic Remaining Useful Life Estimation Under Variant Conditions
abstract: In the age of industry 4.0 and smart manufacturing, a large volume of sensor data is produced from cyber physical systems (CPS) and prediction of remaining useful life (RUL) of a machine or system becomes crucial for prognostics and health management (PHM) Several linear regression and deep learning models have been studied to extract features from segmented time windows and learn the degradation patterns. However, distributions of features are varying between source learning domain and target test domains, due to different working conditions and environments. Thus, the generalization of traditional methods will be influenced, which leads to performance degradation. This paper develops a domain adaptive CNN LSTM (DACL) model to predict the RUL of a system based on the multi dimensional sensor data. The DACL model combines the CNN and LSTM with domain adaptive transfer mechanism and takes the operating conditions into consideration. The features extracted by CNN of both source and target data are transformed to a higher dimensional space by reproducing kernel Hilbert space (RKHS) and the loss function is compensated by using maximum mean discrepancy (MMD) to reduce the distributions discrepancy. The model is evaluated on C MAPSS dataset and demonstrate its performance improvement by comparing with previous methods.

title: Joint model for residual life estimation based on Long Short Term Memory network
abstract: With the rapid development of industrial internet of things, multi sensor technology has been widely used in system condition monitoring and residual useful life (RUL) estimation, which plays a crucial role in preventing catastrophic failures and reducing maintenance losses. However, current prognosis using multi sensor data faces the following challenges: (i) Manual feature selection and extraction and exploration of degradation failure mechanism for a complex domain require a substantial amount of human labor and expertise from the practitioner, which is seldom available. (ii) Data fusion and prognosis are usually divided into two separate steps, resulting in the lack of intrinsic relationship between the two tasks. (iii) The end to end prediction methods directly based on deep learning behave like the black box and provide no information for degradation progression. To overcome these drawbacks, a prediction framework comprising two deep learning models is proposed in this paper. Through the proposed supervised joint training scheme, the framework not only provides a continuous visualization progression of system degradation but also ensures that the generated fusion signal effectively performs in RUL prediction. As a framework application, a joint model of RUL estimation based on an ordinary long short term memory network is designed. In the experiment section, a simulation study is firstly performed to show its RUL prediction performance. Then, a case study is conducted using two public experimental data sets (the aircraft turbofan engine data and the milling) Furthermore, the advantage of the proposed joint model is validated by carrying out a comparison with other methods based on the same experimental data sets.

title: A new physics based data driven guideline for wear modelling and prediction of train wheels
abstract: Wear modelling of train wheels has long been an important research topic; both physics based and data based methods have some weaknesses. To bridge the gap between them and take their advantages, this paper proposes a new physics based data driven guideline for wheel wear modelling and prediction. First, based on wear mechanism analysis, the basic model of tread wear and flange wear are designed considering their correlation; wear models are established separately for different wheel positions considering the uncertainty in a wear process, and further trained mathematically with wear data. Second, wheel reprofiling is closely related to wheel wear and is modelled based on theoretical analysis and reprofiling data. Then, the numerical method for predicting wheel degradation is proposed based on the closed loop alternation between wear and reprofiling; the remaining useful life (RUL) of wheels is further evaluated through point estimation and interval estimation. Finally, the good agreement between trained models and wear data validates the wear models; the proposed guideline is verified by measurement data to produce accurate prediction of wheel degradation and effective evaluation of wheel RUL. The proposed guideline has been applied to the prognostics and health management of wheels for various train types.

title: A Prognostic Framework for PEMFC Based on Least Squares Support Vector Regression Particle Filter
abstract: Proton exchange membrane fuel cell (PEMFC) a promising energy device, has been widely used thanks to its high energy density and low pollutant emissions. Prognostics of PEMFC has attracted increasing attention nowadays, which aims at predicting the health degradation of PEMFC and estimating the residual useful life (RUL) for better management. However, majority existing prognostic methods for PEMFC are restricted for real applications due to the following reasons: 1) accurate models which rely on complex underlying mechanism are hard to obtain; 2) data driven methods usually depend on large amounts of data; 3) the prediction results are usually an estimated value rather than a probability distribution of RUL. Aiming at solving these problems, this paper proposes a prediction framework for PEMFC based on least squares support vector regression particle filter (LSSVR PF) As an intelligent prognostic method, LSSVR can achieve a good performance with limited training samples. The predicted values of LSSVR act as new observations of PF to obtain a probability distribution of the RUL. Effectiveness of the proposed method is verified based on PEMFC dataset provided by FCLAB Research Federation. The results confirm that the proposed method has a high accuracy of RUL prediction for PEMFC.

title: Accelerated aging in electrolytic capacitors for prognostics
abstract: The focus of this work is the analysis of different degradation phenomena based on thermal overstress and electrical overstress accelerated aging systems and the use of accelerated aging techniques for prognostics algorithm development. Results on thermal overstress and electrical overstress experiments are presented. In addition, preliminary results toward the development of physics based degradation models are presented focusing on the electrolyte evaporation failure mechanism. An empirical degradation model based on percentage capacitance loss under electrical overstress is presented and used in: (i) a Bayesian based implementation of model based prognostics using a discrete Kalman filter for health state estimation, and (ii) a dynamic system representation of the degradation model for forecasting and remaining useful life (RUL) estimation. A leave one out validation methodology is used to assess the validity of the methodology under the small sample size constrain. The results observed on the RUL estimation are consistent through the validation tests comparing relative accuracy and prediction error. It has been observed that the inaccuracy of the model to represent the change in degradation behavior observed at the end of the test data is consistent throughout the validation tests, indicating the need of a more detailed degradation model or the use of an algorithm that could estimate model parameters on line. Based on the observed degradation process under different stress intensity with rest periods, the need for more sophisticated degradation models is further supported. The current degradation model does not represent the capacitance recovery over rest periods following an accelerated aging stress period.

title: A Data driven RUL Prediction Method Enhanced by Identified Degradation Model for Lithium ion Battery of EVs
abstract: A reliable and high fidelity of the battery remaining useful life (RUL) prediction is dependent on a large amount of high qualified labeled data, which is a difficult and tedious work in practical implementation. To ease the training process and improve the prediction accuracy under limited labeled training data, this paper proposes a data driven RUL prediction method enhanced by the identified mechanism degradation model. With the Frisch Scheme based Bias Compensating Recursive Least Squares (FBCRLS) algorithm, the battery mechanism aging model parameters are identified under limited labeled data. The model is then used to generate labeled data in a full life cycle, which is used to enhance the training of the Support Vector Regression (SVR) model. Test results reveal that the proposed method can effectively reduce the requirement of measured data and ensure high prediction accuracy even under very limited training data.

title: Degradation modeling and RUL prediction using Wiener process subject to multiple change points and unit heterogeneity
abstract: 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.

title: An IMA degradation model with intermittent faults for RUL prediction
abstract: Integrated modular avionics (IMA) is one of the most advanced systems. Its performance greatly impacts on the working condition of aircraft. For enhancing the safety and reliability of aircraft, this paper researches on modeling of IMA degradation. Since the intermittent fault occurs in the degradation of electronic device, it can be considered as one of the characteristics of degradation. The IMA working principle and the intermittent faults (IFs) mechanism are analyzed, firstly. Secondly, a mathematical degradation model under multiple factors coupling action is presented. Then, the simulation model is built and the stochastic point method is adopted to produce the IFs. The simulation repeated 100 times to obtain the raw data. Finally, support vector machine (SVM) are adopted to predict the remaining useful life (RUL) The statistical results of first failure time are utilized to verify the predicted remaining life.