scholarly journals Remaining Useful Life Prediction of Rolling Bearings Using PSR, JADE, and Extreme Learning Machine

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Yongbin Liu ◽  
Bing He ◽  
Fang Liu ◽  
Siliang Lu ◽  
Yilei Zhao ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Phase Space ◽  
Extreme Learning Machine ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Phase Point ◽  
Rolling Bearings ◽  
Point Distribution ◽  
Useful Life ◽  
Learning Machine

Rolling bearings play a pivotal role in rotating machinery. The degradation assessment and remaining useful life (RUL) prediction of bearings are critical to condition-based maintenance. However, sensitive feature extraction still remains a formidable challenge. In this paper, a novel feature extraction method is introduced to obtain the sensitive features through phase space reconstitution (PSR) and joint with approximate diagonalization of Eigen-matrices (JADE). Firstly, the original features are extracted from bearing vibration signals in time and frequency domain. Secondly, the PSR is applied to embed the original features into high dimensional phase space. The between-class and within-class scatter (SS) are calculated to evaluate the feature sensitivity through the phase point distribution of different degradation stages and then different weights are assigned to the corresponding features based on the calculatedSS. Thirdly, the JADE is employed to fuse the weighted features to obtain the advanced features which can better reflect the bearing degradation process. Finally, the advanced features are input into the extreme learning machine (ELM) to train the RUL prediction model. A set of experimental case studies are carried out to verify the effectiveness of the proposed method. The results show that the extracted advanced features can better reflect the degradation process compared to traditional features and could effectively predict the RUL of bearing.

Remaining Useful Life Prediction of Lithium-ion Batteries Using Multiple Kernel Extreme Learning Machine

2020 ◽  
Vol 13 ◽  
Author(s):  
Renxiong Liu
Keyword(s):  
Lithium Ion Batteries ◽  
Extreme Learning Machine ◽  
Kernel Function ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Prediction Methods ◽  
Multiple Kernel ◽  
Kernel Extreme Learning Machine ◽  
Useful Life ◽  
Learning Machine

Objective: Lithium-ion batteries are important components used in electric automobiles (EVs), fuel cell EVs and other hybrid EVs. Therefore, it is greatly important to discover its remaining useful life (RUL). Methods: In this paper, a battery RUL prediction approach using multiple kernel extreme learning machine (MKELM) is presented. The MKELM’s kernel keeps diversified by consisting multiple kernel functions including Gaussian kernel function, Polynomial kernel function and Sigmoid kernel function, and every kernel function’s weight and parameter are optimized through differential evolution (DE) algorithm. Results : Battery capacity data measured from NASA Ames Prognostics Center are used to demonstrate the prediction procedure of the proposed approach, and the MKELM is compared with other commonly used prediction methods in terms of absolute error, relative accuracy and mean square error. Conclusion: The prediction results prove that the MKELM approach can accurately predict the battery RUL. Furthermore, a compare experiment is executed to validate that the MKELM method is better than other prediction methods in terms of prediction accuracy.

scholarly journals Predicting Remaining Useful Life of Rolling Bearings Based on Reliable Degradation Indicator and Temporal Convolution Network with the Quantile Regression

2021 ◽  
Vol 11 (11) ◽  
pp. 4773
Author(s):  
Qiaoping Tian ◽  
Honglei Wang
Keyword(s):  
Quantile Regression ◽  
Probability Density ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Health State ◽  
Rolling Bearings ◽  
Data Set ◽  
Time Frequency ◽  
Useful Life ◽  
Degradation Indicator

High precision and multi information prediction results of bearing remaining useful life (RUL) can effectively describe the uncertainty of bearing health state and operation state. Aiming at the problem of feature efficient extraction and RUL prediction during rolling bearings operation degradation process, through data reduction and key features mining analysis, a new feature vector based on time-frequency domain joint feature is found to describe the bearings degradation process more comprehensively. In order to keep the effective information without increasing the scale of neural network, a joint feature compression calculation method based on redefined degradation indicator (DI) was proposed to determine the input data set. By combining the temporal convolution network with the quantile regression (TCNQR) algorithm, the probability density forecasting at any time is achieved based on kernel density estimation (KDE) for the conditional distribution of predicted values. The experimental results show that the proposed method can obtain the point prediction results with smaller errors. Compared with the existing quantile regression of long short-term memory network(LSTMQR), the proposed method can construct more accurate prediction interval and probability density curve, which can effectively quantify the uncertainty of bearing running state.

A Clustering-Based Framework for Performance Degradation Prediction of Slewing Bearing Using Multiple Physical Signals

2019 ◽  
Vol 5 (2) ◽  
Author(s):  
Peng Ding ◽  
Hua Wang ◽  
Yongfen Dai
Keyword(s):  
Degradation Process ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Self Organizing Map ◽  
Rolling Bearings ◽  
Heavy Load ◽  
Joint Application ◽  
Learning Modes ◽  
Useful Life ◽  
Slewing Bearings

Diagnosing the failure or predicting the performance state of low-speed and heavy-load slewing bearings is a practical and effective method to reduce unexpected stoppage or optimize the maintenances. Many literatures focus on the performance prediction of small rolling bearings, while studies on slewing bearings' health evaluation are very rare. Among these rare studies, supervised or unsupervised data-driven models are often used alone, few researchers devote to remaining useful life (RUL) prediction using the joint application of two learning modes which could fully take diversity and complexity of slewing bearings' degradation and damage into consideration. Therefore, this paper proposes a clustering-based framework with aids of supervised models and multiple physical signals. Correlation analysis and principle component analysis (PCA)-based multiple sensitive features in time-domain are used to establish the performance recession indicators (PRIs) of torque, temperature, and vibration. Subsequently, these three indicators are divided into several parts representing different degradation periods via optimized self-organizing map (OSOM). Finally, corresponding data-driven life models of these degradation periods are generated. Experimental results indicate that multiple physical signals can effectively describe the degradation process. The proposed clustering-based framework is provided with a more accurate prediction of slewing bearings' RUL and well reflects the performance recession periods.

scholarly journals Aircraft Engines Remaining Useful Life Prediction with an Improved Online Sequential Extreme Learning Machine

2020 ◽  
Vol 10 (3) ◽  
pp. 1062 ◽  
Author(s):  
Tarek Berghout ◽  
Leïla-Hayet Mouss ◽  
Ouahab Kadri ◽  
Lotfi Saïdi ◽  
Mohamed Benbouzid
Keyword(s):  
Extreme Learning Machine ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Selection Strategy ◽  
Mapping Technique ◽  
Model Parameters ◽  
Aircraft Engines ◽  
Useful Life ◽  
Learning Machine

The efficient data investigation for fast and accurate remaining useful life prediction of aircraft engines can be considered as a very important task for maintenance operations. In this context, the key issue is how an appropriate investigation can be conducted for the extraction of important information from data-driven sequences in high dimensional space in order to guarantee a reliable conclusion. In this paper, a new data-driven learning scheme based on an online sequential extreme learning machine algorithm is proposed for remaining useful life prediction. Firstly, a new feature mapping technique based on stacked autoencoders is proposed to enhance features representations through an accurate reconstruction. In addition, to attempt into addressing dynamic programming based on environmental feedback, a new dynamic forgetting function based on the temporal difference of recursive learning is introduced to enhance dynamic tracking ability of newly coming data. Moreover, a new updated selection strategy was developed in order to discard the unwanted data sequences and to ensure the convergence of the training model parameters to their appropriate values. The proposed approach is validated on the C-MAPSS dataset where experimental results confirm that it yields satisfactory accuracy and efficiency of the prediction model compared to other existing methods.

Residual life prediction for ball bearings based on joint approximate diagonalization of eigen matrices and extreme learning machine

2015 ◽  
Vol 231 (9) ◽  
pp. 1699-1711 ◽  
Author(s):  
Fang Liu ◽  
Yongbin Liu ◽  
Fenglin Chen ◽  
Bing He
Keyword(s):  
Frequency Domain ◽  
Extreme Learning Machine ◽  
Life Prediction ◽  
Residual Life ◽  
Remaining Useful Life ◽  
Time Frequency ◽  
Fast Learning ◽  
Useful Life ◽  
Learning Machine ◽  
Degradation Indicator

Data-driven approaches have been proved effective for remaining useful life estimation of key components (bearings for example) in rotating machinery. In such approaches, it is important to determine an appropriate degradation indicator from the collected run-to-failure life cycle data. In this paper, a new degradation indicator is introduced based on the joint approximate diagonalization of eigen matrices algorithm. First, a matrix consisting of time domain, frequency domain, and time–frequency domain features extracted from the collected data instances is created. Then a two-layer joint approximate diagonalization of eigen matrices is introduced to transform the matrix to the advanced features (a vector) that represents the behavior of the bearing’s degradation. As an independent component analysis method, the designed two-layer joint approximate diagonalization of eigen matrices is able to eliminate the redundancy of the directly extracted features. Further, the obtained vector is input into an extreme learning machine to train a remaining useful life prediction model. Finally, a set of experimental cases are utilized to verify the presented method. Results show that the two-layer joint approximate diagonalization of eigen matrices is capable of exploring features that reflects the trend of bearing’s degradation state much better. And due to the easy parameter configuration and fast learning speed, the extreme learning machine is capable of training a model that can effectively predict the remaining useful life of the bearings.

Tool wear condition monitoring based on wavelet transform and improved extreme learning machine

2019 ◽  
Vol 234 (5) ◽  
pp. 1057-1068 ◽  
Author(s):  
Soufiane Laddada ◽  
Med. Ouali Si-Chaib ◽  
Tarak Benkedjouh ◽  
Redouane Drai
Keyword(s):  
Wavelet Transform ◽  
Tool Wear ◽  
Extreme Learning Machine ◽  
Cutting Tools ◽  
Remaining Useful Life ◽  
Machining Process ◽  
Non Linear ◽  
Useful Life ◽  
Learning Machine ◽  
Wear Condition

In machining process, tool wear is an inevitable consequence which progresses rapidly leading to a catastrophic failure of the system and accidents. Moreover, machinery failure has become more costly and has undesirable consequences on the availability and the productivity. Consequently, developing a robust approach for monitoring tool wear condition is needed to get accurate product dimensions with high quality surface and reduced stopping time of machines. Prognostics and health management has become one of the most challenging aspects for monitoring the wear condition of cutting tools. This study focuses on the evaluation of the current health condition of cutting tools and the prediction of its remaining useful life. Indeed, the proposed method consists of the integration of complex continuous wavelet transform (CCWT) and improved extreme learning machine (IELM). In the proposed IELM, the hidden layer output matrix is given by inverting the Moore–Penrose generalized inverse. After the decomposition of the acoustic emission signals using CCWT, the nodes energy of coefficients have been taken as relevant features which are then used as inputs in IELM. The principal idea is that a non-linear regression in a feature space of high dimension is involved by the extreme learning machine to map the input data via a non-linear function for generating the degradation model. Then, the health indicator is obtained through the exploitation of the derived model which is in turn used to estimate the remaining useful life. The method was carried out on data of the real world collected during various cuts of a computer numerical controlled tool.

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