Review of remaining useful life prediction using support vector machine for engineering assets

The Li-ion battery has high discharge voltage, long cycle life, good safety performance, no memory effect and other advantages. So it has being more and more used and concerned. This paper reviews various aspects of recent research and developments in Li-ion battery prognostics and health monitoring,and summarizes the techniques,algorithms and models used for state-of-charge estimation,voltage estimation,capacity estimation and remaining-useful-life prediction. Especially for state-of-charge estimation, this paper summed up many methods, such as current integration method, open circuit voltage method, Fuzzy logic, Autoregressive moving average model, Electrochemical impedance spectroscopy, Support vector machine and support vector machine based on Extended Kalman filter. And their advantages and disadvantages are summarized.

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Bearing remaining useful life prediction based on optimized support vector regression model with denoising technique

2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS) ◽

10.1109/icps49255.2021.9468258 ◽

2021 ◽

Author(s):

Sheng Cao ◽

Yuchen Jiang ◽

Hao Luo ◽

Yanming Fu ◽

Xianling Li ◽

...

Keyword(s):

Regression Model ◽

Support Vector Regression ◽

Life Prediction ◽

Remaining Useful Life ◽

Support Vector ◽

Support Vector Regression Model ◽

Useful Life

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A Bayesian least-squares support vector machine method for predicting the remaining useful life of a microwave component

Advances in Mechanical Engineering ◽

10.1177/1687814016685963 ◽

2017 ◽

Vol 9 (1) ◽

pp. 168781401668596 ◽

Cited By ~ 9

Author(s):

Fuqiang Sun ◽

Xiaoyang Li ◽

Haitao Liao ◽

Xiankun Zhang

Keyword(s):

Support Vector Machine ◽

Least Squares ◽

Remaining Useful Life ◽

Lifetime Prediction ◽

Support Vector ◽

Machine Method ◽

Support Vector Machine Method ◽

Microwave Component ◽

Useful Life ◽

Bayesian Least Squares

Rapid and accurate lifetime prediction of critical components in a system is important to maintaining the system’s reliable operation. To this end, many lifetime prediction methods have been developed to handle various failure-related data collected in different situations. Among these methods, machine learning and Bayesian updating are the most popular ones. In this article, a Bayesian least-squares support vector machine method that combines least-squares support vector machine with Bayesian inference is developed for predicting the remaining useful life of a microwave component. A degradation model describing the change in the component’s power gain over time is developed, and the point and interval remaining useful life estimates are obtained considering a predefined failure threshold. In our case study, the radial basis function neural network approach is also implemented for comparison purposes. The results indicate that the Bayesian least-squares support vector machine method is more precise and stable in predicting the remaining useful life of this type of components.

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Lithium-ion battery Remaining Useful Life prediction based on grey Support Vector Machines

Safety and Reliability of Complex Engineered Systems ◽

10.1201/b19094-272 ◽

2015 ◽

pp. 2089-2094

Author(s):

J Miao ◽

X Li ◽

J Liu ◽

C Peng

Keyword(s):

Support Vector Machines ◽

Lithium Ion Battery ◽

Life Prediction ◽

Lithium Ion ◽

Remaining Useful Life ◽

Support Vector ◽

Vector Machines ◽

Useful Life

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Battery remaining useful life prediction algorithm based on support vector regression and unscented particle filter

2016 IEEE International Conference on Prognostics and Health Management (ICPHM) ◽

10.1109/icphm.2016.7542844 ◽

2016 ◽

Cited By ~ 2

Author(s):

Xi Peng ◽

Chao Zhang ◽

Yang Yu ◽

Yong Zhou

Keyword(s):

Particle Filter ◽

Support Vector Regression ◽

Life Prediction ◽

Remaining Useful Life ◽

Prediction Algorithm ◽

Support Vector ◽

Unscented Particle Filter ◽

Useful Life

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A Scheme with Acoustic Emission Hit Removal for the Remaining Useful Life Prediction of Concrete Structures

Sensors ◽

10.3390/s21227761 ◽

2021 ◽

Vol 21 (22) ◽

pp. 7761

Author(s):

Tuan-Khai Nguyen ◽

Zahoor Ahmad ◽

Jong-Myon Kim

Keyword(s):

Neural Network ◽

Acoustic Emission ◽

Recurrent Neural Network ◽

Life Prediction ◽

Deep Neural Network ◽

Remaining Useful Life ◽

Support Vector ◽

Health Indicator ◽

Removal Process ◽

Useful Life

In this study, a scheme of remaining useful lifetime (RUL) prognosis from raw acoustic emission (AE) data is presented to predict the concrete structure’s failure before its occurrence, thus possibly prolong its service life and minimizing the risk of accidental damage. The deterioration process is portrayed by the health indicator (HI), which is automatically constructed from raw AE data with a deep neural network pretrained and fine-tuned by a stacked autoencoder deep neural network (SAE-DNN). For the deep neural network structure to perform a more accurate construction of health indicator lines, a hit removal process with a one-class support vector machine (OC-SVM), which has not been investigated in previous studies, is proposed to extract only the hits which matter the most to the portrait of deterioration. The new set of hits is then harnessed as the training labels for the deep neural network. After the completion of the health indicator line construction, health indicators are forwarded to a long short-term memory recurrent neural network (LSTM-RNN) for the training and validation of the remaining useful life prediction, as this structure is capable of capturing the long-term dependencies, even with a limited set of data. Our prediction result shows a significant improvement in comparison with a similar scheme but without the hit removal process and other methods, such as the gated recurrent unit recurrent neural network (GRU-RNN) and the simple recurrent neural network.

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