Sparse auto-encoder with regularization method for health indicator construction and remaining useful life prediction of rolling bearing

The accurate prediction of the remaining useful life (RUL) of rolling bearings is of great significance for a rational formulation of maintenance strategies and the reduction of maintenance costs. According to the two-stage nonlinear degradation characteristics of rolling bearing operation, this paper proposes a prognosis model based on modified stochastic filtering. First, multiple features reextracted from the time domain, frequency domain, and complexity angles, and the baseline Gaussian mixture model (GMM) is established using the normal operating data after spectral regression. The Bayesian-inferred distance (BID) is used as a quantitative indicator to reflect the bearing performance degradation degree. Then, taking multiparameter fusion results as input, the relationship between BID and remaining life is established by the two-stage stochastic filtering model to realize online dynamic remaining useful life prediction. The method in this paper overcomes the difficulty of accurately defining the failure threshold of rolling bearing. At the same time, it reduces the computational burden, avoiding the need of calculating the joint probability distribution for high-dimensional data. Finally, the proposed method has been verified experimentally to have high precision and engineering application value.

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Rolling bearing remaining useful life prediction via weight tracking relevance vector machine

Measurement Science and Technology ◽

10.1088/1361-6501/abbe3b ◽

2020 ◽

Vol 32 (2) ◽

pp. 024006

Author(s):

Jian Tang ◽

Guanhui Zheng ◽

Dong He ◽

Xiaoxi Ding ◽

Wenbin Huang ◽

...

Keyword(s):

Life Prediction ◽

Rolling Bearing ◽

Relevance Vector Machine ◽

Remaining Useful Life ◽

Useful Life

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A recurrent neural network based health indicator for remaining useful life prediction of bearings

Neurocomputing ◽

10.1016/j.neucom.2017.02.045 ◽

2017 ◽

Vol 240 ◽

pp. 98-109 ◽

Cited By ~ 295

Author(s):

Liang Guo ◽

Naipeng Li ◽

Feng Jia ◽

Yaguo Lei ◽

Jing Lin

Keyword(s):

Neural Network ◽

Recurrent Neural Network ◽

Life Prediction ◽

Remaining Useful Life ◽

Health Indicator ◽

Useful Life

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PCA based health indicator for remaining useful life prediction of wind turbine gearbox

Vibroengineering PROCEDIA ◽

10.21595/vp.2019.21161 ◽

2019 ◽

Vol 29 ◽

pp. 31-36

Author(s):

Sabareesh G R ◽

Hemanth Mithun Praveen ◽

Divya Shah ◽

Krishna Dutt Pandey ◽

Vamsi I

Keyword(s):

Wind Turbine ◽

Life Prediction ◽

Remaining Useful Life ◽

Health Indicator ◽

Wind Turbine Gearbox ◽

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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