Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders.

By Han Liu, Jian-zhong Zhou, Yang Zheng, Wei Jiang, Yuncheng Zhang. ISA transactions, 2018

547 citations recorded by the source corpus.

As the rolling bearings being the key part of rotary machine, its healthy condition is quite important for safety production. Fault diagnosis of rolling bearing has been research focus for the sake of improving the economic efficiency and guaranteeing the operation security. However, the collected signals are mixed with ambient noise during the operation of rotary machine, which brings great challenge to the exact diagnosis results. Using signals collected from multiple sensors can avoid the loss of local information and extract more helpful characteristics. Recurrent Neural Networks (RNN) is a type of artificial neural network which can deal with multiple time sequence data. The capacity of RNN has been proved outstanding for catching time relevance about time sequence data. This paper proposed a novel method for bearing fault diagnosis with RNN in the form of an autoencoder. In this approach, multiple vibration value of the rolling bearings of the next period are predicted from the previous period by means of Gated Recurrent Unit (GRU)-based denoising autoencoder. These GRU-based non-linear predictive denoising autoencoders (GRU-NP-DAEs) are trained with strong generalization ability for each different fault pattern. Then for the given input data, the reconstruction errors between the next period data and the output data generated by different GRU-NP-DAEs are used to detect anomalous conditions and classify fault type. Classic rotating machinery datasets have been employed to testify the effectiveness of the proposed diagnosis method and its preponderance over some state-of-the-art methods. The experiment results indicate that the proposed method achieves satisfactory performance with strong robustness and high classification accuracy.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/b57fb54dd2cf9769053a7afdf50e537b07e5b07e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Qiuhua Miao
2. Yudong Cao
3. Yifei Ding
4. Dhiraj Neupane
5. Jong-Hoon Seok
6. Shen Zhang
7. Minping Jia
8. Bingnan Wang

In Mechanical Engineering

1. Qiuhua Miao
2. Yudong Cao
3. Yifei Ding
4. Dhiraj Neupane
5. Jong-Hoon Seok
6. Shen Zhang
7. Minping Jia
8. Bingnan Wang

Citations in the corpus

• Deep Learning Algorithms for Bearing Fault Diagnosticsx—A Comprehensive Review (2019, background): “Other examples include a mixture of CNN and DBN [175], a deep residual network (DRN) [176], [177], a deep stack network [178], a RNN based auto-encoders [179], sparse filtering [180], etc.”
• A novel time–frequency Transformer based on self–attention mechanism and its application in fault diagnosis of rolling bearings (2021, background): “Liu’s GRU-based non-linear predictive denoising autoencoder (GRU-NP-DAE) [16], Shao’s auto-encoder (AE) [19] and Mao’s auto-encoder extreme learning machine (AE-ELM) [13].”
• Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review (2020, methodology): “Fault diagnosis of rolling bearings with RNN-based auto-encoders is proposed in [129] in which a Gated Recurrent Unit (GRU) based denoising auto-encoder is used to predict the multiple vibration value of REB of the following period are forecasted from the preceding period.”
• Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing (2023, unknown): “Liu et al. [13] presented a recurrent neural network-based autoencoder for bearing fault diagnosis, and used the information from multiple sensors to avoid the loss of local information and improve diagnostic performance.”
• Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review (2019, unknown): “Other examples include a mixture of CNN and DBN [163], a deep residual network (DRN) [164], [165], a deep stack network [166], a RNN based auto-encoders [167], sparse filtering [168], and a ew deep le rning architecture – the capsule network [169], proposed by Hinton et al.”