Mechanical fault diagnosis using Convolutional Neural Networks and Extreme Learning Machine

By Zhuyun Chen, K. Gryllias, Weihua Li. Mechanical systems and signal processing, 2019

321 citations recorded by the source corpus.

Abstract In the era of the so called 4th industrial revolution, the Factory of the Future and the Industrial Internet of Things, the industrial mechanical systems become continuously more intelligent and more complex. Therefore, there is a clear need for research and development on data driven methodologies and condition monitoring techniques which are able to achieve fast, reliable and high-quality diagnosis in an automatic manner. In this paper, a novel fault diagnosis approach integrating Convolutional Neural Networks (CNN) and Extreme Learning Machine (ELM) is proposed, consisting of three main stages. Firstly, the Continuous Wavelet Transform (CWT) is employed in order to obtain pre-processed representations of raw vibration signals. Secondly, a CNN with a square pooling architecture is constructed to extract high-level features. The model does not require extra training and fine-tuning, which can effectively reduce computational cost. Finally, ELM as a strong classifier is further utilized to improve the classification performance on the diagnosis framework. Two datasets, including a gearbox dataset and a motor bearing dataset, have been collected and used to verify the effectiveness of the proposed method. A comprehensive comparison and analysis with widely used algorithms is also performed. The results demonstrate that the proposed method can detect different fault types and outperforms other methods in terms of classification accuracy.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/82c442ec34a68e072ad31d70a9f92002a3e1a399

How researchers see this paper

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

In Artificial Intelligence

1. Zhu Mao
2. Ming Zhao
3. Jing Lin
4. Min Xia
5. Meng Ma
6. Kaixuan Liang
7. Jinyang Jiao
8. Haidong Shao

In Control and Systems Engineering

1. Zhu Mao
2. Ming Zhao
3. Jing Lin
4. Min Xia
5. Lei Shu
6. Clarence W. de Silva
7. Meng Ma
8. Darren Williams

Citations in the corpus

• Deep-Convolution-Based LSTM Network for Remaining Useful Life Prediction (2021, background): “Convolutional operation can learn the meaningful features automatically given the time-domain features [31].”
• Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning (2021, background): “…and accurately establishing nonlinear mapping of different health conditions, popular DL techniques including deep belief network (DBN) [8], stacked auto-encoder (SAE) [9], and convolutional neural network (CNN) [10] have attracted increasing attention and become the very popular tools for IFD.”
• A comprehensive review on convolutional neural network in machine fault diagnosis (2020, methodology): “[86] employed the continuous WT to process raw vibration signals and then designed a convolutional model with a square pooling architecture for feature extraction.”