Mechanical fault diagnosis using Convolutional Neural Networks and Extreme Learning Machine

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.