Dislocated Time Series Convolutional Neural Architecture: An Intelligent Fault Diagnosis Approach for Electric Machine

• Published in: IEEE transactions on industrial informatics 2017-06, Vol.13 (3), p.1310-1320
• Main Authors: Liu, Ruonan, Meng, Guotao, Yang, Boyuan, Sun, Chuang, Chen, Xuefeng
• Format: Article
• Language: English
• Subjects: Architecture; Classifiers; Computer simulation; Construction equipment; Convolution; Convolutional neural architecture; dislocated time series; Dislocations; electric machine; Electric machines; Fault diagnosis; Feature extraction; Feature recognition; Industrial applications; Informatics; intelligence fault diagnosis; Kernel; Neural networks; nonstationary condition; Object recognition; State of the art; Time series; Vibration; Vibrations
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2016.2645238

In most current intelligent diagnosis methods, fault classifiers of electric machine are built based on complex handcrafted features extractor from raw signals, which depend on prior knowledge and is difficult to implement intelligentization authentically. In addition, the increasingly complicated industrial structures and data make handcrafted features extractors less suited. Convolutional neural network (CNN) provides an efficient method to act on raw signals directly by weight sharing and local connections without feature extractors. However, effective as CNN works on image recognition, it does not work well in industrial applications due to the differences between image and industrial signals. Inspired by the idea of CNN, we develop a novel diagnosis framework based on the characteristics of industrial vibration signals, which is called dislocated time series CNN (DTS-CNN). The DTS-CNN architecture is composed of dislocate layer, convolutional layer, sub-sampling layer and fully connected layer. By adding a dislocate layer, this model can extract the relationship between signals with different intervals in periodic mechanical signals, thereby overcome the weaknesses of traditional CNNs and is more applicable for modern electric machines, especially under nonstationary conditions. Experiments under constant and nonstationary conditions are performed on a machine fault simulator to validate the proposed framework. The results and comparison with respect to the state of the art in the field is illustrated in detail, which highlights the superiority of the proposed method in industrial applications.