A bearing data analysis based on kurtogram and deep learning sequence models. (October 2019)

• Record Type: Journal Article
• Authors: Udmale, Sandeep S.; Singh, Sanjay Kumar; Bhirud, Sunil G.
• Is Part Of: Measurement. Volume 145(2019)
• Page Start: 665
• Page End: 677
• Publication Date: 2019-10
• Subjects: Fault classification -- Kurtogram -- Recurrent neural network (RNN) -- Rolling element bearing (REB) -- Sequence models; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2019.05.039
• Languages: English
• ISSNs: 0263-2241

Abstract

Graphical abstract: Highlights: A kurtogram based deep sequential model is examined for bearing fault classification. The deep learning sequential model has a good ability to analyze the sequential data. Analysing the kurtogram as sequential data helps in improving the performance. Conversion of raw signal to kurtogram helps to generate good feature representations. Abstract: The condition monitoring of rotating machinery has been widely accepted by the industrial system for intelligent fault diagnosis to achieve sustainability, high performance and provide safety to workers. Therefore, in recent years, artificial intelligence (AI) and signal processing (SP) methods are operated collectively for fault diagnosis. The complex and hybrid input feature set are constructed using SP methods for AI-based fault diagnosis. Thus, over the years the numbers of features in the feature space are increasing to represent the various faults as well as fault severities, and also, different feature selection techniques are operated on feature space to determine the ideal features. Consequently, it is a challenging task to design the dominant feature set for distinguishing the type of defects. Also, the requirement of a number of features is changing due to various working conditions of rotating machinery. Therefore, a new intelligent diagnosis method for fault classification build on the kurtogram and sequence models (SM) of deep learning is proposed in this paper. The kurtogram is a comprehensive tool for providing well-defined information about defects by organizing frequency domain kurtosis values in order at each level. Thus the SM analyzes the kurtogram as sequential data for fault diagnosis and hence helps to eliminate the feature selection exercise for identifying the dominant features from feature space. The proposed method has examined using two vibration datasets of bearing. The result demonstrates that the proposed method has a promising performance and achieves decent fault classification accuracy in comparison with other methods.