NDLI: Intelligent fault diagnosis method for rotating machinery based on vibration signal analysis and hybrid multi-object deep CNN

Content Provider	IET Digital Library
Author	Xin, Yu Li, Shunming Wang, Jinrui An, Zenghui Zhang, Wei
Abstract	Traditional labour-intensive methods always suffer from time and frequency blurring and cross-term interference when detecting fault features from vibration signals and their time–frequency representation. Such methods need essential expertise and prior knowledge to identify features. Intelligent fault diagnosis methods based on deep learning have attracted great attention because of their ability to automatically learn the representative features in fault diagnosis. Therefore, a novel multi-object deep convolutional neural network (CNN) is proposed to diagnose the faults. In this method, time domain, frequency domain, and time–frequency data are organised as the hybrid multi-object input to avoid the shortcoming of the single type of data, which cannot reveal abundant information about amplitude and frequency in vibration signals. Deep CNN with four convolutional layers is constructed to intelligently learn the distinguished features. The adjacent convolutional layer is used to sensitively capture slight changes in the input data, and the other layers are used to extract and reduce the abundant features. Random forest is used to accurately classify the fault types. The vibration signal of cylinder rolling bearing and gearbox is used to validate the proposed method. The results are compared with those of other state-of-the-art methods to show the superior performance of the proposed method.
Starting Page	407
Ending Page	415
Page Count	9
ISSN	17518822
Volume Number	14
e-ISSN	17518830
Issue Number	Issue 4, Jun (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-smt/14/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-smt.2018.5672
Journal	IET Science, Measurement & Technology
Publisher Date	2020-01-17
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Civil And Mechanical Engineering Computing Convolutional Neural Nets Cross-term Interference Cylinder Rolling Bearing Deep Learning Digital Signal Processing Fault Diagnosis Feature Extraction Frequency Blurring Hybrid Multiobject Deep CNN Inspection And Quality Control Intelligent Fault Diagnosis Method Labour-intensive Method Learning in AI Mathematical Analysis Mechanical Component Mechanical Engineering Mechanical Engineering Application of IT Multiobject Deep Convolutional Neural Network Neural Computing Technique Rolling Bearings Signal Processing And Detection Statistics Time-frequency Analysis Time–frequency Data Time–frequency Representation Vibration Signal Analysis Vibrational Signal Processing Vibrations And Shock Wave
Content Type	Text
Resource Type	Article
Subject	Atomic and Molecular Physics, and Optics Electrical and Electronic Engineering

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