NDLI: Simultaneous bearing fault diagnosis and severity detection using a LAMSTAR network-based approach

Content Provider	IET Digital Library
Author	He, Miao He, David
Abstract	Bearings are one of the most important components in many industrial machines. Effective bearing fault diagnosis and severity detection are critical for keeping the machines operate normally and safe. In this study, the problem of simultaneous bearing fault diagnosis and severity detection with deep learning is addressed. Existing solutions developed using deep learning rely on fault feature extraction using complicated signal processing techniques. They perform bearing fault diagnosis and severity detection separately and normally require extensive supervised fine tuning. This study presents an effective deep learning-based solution using a large memory storage and retrieval (LAMSTAR) neural network. The developed approach can automatically extract self-learned fault features and perform bearing fault diagnosis and severity detection simultaneously. The structure of the LAMSTAR network is determined by optimally selecting the sliding box size of the input time–frequency matrix. The effectiveness of the proposed approach is validated using data collected from rolling element bearing tests.
Starting Page	893
Ending Page	901
Page Count	9
ISSN	17518822
Volume Number	12
e-ISSN	17518830
Issue Number	Issue 7, Oct (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-smt/12/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-smt.2017.0528
Journal	IET Science, Measurement & Technology
Publisher Date	2018-06-06
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Bearing Fault Diagnosis Bearing Severity Detection Civil And Mechanical Engineering Computing Condition Monitoring Deep Learning Digital Signal Processing Element Bearing Tests Fault Diagnosis Fault Feature Extraction Feature Extraction Input Time–frequency Matrix LAMSTAR Neural Network Large Memory Storage And Retrieval Learning in AI Machine Bearings Maintenance And Reliability Mechanical Component Mechanical Engineering Application of IT Mechanical Engineering Computing Memory Storage Neural Computing Technique Neural Nets Rolling Bearings Self-learned Fault Feature Signal Processing Signal Processing And Detection
Content Type	Text
Resource Type	Article
Subject	Atomic and Molecular Physics, and Optics Electrical and Electronic Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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