Open AccessGear fault monitoring based on unsupervised feature dimensional reduction and optimized LSSVM-BSOA machine learning model
Author(s) -
Vu Quoc Huy Nguyen,
Van Thuan Pham
Publication year - 2022
Publication title -
journal of mechanical engineering and sciences
Language(s) - English
Resource type - Journals
eISSN - 2231-8380
pISSN - 2289-4659
DOI - 10.15282/jmes.16.1.2022.01.0684
Subject(s) - bottleneck , support vector machine , pattern recognition (psychology) , classifier (uml) , artificial intelligence , vibration , feature vector , computer science , fault (geology) , feature extraction , engineering , data mining , machine learning , physics , quantum mechanics , seismology , embedded system , geology
In the trend of Industry 4.0 development, the big data of system operation is significant for analyzing, predicting, or identifying any possible problem. This study proposes a new diagnosis technique for identifying the vibration signal, which combines the feature dimensional reduction method and optimized classifier. Firstly, an auto-encoder feature dimensional reduction (AE-FDR) method is constructed with the bottleneck hidden layer to extract the low-dimensional feature. Secondly, a supervised classifier is formed to carry out fine-turning and classification. The least square-support vector machine (LSSVM) classifier is used as basic with an optimized parameter exploited by the backtracking search optimisation algorithm (BSOA). This LSSVM-BSOA is used to identify the gear fault based on the original vibration data. The proposed AE-FDR-LSSVM-BSOA diagnosis technique shows good ability for identifying the gear fault. A helical gear is experimented with three fault status for evaluate this method. The diagnosis result achieves a high accuracy of 93.3%.