Gear fault monitoring based on unsupervised feature dimensional reduction and optimized LSSVM-BSOA machine learning model
Keywords:Gear fault monitoring, Feature dimensional reduction, Auto-encoder, LSSVM-BSOA classification model
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%.
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