Fault diagnosis method for worm gearbox using convolutional network and ensemble learning

J. C. Hsiao*, Kumar Shivam, Tai-Yan Kam

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

Abstract

Worm gearboxes are popular across various industrial applications since they offer significant gear ratios in small installation spaces. Despite having multiple advantages, worm gearboxes are subjected to higher friction due to sliding design and are prone to damage and increased transmission error over the period of operation. Delayed diagnosis of worm gearbox degradation can lead to low-quality products and/or unnecessary production line downtime. Using vibration characteristics of worm gearbox, it is possible to determine the fault and transmission error at a given period in time. In this paper, an ensemble machine learning model is trained and deployed to monitor the transmission error of worm gearbox and classify between new, operational and old conditions. 1D CNN (one dimensional convolutional neural network) model is used to automatically extract features in vibration signal of X, Y, and Z axes and predict the relevant state of worm gear. The proposed technique uses ensemble machine learning technique fusion of features extracted by multi-layer 1D CNN for three axes vibration data. The proposed method could achieve 96% accuracy and performs significantly better than traditional sequential and ensemble machine learning models on a dataset of with 7,870 samples with 800 samples labeled as new condition, 4,740 samples as operational and 2,330 as old.

Original languageEnglish
Article number012030
JournalJournal of Physics: Conference Series
Volume1509
Issue number1
DOIs
StatePublished - 6 May 2020
Event10th Asian-Pacific Conference on Aerospace Technology and Science, APCATS 2019 and the 4th Asian Joint Symposium on Aerospace Engineering, AJSAE 2019 - Hsin Chu, Taiwan
Duration: 28 Aug 201931 Aug 2019

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