Title: Hidden Markov model based rotate vector reducer fault detection using acoustic emissions

Authors: Haibo An; Wei Liang; Yinlong Zhang; Jindong Tan

Addresses: State Key Laboratory of Robotics, Chinese Academy of Sciences, Shenyang, 110016, China; Key Laboratory of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China; University of Chinese Academy of Sciences, Beijing, 100049, China ' State Key Laboratory of Robotics, Chinese Academy of Sciences, Shenyang 110016, China; Key Laboratory of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China ' Shenyang Institute of Automation, Guangzhou, Chinese Academy of Sciences, Guangzhou, 511548, China ' Department of Mechanical, Aerospace and Biomedical Engineering, University of Tennessee, Konxville, TN, 37996, USA

Abstract: This paper proposes a hidden Markov model (HMM) based RV reducer fault detection using acoustic emission (AE) measurements. Compared with the conventional faults from the common rotating machinery (such as bearings and gears), faults from RV reducer are more complicated and undetectable due to its inherent inline and two-stage meshing structure. To this end, this work modifies the HMM model by taking into account not only the current observations and previous states, but the subsequent series of observations within posteriori probability framework. Through this way, the random and unknown disturbance could be suppressed. Besides, HMM is also applied to separate AE signal bulks within one cycle that has 39 subcycles. The proposed method has been evaluated on our collected AE signal dataset from the RV reducer in the industrial robotic platform. The experimental results and analysis validate the effectiveness and accuracy of our RV reducer fault detection model.

Keywords: RV; rotate vector reducer; fault detection; HMM; hidden Markov model; AE; acoustic emission.

DOI: 10.1504/IJSNET.2020.104927

International Journal of Sensor Networks, 2020 Vol.32 No.2, pp.116 - 125

Received: 24 Jul 2019
Accepted: 09 Sep 2019
Published online: 06 Feb 2020 *

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