Hidden Markov model based rotate vector reducer fault detection using acoustic emissions Online publication date: Thu, 06-Feb-2020
by Haibo An; Wei Liang; Yinlong Zhang; Jindong Tan
International Journal of Sensor Networks (IJSNET), Vol. 32, No. 2, 2020
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Sensor Networks (IJSNET):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com