Causal Disentanglement Hidden Markov Model for Fault Diagnosis
- URL: http://arxiv.org/abs/2308.03027v1
- Date: Sun, 6 Aug 2023 05:58:45 GMT
- Title: Causal Disentanglement Hidden Markov Model for Fault Diagnosis
- Authors: Rihao Chang, Yongtao Ma, Weizhi Nie, Jie Nie, An-an Liu
- Abstract summary: We propose a Causal Disentanglement Hidden Markov model (CDHM) to learn the causality in the bearing fault mechanism.
Specifically, we make full use of the time-series data and progressively disentangle the vibration signal into fault-relevant and fault-irrelevant factors.
To expand the scope of the application, we adopt unsupervised domain adaptation to transfer the learned disentangled representations to other working environments.
- Score: 55.90917958154425
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In modern industries, fault diagnosis has been widely applied with the goal
of realizing predictive maintenance. The key issue for the fault diagnosis
system is to extract representative characteristics of the fault signal and
then accurately predict the fault type. In this paper, we propose a Causal
Disentanglement Hidden Markov model (CDHM) to learn the causality in the
bearing fault mechanism and thus, capture their characteristics to achieve a
more robust representation. Specifically, we make full use of the time-series
data and progressively disentangle the vibration signal into fault-relevant and
fault-irrelevant factors. The ELBO is reformulated to optimize the learning of
the causal disentanglement Markov model. Moreover, to expand the scope of the
application, we adopt unsupervised domain adaptation to transfer the learned
disentangled representations to other working environments. Experiments were
conducted on the CWRU dataset and IMS dataset. Relevant results validate the
superiority of the proposed method.
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