Mode-Decoupling Auto-Encoder for Machinery Fault Diagnosis Under Unknown Working Conditions

Rotating machinery often runs under variable working conditions, which results that the working condition of testing samples is unknown for the diagnosis model. The performance of the existed diagnosis methods trained by the samples under the known working condition will be deteriorated when they are used to diagnose the machine under an unknown working condition. The core for solving this issue is to eliminate the influence of working conditions. Inspired by this idea, a mode-decoupling autoencoder (MDAE) with two autoencoders, namely, fault-related mode (FRM) autoencoder and working condition mode (WCM) autoencoder is proposed for machinery fault diagnosis under unknown working conditions. An optimization object with reconstruction loss term, elimination loss term and classification loss term, is custom-tailored for the MDAE to ensure that the FRM autoencoder extracts the FRM and eliminates the WCM as best it can. As a result, the embedding feature extracted by the FRM autoencoder can be directly input into the classifier for the machinery fault diagnosis under unknown working conditions. Experimental results validate the superiority of MDAE in machinery fault diagnosis under unknown working conditions. Moreover, a detailed discussion is performed on the effects of model setting and the interpretability of mode decoupling of MDAE, that is, the stability of MDAE is well at a certain range and the merit of MDAE is given that the WCM autoencoder can drive the trained FRM autoencoder to eliminate the WCM guided by the knowledge of the normal samples.