Unsupervised fault diagnosis method for mechanical equipment based on adversarial flow model

Abstract

The present invention provides an unsupervised fault diagnosis method for mechanical equipment based on an adversarial flow model, main steps including: data preprocessing, converting a mechanical vibration signal into a frequency domain signal, and normalizing the amplitude value of the signal into a range of [0, 1]; prior distribution designing: designing a mixture of Gaussian distribution with K subdistributions, wherein K is determined by the number of mechanical equipment status; model construction: constructing an unsupervised fault diagnosis model by combining an autoencoder, a flow model, and a classifier; model training: training the unsupervised fault diagnosis model by using various classes of status data, along with the designed prior distribution, preset training steps, loss functions, and an optimization algorithm; and fault diagnosis: inputting status data of mechanical equipment into the trained unsupervised fault diagnosis model to obtain a data clustering result and a fault diagnosis result.