A Novel Deep Learning Model for Mechanical Rotating Parts Fault Diagnosis Based on Optimal Transport and Generative Adversarial Networks

Abstract

To solve the poor real-time performance of the existing fault diagnosis algorithms on transmission system rotating components, this paper proposes a novel high-dimensional OT-Caps (Optimal Transport–Capsule Network) model. Based on the traditional capsule network algorithm, an auxiliary loss is introduced during the offline training process to improve the network architecture. Simultaneously, an optimal transport theory and a generative adversarial network are introduced into the auxiliary loss, which accurately depicts the error distribution of the fault characteristic. The proposed model solves the low real-time performance of the capsule network algorithm due to complex architecture, long calculation time, and oversized hardware resource consumption. Meanwhile, it ensures the high precision, early prediction, and transfer aptitude of fault diagnosis. Finally, the model’s effectiveness is verified by the public data sets and the actual faults data of the transmission system, which provide technical support for the application.

A Review of Real-Time Fault Diagnosis Methods for Industrial Smart Manufacturing

“…Representation evaluation block (REB) is proposed to evaluate and reduce the feature difference between the teacher and student networks. Wang et al [102] proposed a high real-time Optimal Transport-Capsule Network (OT-Caps) fault diagnosis model. The model expands the one-dimensional neuron in the traditional CNN into the multidimensional neuron according to the characteristics of the capsule network, which enhances the data mining capability and fault feature storage capability of the deep network.…”

Predictive maintenance of electromechanical systems based on enhanced generative adversarial neural network with convolutional neural network

“…The G and D network is not necessary to be directly invertible and must be differentiable. The G network is an analysis of some description space, denoted a (latent space), to the space of the data [13]. Basically, in the GAN model, the D network may be similarly described as a function that maps from data to an eventuality that the data is from the real data allocation, rather than the G allocation: D: (Dx) (0 or 1).…”

A Fault Diagnosis Approach for Electromechanical Actuators with Simulating Model under Small Experimental Data Sample Condition

“…Theoretically, fault diagnosis aims at detecting and identifying any type of potential abnormalities and faults [10,11]. Numerous artificial intelligence techniques and statistical learning methods have been widely used in fault diagnosis, such as k-nearest neighbor (k-NN) algorithms [12], Bayesian classifier [13], support vector machine (SVM) [14], and deep learning approaches [15]. In recent years, deep-learning algorithms have underpinned the state-of-the-art implementations for fault diagnosis tasks [16,17].…”