Fault Diagnosis of Induction Motors with Imbalanced Data Using Deep Convolutional Generative Adversarial Network

Abstract

A homemade defective model of an induction motor was created by the laboratory team to acquire the vibration acceleration signals of five operating states of an induction motor under different loads. Two major learning models, namely a deep convolutional generative adversarial network (DCGAN) and a convolutional neural network, were applied for fault diagnosis of the induction motor to the problem of an imbalanced training dataset. Two datasets were studied and analyzed: a sufficient and balanced training dataset and insufficient and imbalanced training data. When the training datasets were adequate and balanced, time–frequency analysis was advantageous for fault diagnosis at different loads, with the diagnostic accuracy achieving 95.06% and 96.38%. For the insufficient and imbalanced training dataset, regardless of the signal preprocessing method, the more imbalanced the training dataset, the lower the diagnostic accuracy was for the testing dataset. Samples generated by DCGAN were found to exhibit 80% similarity with the actual data through comparison. By oversampling the imbalanced dataset, DCGAN achieved a 90% diagnostic accuracy, close to the accuracy achieved using a balanced dataset. Among all oversampling techniques, the pro-balanced method yielded the optimal result. The diagnostic accuracy reached 85% in the cross-load test, indicating that the generated data had successfully learned the different fault features that validate the DCGAN’s ability to learn parts of input signals.

Fault Detection in Induction Motor Using Time Domain and Spectral Imaging-Based Transfer Learning Approach on Vibration Data

“…They also discussed difficulties for future work on emerging technologies for research activities. Chang, H.-C et al [ 26 ] showed the usage and advantages of DCGAN in generating the dataset for fault diagnosis to oversample the Imbalanced data. Their results showed promising results when dealing with imbalanced data and using DCGAN and CNN on time-frequency features for fault severity classification tasks.…”

A Novel Mechanical Fault Diagnosis Based on Transfer Learning with Probability Confidence Convolutional Neural Network Model

“…Testing with different operating conditions of equipment such as speed, load, environmental noise, and fault location, can result in uneven data distribution and unbalanced sampling [13]. The method of generative adversarial network (GAN) generates data deriving from learning different failure characteristics to expand the training data and solve the data imbalance problem [14]. However, most diagnostic models proposed so far are based on supervised learning that identifies labels [15], and identifying different types of fault data is more challenging.…”