A Comparative Study of Deep Learning Model Based Equipment Fault Diagnosis and Prognosis

Abstract

Bearing fault diagnosis and prognosis are crucial for the effective management of industrial equipment. Due to the automatic feature extraction of Deep Learning (DL) models, many recent studies have focused on using DL for these tasks. However, most studies address only one of these tasks. This study aims to present DL models and their powerful ML tools capable of both fault diagnosis and prognosis on industrial equipment. To identify the best DL model for both tasks, a comparative study is conducted on various DL models and ML tools, including Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), CNN in parallel with LSTM (CNN-LSTM), Bidirectional LSTM (Bi-LSTM), and transformer models. The ML tools investigated include Recurrent Dropout, Residual Network (ResNet), and Monte Carlo Dropout (MC Dropout). These models are validated using online datasets from Case Western Reserve University (CWRU) and Xi’an Jiao Tong University (XJTU-SY) for the task of fault diagnosis. For fault prognosis, datasets from XJTU-SY and IEEE PHM are used. The results demonstrate the superiority of the ResCNN-LSTM model in both fault diagnosis and prognosis tasks. It achieves prediction accuracy of 99.87% and 96.39% and F1-scores of 0.998 and 0.964 for fault diagnosis on the CWRU and XJTU-SY datasets, respectively. Additionally, it shows a Root Mean Square Error (RMSE) of 8.56 and Mean Absolute Error (MAE) of 12.16 for fault prognosis on the XJTU dataset, and an RMSE of 12.18 using the IEEE PHM bearing dataset. These high performance metrics indicate the model's effectiveness in accurately diagnosing faults and predicting failures.