A DEEP AUTO ENCODER WITH REINFORCEMENT LEARNING FOR ROTATING MACHINERY FAULT DIAGNOSIS

Abstract

Fault diagnosis in rotating machinery plays a crucial role in ensuring operational reliability and safety.Rotating machinery plays a critical role in various industrial applications,but en suring its reliability and safety is of utmost importance.Fault diagnosis in rotating machinery is a vital task that involves identifying and addressing potential issues to prevent catastrophic accidents and enable effective maintenance.Traditional fault diagnosis methods have certain limitations, such as manual analysis and limited accuracy. In recent years,deep learning tech niques have emerged as promising approaches for automating the fault diagnosis process.This study proposes a novel approach for fault diagnosis in rotating machinery by combining deep learning with reinforcement learning.The proposed method leverages a deep auto encoder augmented with reinforcement learning techniques to improve the accuracy and effectiveness of fault diagnosis.The deep auto encoder extracts relevant features by compressing input data into a lower-dimensional representation and reconstructing the original input.This process inherently performs feature extraction, capturing informative characteristics in the encoded layer.Furthermore,reinforcement learning, specifically a deep Q network, is employed to en hance the accuracy of failure mode diagnosis.By continuously interacting with the datasets and learning from the feedback received, the models can improve their diagnostic capabilities and handle compound failures more effectively.The performance of the proposed approach is evaluated using two real-world datasets, namely the CWRU and MAFAULDA datasets, which cover different fault diagnosis and time series analysis scenarios.Various models, in cluding 1D CNN, LSTM, and GRU, are utilized to process the time series data and extract meaningful features.The evaluation metrics used to assess the effectiveness of the trained models include accuracy, precision, recall, and F1 score.Additionally, a confusion matrix and a classification report are generated to provide comprehensive insights into the performance of the models.The results demonstrate that the proposed approach,combining deep learning with reinforcement learning,holds significant potential for accurate fault diagnosis in rotating machinery.