Rolling Element Bearing Fault Diagnosis by the Implementation of Elman Neural Networks With Long Short-Term Memory Strategy

Abstract

Bearing clearance is a common issue in mechanical systems due to unavoidable assembly errors, leading to weak fault features that are challenging to detect. This study introduces a novel diagnostic technique for detecting bearing clearance faults using the Elman neural network (ENN)-based long short-term memory (LSTM). The raw vibration data from an accelerometer are processed using the fast Fourier transform (FFT) to extract frequency-domain features. ENN is employed to identify clearance faults under various operating conditions, while LSTM captures temporal dependencies in the data. This hybrid ENN-LSTM approach eliminates the need for manual feature extraction, reducing the risk of errors associated with expert-driven methods. The proposed method demonstrates robust generalization performance and achieves an average fault identification accuracy of 99.16% across different operating conditions. This research offers valuable insights for improving fault diagnostics in rotor-bearing systems.