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 is 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.
