Bearing Fault Diagnosis Using Lightweight and Robust One-Dimensional Convolution Neural Network in the Frequency Domain

Abstract

The massive environmental noise interference and insufficient effective sample degradation data of the intelligent fault diagnosis performance methods pose an extremely concerning issue. Realising the challenge of developing a facile and straightforward model that resolves these problems, this study proposed the One-Dimensional Convolutional Neural Network (1D-CNN) based on frequency-domain signal processing. The Fast Fourier Transform (FFT) analysis is initially utilised to transform the signals from the time domain to the frequency domain; the data was represented using a phasor notation, which separates magnitude and phase and then fed to the 1D-CNN. Subsequently, the model is trained with White Gaussian Noise (WGN) to improve its robustness and resilience to noise. Based on the findings, the proposed model successfully achieved 100% classification accuracy from clean signals and simultaneously achieved considerable robustness to noise and exceptional domain adaptation ability. The diagnosis accuracy retained up to 97.37%, which was higher than the accuracy of the CNN without training under noisy conditions at only 43.75%. Furthermore, the model achieved an accuracy of up to 98.1% under different working conditions, which was superior to other reported models. In addition, the proposed model outperformed the state-of-art methods as the Signal-to-Noise Ratio (SNR) was lowered to −10 dB achieving 97.37% accuracy. In short, the proposed 1D-CNN model is a promising effective rolling bearing fault diagnosis.

Physics-Informed Time-Frequency Fusion Network With Attention for Noise-Robust Bearing Fault Diagnosis

“…A composite of 1D CNN and LSTM model is proposed in MCNN-LSTM [19] that extracted high-frequency and low-frequency components of the vibration signal with multi-scale kernels. Another 1D CNN used both the frequency spectrum and the phase information of bearing vibration signals for noise robustness [36]. However, the aforementioned studies were designed for either time or frequency domain input only, not for multi-domain signals; such signals can effectively enhance the robustness against noise, as we also demonstrate in the present study.…”

Multi-dimensional CNN Based Feature Extraction with Feature Fusion and SVM for Human Activity Recognition in Surveillance Videos

“…In the proposed approach, automatic feature extraction can be accomplished through CNN. The feature extractions obtained through 3D-CNN (34) , 2D-CNN (35) , and 1D-CNN (36) have been utilized in recent research in which satisfactory performance was achieved. The extracted features from these algorithms are differentiated from each other.…”

A comprehensive review of mechanical fault diagnosis methods based on convolutional neural network

“…At the same time, the validity of the method in signal processing applications is verified, and it can solve the problem of fault diagnosis with a small sample. Hakim M. et al [87] proposed a rolling bearing fault diagnosis method based on a one-dimensional CNN with multi-source domain transfer learning to solve the problem of the dependence of mechanical equipment fault diagnosis on complete data and the scarcity of actual malfunction data. The results show that the classification accuracy of the proposed method is significantly higher than that of the traditional fault diagnosis methods in the case of sparse fault data, and it has a faster convergence speed and better stability.…”