Approach to Bearing Fault Diagnosis: CNN-Based Classification Across Different Preprocessing Techniquese

This paper presents a rigorous investigation into the efficacy of diverse preprocessing methods for bearing fault classification, leveraging the comprehensive CWRU dataset. Four distinct approaches were explored: raw data analysis, Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), and Continuous Wavelet Transform (CWT). The study introduces a Convolutional Neural Network (CNN) as the underlying algorithm for fault classification. Through extensive experimentation and analysis, we assess the performance of CNN in conjunction with each preprocessing technique. The results provide valuable insights into the strengths and limitations of raw data and frequency-domain representations, highlighting the impact on the accuracy of fault classification in machinery health monitoring applications, which was decided to be the main score in models evaluation. This comparative analysis can not only contribute to the advancement of condition monitoring but also assist practitioners in selecting optimal preprocessing methods for their specific needs.