Machine Fault Diagnosis through Vibration Analysis: Continuous Wavelet Transform with Complex Morlet Wavelet and Time–Frequency RGB Image Recognition via Convolutional Neural Network

Abstract

In pursuit of advancing fault diagnosis in electromechanical systems, this research focusses on vibration analysis through innovative techniques. The study unfolds in a structured manner, beginning with an introduction that situates the research question in a broader context, emphasising the critical role of fault diagnosis. Subsequently, the methods section offers a concise summary of the primary techniques employed, highlighting the utilisation of short-time Fourier transform (STFT) and continuous wavelet transform (CWT) for extracting time–frequency components from the signal. The results section succinctly summarises the main findings of the article, showcasing the results of features extraction by CWT and subsequently utilising a convolutional neural network (CNN) for fault diagnosis. The proposed method, named CWTx6-CNN, was compared with the STFTx6-CNN method of the previous stage of the investigation. Visual insights into the time–frequency characteristics of the inertial measurement unit (IMU) data are presented for various operational classes, offering a clear representation of fault-related features. Finally, the conclusion section underscores the advantages of the suggested method, particularly the concentration of single-frequency components for enhanced fault representation. The research demonstrates commendable classification performance, highlighting the efficiency of the suggested approach in real-time scenarios of fault analysis in less than 50 ms. Calculation by CWT with a complex Morlet wavelet of six time–frequency images and combining them into a single colour image took less than 35 ms. In this study, interpretability techniques have been employed to address the imperative need for transparency in intricate neural network models, particularly in the context of the case presented. Notably, techniques such as Grad-CAM (gradient-weighted class activation mapping), occlusion, and LIME (locally interpretable model-agnostic explanation) have proven instrumental in elucidating the inner workings of the model. Through a comparative analysis of the proposed CWTx6-CNN method and the reference STFTx6-CNN method, the application of interpretability techniques, including Grad-CAM, occlusion, and LIME, has played a pivotal role in revealing the distinctive spectral representations of these methodologies.

Machine Fault Diagnosis through Vibration Analysis: Time Series Conversion to Grayscale and RGB Images for Recognition via Convolutional Neural Networks

“…Additionally, Table 2 provides insights into the image generation efficiency of each method, which directly impacts the overall processing time for fault diagnosis. The reference methods (STFTx6-CNN [1] and CWTx6-CNN [31]) achieved perfect validation accuracy (100%), and their training times of several minutes are significantly faster compared to the those of the proposed methods, which had training speeds exceeding 30 min (IMU6DoF-Time2GrayscaleGrid-CNN, IMU6DoF-Time2RGBbyType-CNN, and IMU6DoF-Time2RGBbyAxis-CNN). Additionally, the reference methods achieved over 90% convergence after five iterations, whereas the proposed methods require 60 to 150 iterations for similar accuracy.…”

“…Vibration analysis involves the study of these signals to identify abnormal patterns indicative of faults or anomalies. Traditional methods include Fourier transform-based techniques like Short-Time Fourier Transform (STFT) [1] and Continuous Wavelet Transform (CWT) [31], which provide insights into the frequency content of vibration signals.…”

Convolutional Neural Network with Attention Mechanism and Visual Vibration Signal Analysis for Bearing Fault Diagnosis

“…In this method, short-time Fourier transform (STFT) is applied to transform 6 DOF sensor data into spectrograms that are combined into an RGB image. He also proposed a method which extends the application of CNNs to the recognition of specially crafted time–frequency images [ 23 ]. This method extracts features from the original signal by CWT and subsequently utilizes a CNN for fault diagnosis.…”

Deep Learning-Enhanced Small-Sample Bearing Fault Analysis Using Q-Transform and HOG Image Features in a GRU-XAI Framework

“…Fast Fourier transform (FFT) converts vibration signals into the frequency domain, detecting abnormal frequencies related to bearing faults [5]. Wavelet transform (WT) analyzes signals in both time and frequency domains, offering superior time-frequency resolution and detecting transient fault signals [6]. Ensemble Empirical Mode Decomposition (EEMD) decomposes bearing signals into different time scales to extract relevant information, addressing issues like mode mixing and spectral leakage present in conventional EMD [7].…”