Mechanical Vibrations Analysis in Direct Drive Using CWT with Complex Morlet Wavelet

Łuczak, Dominik

doi:10.2478/pead-2023-0005

Cited by 14 publications

(10 citation statements)

References 22 publications

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“…The system was preliminary investigated at 100 Hz, 200 Hz, 400 Hz, 500 Hz, and 2000 Hz sampling frequencies and 200 Hz was selected, in which frequency components are rich. In previous research investigations for mechanical vibrations in direct motor drives, up to 10,000 Hz samplings of a, b, c currents with multiple mechanical resonances were conducted [33][34][35]. However, the proof of concept which verifies if the idea is feasible does not require a sufficiently high sampling frequency; therefore, 200 Hz was a wise selection.…”

Section: Discussionmentioning

confidence: 99%

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

Łuczak

2024

Energies

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Accurate and timely fault detection is crucial for ensuring the smooth operation and longevity of rotating machinery. This study explores the effectiveness of image-based approaches for machine fault diagnosis using data from a 6DOF IMU (Inertial Measurement Unit) sensor. Three novel methods are proposed. The IMU6DoF-Time2GrayscaleGrid-CNN method converts the time series sensor data into a single grayscale image, leveraging the efficiency of a grayscale representation and the power of convolutional neural networks (CNNs) for feature extraction. The IMU6DoF-Time2RGBbyType-CNN method utilizes RGB images. The IMU6DoF-Time2RGBbyAxis-CNN method employs an RGB image where each channel corresponds to a specific axis (X, Y, Z) of the sensor data. This axis-aligned representation potentially allows the CNN to learn the relationships between movements along different axes. The performance of all three methods is evaluated through extensive training and testing on a dataset containing various operational states (idle, normal, fault). All methods achieve high accuracy in classifying these states. While the grayscale method offers the fastest training convergence, the RGB-based methods might provide additional insights. The interpretability of the models is also explored using Grad-CAM visualizations. This research demonstrates the potential of image-based approaches with CNNs for robust and interpretable machine fault diagnosis using sensor data.

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Section: Discussionmentioning

confidence: 99%

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

Łuczak

2024

Energies

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“…The decision made by the fault diagnosis system can be taken based on data in the time domain [ 119 , 120 ] or other domains, for example, the frequency domain [ 121 , 122 , 123 , 124 ], the time–frequency domain [ 125 , 126 , 127 , 128 , 129 , 130 , 131 ] or the time-scale domain [ 132 , 133 , 134 , 135 ].…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

Cloud Based Fault Diagnosis by Convolutional Neural Network as Time–Frequency RGB Image Recognition of Industrial Machine Vibration with Internet of Things Connectivity

Łuczak

Brock

Siembab

2023

Sensors

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The human-centric and resilient European industry called Industry 5.0 requires a long lifetime of machines to reduce electronic waste. The appropriate way to handle this problem is to apply a diagnostic system capable of remotely detecting, isolating, and identifying faults. The authors present usage of HTTP/1.1 protocol for batch processing as a fault diagnosis server. Data are sent by microcontroller HTTP client in JSON format to the diagnosis server. Moreover, the MQTT protocol was used for stream (micro batch) processing from microcontroller client to two fault diagnosis clients. The first fault diagnosis MQTT client uses only frequency data for evaluation. The authors’ enhancement to standard fast Fourier transform (FFT) was their usage of sliding discrete Fourier transform (rSDFT, mSDFT, gSDFT, and oSDFT) which allows recursively updating the spectrum based on a new sample in the time domain and previous results in the frequency domain. This approach allows to reduce the computational cost. The second approach of the MQTT client for fault diagnosis uses short-time Fourier transform (STFT) to transform IMU 6 DOF sensor data into six spectrograms that are combined into an RGB image. All three-axis accelerometer and three-axis gyroscope data are used to obtain a time-frequency RGB image. The diagnosis of the machine is performed by a trained convolutional neural network suitable for RGB image recognition. Prediction result is returned as a JSON object with predicted state and probability of each state. For HTTP, the fault diagnosis result is sent in response, and for MQTT, it is send to prediction topic. Both protocols and both proposed approaches are suitable for fault diagnosis based on the mechanical vibration of the rotary machine and were tested in demonstration.

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“…To delve into this question, a time-frequency analysis becomes imperative. Unfortunately, the application of short-time Fourier transform (STFT) to address this problem introduces the blurring of certain frequencies observed in FFT [54]. Recognising this limitation, the author sought alternative tools to achieve effective time and frequency localisation while minimising the blurring of frequency data.…”

Section: Extracting Features In Time-scale (Time-frequency) Domainmentioning

confidence: 99%

“…Continuous wavelet transform (CWT), employed with a meticulously chosen mother wavelet and optimal parameter selection, yields more satisfactory results. In particular, the use of CWT with a complex Morlet wavelet produces superior results compared to STFT, providing a clearer representation of both the time and frequency characteristics in the analysed signal [54].…”

Section: Extracting Features In Time-scale (Time-frequency) Domainmentioning

confidence: 99%

“…In the previous author stage of research on vibration analysis for electric direct drive with CWT [54], the velocity signal was analysed using STFT and CWT with excitation by a linear chirp signal, without additional vibration sensors, and there was no image recognition as a decision-making system. In this stage of research, the author uses an additional sensor inertial measurement unit (IMU) with six axes, and then data are converted into a RGB image by continuous wavelet transform (CWT) and recognised by the CNN.…”

Section: Extracting Features In Time-scale (Time-frequency) Domainmentioning

confidence: 99%

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

Łuczak

2024

Electronics

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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.

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Mechanical Vibrations Analysis in Direct Drive Using CWT with Complex Morlet Wavelet

Cited by 14 publications

References 22 publications

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

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

Cloud Based Fault Diagnosis by Convolutional Neural Network as Time–Frequency RGB Image Recognition of Industrial Machine Vibration with Internet of Things Connectivity

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

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