An edge-filter FBG interrogation approach based on tunable Fabry-Perot filter for strain measurement of planetary gearbox

Zhang, Xiaodong; Niu, Hang; Chao-zhen, Hou; Di, Fagui

doi:10.1016/j.yofte.2020.102379

Cited by 19 publications

(6 citation statements)

References 23 publications

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“…The results of visualization using raw data from the second DUT are presented in Figure 15(a). By applying the TVF-EMD and window filter, the coordinates of the crack were clearly observed around (20,30), as demonstrated in Figure 15(b), which matched the actual location of the crack as shown in Figure 15(c). Moreover, the visualization indicates that the crack is square shaped, with a length and width of approximately 2 cm, in agreement with the actual size and shape of the crack.…”

Section: Resultsmentioning

confidence: 56%

“…It was observed that it was difficult to locate the crack and identify its shape from the raw data without the application of the TVF-EMD and window filter. By utilizing TVF-EMD to extract the S0 mode and applying a window filter to eliminate direct wave and boundary reflections, a crack became apparent in the visualization near the coordinates (30,20), as illustrated in Figure 14(b). The result of visualization was found to match the actual location of the cracks, as depicted in Figure 14(c).…”

Section: Resultsmentioning

confidence: 99%

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All-fiber photoacoustic system for large-area nondestructive testing

Wu,

Fu,

et al. 2023

Structural Health Monitoring

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Nondestructive testing (NDT) is of paramount importance in ensuring the safe operation of equipment. Among various NDT techniques, ultrasonic NDT has garnered widespread attention due to its high sensitivity, fast speed, and accurate defect location. Photoacoustic NDT, a burgeoning field in ultrasonic NDT, is particularly attractive due to its immunity to electromagnetic interference. However, existing photoacoustic NDT systems suffer from inadequate excitation intensity and complex ultrasonic signal characteristics, impeding large-area NDT and accurate crack visualization. In this study, we present an all-fiber photoacoustic system for large-area NDT. To address the issues, we have developed a photoacoustic generator unit that can be optimized and controlled to generate stronger ultrasonic signals. Furthermore, we have employed mode decomposition to simplify the detected ultrasonic signals by mitigating the acoustic impedance mismatch-induced mode mixing problem in the system. As a result, the technology allows for large-area crack monitoring of up to 50*50 cm2 with an improved resolution of 1 mm. The present technology paves the way for high-resolution equipment crack monitoring with substantially enhanced accuracy in various environments.

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Section: Resultsmentioning

confidence: 56%

Section: Resultsmentioning

confidence: 99%

All-fiber photoacoustic system for large-area nondestructive testing

Wu,

Fu,

et al. 2023

Structural Health Monitoring

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“…It can be an additional sensor only for fault diagnosis or a sensor already present in the system used by the control algorithms. The electromechanical machine or power system can be investigated by many different sensors and signals: current [ 71 , 72 ] and voltage [ 73 , 74 ], torque [ 75 , 76 ], angular velocity/position [ 77 , 78 ], linear 3-axis acceleration/speed/position [ 16 , 17 ], Doppler laser vibrometer [ 79 ], transmission coefficient and reflexion coefficient of omnidirectional antenna [ 80 ], strain/tension [ 81 , 82 , 83 , 84 ], power consumption [ 85 , 86 , 87 , 88 ], internal/external temperature at selected points [ 89 , 90 ] or surface temperature by thermal camera [ 91 , 92 ], depending on frequency range: displacement [ 93 ], vibrations [ 15 , 18 , 94 , 95 , 96 ], sound [ 97 , 98 , 99 ], sound from several microphones [ 100 ] or ultrasound [ 101 , 102 ], vibro-acoustic [ 103 ], chemical analysis of lubrication [ 104 , 105 ], chemical analysis by spectral imaging [ 106 , 107 , 108 , 109 ], camera imaging in human colour spectrum [ 110 , 111 , 112 , <...…”

Section: General Structure Of Fault Diagnosis and Perspective Mainten...mentioning

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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“…The sensor depicted in Figure 1 can take various forms, serving as a dedicated sensor designed solely for fault diagnosis or as an integral part of the system, utilised by control algorithms. Investigating electromechanical machines, rolling bearing [5,7,8] or power systems can employ various sensors and signals, including measurements of current [9,10] and voltage [11,12], torque [13,14], angular velocity/position [15,16], linear 3DOF acceleration/velocity/position [3,5], a laser Doppler vibrometer [17], the transmittance and reflectance of an omnidirectional antenna [18], strain/force [19][20][21][22], energy consumption [23][24][25][26], inner/outer temperature at specific locations [27,28], or outer-part temperature captured by an infrared camera [2,29]. The selection of sensors and signals is dependent on the frequency range and specific characteristics of the electromechanical system under examination.…”

Section: Introductionmentioning

confidence: 99%

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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An edge-filter FBG interrogation approach based on tunable Fabry-Perot filter for strain measurement of planetary gearbox

Cited by 19 publications

References 23 publications

All-fiber photoacoustic system for large-area nondestructive testing

All-fiber photoacoustic system for large-area nondestructive testing

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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