Real-time Faults Prediction by Deep Learning with Multi-sensor Measurements over IoT Networks
The advancements in sensing, data processing, and communication technology have enabled machine learning systems to make confident decisions and made it possible to optimise the operations and maintenance of the physical assets, manufacturing systems and processes of prediction and prevention. In ma...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IFSA Publishing, S.L.
2021-04-01
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| Series: | Sensors & Transducers |
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| Online Access: | https://sensorsportal.com/HTML/DIGEST/april_2021/Vol_251/P_3219.pdf |
| _version_ | 1797751317456748544 |
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| author | G. Y. Luo Y. Q. Luo H. G. Gan |
| author_facet | G. Y. Luo Y. Q. Luo H. G. Gan |
| author_sort | G. Y. Luo |
| collection | DOAJ |
| description | The advancements in sensing, data processing, and communication technology have enabled machine learning systems to make confident decisions and made it possible to optimise the operations and maintenance of the physical assets, manufacturing systems and processes of prediction and prevention. In many industrial applications, it is critical to use deep neural networks that make predictions both fast and accurate, and can be applied to coupled multiple-input multiple-output (MIMO) system of complex industrial processes. However, due to the difficulty in correctly interpreting the multi-sensor data and extracting the desired information, and the strong nonlinearity and its nearly instantaneous response to disturbances, it is still very challenging to achieve accurate faults prediction and optimised performance in such a complex MIMO system. In this paper, we propose to transform the raw multi-sensor data into the time-frequency domain by developing fast lifting wavelet transform with computational efficiency to obtain a big feature vector containing all the relevant features from all of the sensors, and giving more informative signatures for faults prediction. To raise the power and capabilities of machine learning, we propose a novel machine learning system designed by building IoT networks to remotely collect data, and developing deep wavelet neural networks (DWNN) with Gaussian (Mexican hat) wavelet derived as activation functions to improve nonlinear fitting and convergence speed, and to process the real-time data for faults prediction. Experimental results demonstrate that features extracted in time-frequency domain can reveal the presence of a fault, and its type and cause can be explained by the trained DWNN over the IoT communication networks, where the sensing capabilities and the computational power are provided by the designed controller, transmitter and cloud server to track everything that is relevant to operations, such that by deep learning with real-time data analytics we can have a knowledge base from which to predict faults, correct errors, optimize system performance and maximise efficiency. |
| first_indexed | 2024-03-12T16:45:39Z |
| format | Article |
| id | doaj.art-89ec50f47c554b629f251c9b20e07a71 |
| institution | Directory Open Access Journal |
| issn | 2306-8515 1726-5479 |
| language | English |
| last_indexed | 2024-03-12T16:45:39Z |
| publishDate | 2021-04-01 |
| publisher | IFSA Publishing, S.L. |
| record_format | Article |
| series | Sensors & Transducers |
| spelling | doaj.art-89ec50f47c554b629f251c9b20e07a712023-08-08T13:16:56ZengIFSA Publishing, S.L.Sensors & Transducers2306-85151726-54792021-04-012514110 Real-time Faults Prediction by Deep Learning with Multi-sensor Measurements over IoT NetworksG. Y. Luo0Y. Q. Luo1H. G. Gan2School of Physics and Materials Science, Guangzhou UniversityDepartment of Mathematics, London School of Economics and Political ScienceSchool of Physics and Materials Science, Guangzhou UniversityThe advancements in sensing, data processing, and communication technology have enabled machine learning systems to make confident decisions and made it possible to optimise the operations and maintenance of the physical assets, manufacturing systems and processes of prediction and prevention. In many industrial applications, it is critical to use deep neural networks that make predictions both fast and accurate, and can be applied to coupled multiple-input multiple-output (MIMO) system of complex industrial processes. However, due to the difficulty in correctly interpreting the multi-sensor data and extracting the desired information, and the strong nonlinearity and its nearly instantaneous response to disturbances, it is still very challenging to achieve accurate faults prediction and optimised performance in such a complex MIMO system. In this paper, we propose to transform the raw multi-sensor data into the time-frequency domain by developing fast lifting wavelet transform with computational efficiency to obtain a big feature vector containing all the relevant features from all of the sensors, and giving more informative signatures for faults prediction. To raise the power and capabilities of machine learning, we propose a novel machine learning system designed by building IoT networks to remotely collect data, and developing deep wavelet neural networks (DWNN) with Gaussian (Mexican hat) wavelet derived as activation functions to improve nonlinear fitting and convergence speed, and to process the real-time data for faults prediction. Experimental results demonstrate that features extracted in time-frequency domain can reveal the presence of a fault, and its type and cause can be explained by the trained DWNN over the IoT communication networks, where the sensing capabilities and the computational power are provided by the designed controller, transmitter and cloud server to track everything that is relevant to operations, such that by deep learning with real-time data analytics we can have a knowledge base from which to predict faults, correct errors, optimize system performance and maximise efficiency.https://sensorsportal.com/HTML/DIGEST/april_2021/Vol_251/P_3219.pdfreal-time faults predictionmachine learningdeep wavelet neural networkactivation functiondata analytics in time-frequency domainwavelet analysis for feature extraction |
| spellingShingle | G. Y. Luo Y. Q. Luo H. G. Gan Real-time Faults Prediction by Deep Learning with Multi-sensor Measurements over IoT Networks Sensors & Transducers real-time faults prediction machine learning deep wavelet neural network activation function data analytics in time-frequency domain wavelet analysis for feature extraction |
| title | Real-time Faults Prediction by Deep Learning with Multi-sensor Measurements over IoT Networks |
| title_full | Real-time Faults Prediction by Deep Learning with Multi-sensor Measurements over IoT Networks |
| title_fullStr | Real-time Faults Prediction by Deep Learning with Multi-sensor Measurements over IoT Networks |
| title_full_unstemmed | Real-time Faults Prediction by Deep Learning with Multi-sensor Measurements over IoT Networks |
| title_short | Real-time Faults Prediction by Deep Learning with Multi-sensor Measurements over IoT Networks |
| title_sort | real time faults prediction by deep learning with multi sensor measurements over iot networks |
| topic | real-time faults prediction machine learning deep wavelet neural network activation function data analytics in time-frequency domain wavelet analysis for feature extraction |
| url | https://sensorsportal.com/HTML/DIGEST/april_2021/Vol_251/P_3219.pdf |
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