Multitask Convolutional Neural Network for Rolling Element Bearing Fault Identification
نویسندگان
چکیده
منابع مشابه
A DWT and SVM based method for rolling element bearing fault diagnosis and its comparison with Artificial Neural Networks
A classification technique using Support Vector Machine (SVM) classifier for detection of rolling element bearing fault is presented here. The SVM was fed from features that were extracted from of vibration signals obtained from experimental setup consisting of rotating driveline that was mounted on rolling element bearings which were run in normal and with artificially faults induced conditio...
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Motor systems are very important in modern society. They convert almost 60% of the electricity produced in the U.S. into other forms of energy to provide power to other equipment. In the performance of all motor systems, bearings play an important role. Many problems arising in motor operations are linked to bearing faults. In many cases, the accuracy of the instruments and devices used to moni...
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In this work an automatic fault classification system is developed for bearing fault classification of three phase induction motor. The system uses the wavelet packet decomposition using ‘db8’ mother wavelet function for feature extraction from the vibration signal, recorded for various bearing fault conditions. The selection of best node of wavelet packet tree is performed by using best tree a...
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There has been immense success on the application of Convolutional Neural Nets (CNN) to image and acoustic data analysis. In this paper, rather than preprocessing vibration signals to denoise or extract features, we investigate the usage of CNNs on raw signals; in particular, we test the accuracy of CNNs as classifiers on bearing fault data, by varying the configurations of the CNN from one-lay...
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ژورنال
عنوان ژورنال: Shock and Vibration
سال: 2020
ISSN: 1070-9622,1875-9203
DOI: 10.1155/2020/1971945