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Deep Learning-Based Industrial Fault Diagnosis Using Induction Motor Bearing Signals
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Author(s): Saiful Islam (Ahsanullah University of Science and Technology, Bangladesh), Sovon Chakraborty (European University of Bangladesh, Bangladesh), Jannatun Naeem Muna (United International University, Bangladesh), Moumita Kabir (European University of Bangladesh, Bangladesh), Zurana Mehrin Ruhi (Brac University, Bangladesh)and Jia Uddin (Woosong University, South Korea)
Copyright: 2023
Pages: 29
Source title:
Applied AI and Multimedia Technologies for Smart Manufacturing and CPS Applications
Source Author(s)/Editor(s): Emmanuel Oyekanlu (Drexel University, USA)
DOI: 10.4018/978-1-7998-7852-0.ch003
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Abstract
Earlier detection of faults in industrial types of machinery can reduce the cost of production. Observing these machines for humans is always a difficult task, for that purpose we need an automated process that can constantly monitor these machines. Without continuous monitoring, a huge downfall can happen that can cost enormous monitory value. In this research, we propose some transfer learning models along with LSTM for earlier detection of faults from vibration signals. Open source Case Western Reserve University (CWRU) dataset has been used to detect four types of signals using transfer learning models. The four classes are Normal, Inner, Ball, Outer. The dataset has divided into three parts namely set1, set2, and set3. VGG19, DenseNet-121, ResNet-50, InceptionV3, and LSTM are applied to that dataset for detecting faults in this signal. The earlier result shows VGG19, LSTM and InceptionV3 can predict the faults in signal with 100% accuracy in the validation set where DenseNet-121, Resnet-50 show an accuracy of 97% and 98% respectively.
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