In the fourth industrial revolution, data-driven intelligent fault diagnosis for industrial purposes serves a crucial role. In contemporary times, although deep learning is a popular approach for fault diagnosis, it requires massive amounts of labelled samples for training, which is arduous to come by in the real world. Our contribution to introduce a novel comprehensive intelligent fault detection model using the Case Western Reserve University dataset is divided into two steps. Firstly, a new hybrid signal decomposition methodology is developed comprising Empirical Mode Decomposition and Variational Mode Decomposition to leverage signal information from both processes for effective feature extraction. Secondly, transfer learning with DenseNet121 is employed to alleviate the constraints of deep learning models. Finally, our proposed novel technique surpassed not only previous outcomes but also generated state-of-the-art outcomes represented via the F1 score.
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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