Deep Learning-Based Industrial Fault Diagnosis Using Induction Motor Bearing Signals

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.

A cost-effective ECG monitoring in rural areas: leveraging artificial neural networks for efficient healthcare solutions

“…One of the primary methods for detecting heart disease is through the measurement and continuous monitoring of heartbeats. Electrocardiogram (ECG) signals provide a detailed understanding of heart conditions by analyzing physiological signals [4], [5]. Thanks to technological advancements, ECG signals can now be accurately measured and observed using ECG monitoring devices [6], [7].…”