An explainable deep learning approach for detection and isolation of sensor and machine faults in predictive maintenance paradigm

Exploring foci of: Measurement Science and Technology • Vol 35 • No 1 An explainable deep learning approach for detection and isolation of sensor and machine faults in predictive maintenance paradigm October 2023 • Aparna Sinha, Debanjan Das Abstract The predictive health maintenance techniques identify the machine faults by analyzing the data collected by low-cost sensors assuming that sensors are free from any faults. However, aging and environmental condition cause sensors also be faulty, leading to incorrect interpretations of the collected data and subsequently resulting in erroneous machine health predictions. To mitigate this problem, this paper proposes a hybrid model that can differentiate between sensor and system faults. The data used for t… Open Article Page

Computer Science Autoencoder Convolutional Neural Network Artificial Intelligence Deep Learning Machine Learning Fault (Geology) Extreme Learning Machine Data Mining Open Article

Engineering Random Forest Seismology Open Article