<i>D-dCNN</i>: A Novel Hybrid Deep Learning-Based Tool for Vibration-Based Diagnostics

• Main Authors: Ugochukwu Ejike Akpudo, Jang-Wook Hur
• Format: Article
• Language: English
• Published: MDPI AG 2021-08-01
• Series: Energies
• Subjects: parallel learning; vibration monitoring; fault detection and isolation; convolutional neural network; deep neural network
• Online Access: https://www.mdpi.com/1996-1073/14/17/5286

This paper develops a novel hybrid feature learner and classifier for vibration-based fault detection and isolation (FDI) of industrial apartments. The trained model extracts high-level discriminative features from vibration signals and predicts equipment state. Against the limitations of traditional machine learning (ML)-based classifiers, the convolutional neural network (CNN) and deep neural network (DNN) are not only superior for real-time applications, but they also come with other benefits including ease-of-use, automated feature learning, and higher predictive accuracies. This study proposes a hybrid DNN and one-dimensional CNN diagnostics model (<i>D-dCNN</i>) which automatically extracts high-level discriminative features from vibration signals for FDI. Via <i>Softmax averaging</i> at the output layer, the model mitigates the limitations of the standalone classifiers. A diagnostic case study demonstrates the efficiency of the model with a significant accuracy of 92% (F1 score) and extensive comparative empirical validations.