Fog-based Intelligent Machine Malfunction Monitoring System for Industry 4.0

Abstract

There is an exponential increase in the use of Industrial Internet of Things (IIoT) devices for controlling and monitoring machines in an automated manufacturing industry. Different temperature sensors, pressure sensors, audio sensors, and camera devices are used as IIoT devices for pipeline monitoring and machine operation control in the industrial environment. But, monitoring and identifying the machine malfunction in an industrial environment is a challenging task. In this work, we consider machines fault diagnosis based on their operating sound using the fog computing architecture in the industrial environment. The different computing units, such as Industrial controller units or Micro Data Center are used as the fog server in the industrial environment to analyze and classify the machine sounds as normal and abnormal. The Linear Prediction Coefficients and Mel Frequency Cepstral Coefficients are extracted from machine sound to develop and deploy supervised machine learning models on the fog server to monitor and identify the malfunctioning machines based on the operating sound. The experimental results show the performance of machine learning models for the machines sound recorded with different Signal to Noise Ratio levels for normal and abnormal operations. IEEE