Inertial Machine Monitoring System for Automated Failure Detection

Abstract

Smart manufacturing technologies are emerging which combine industrial equipment with Internet-of-Things (IoT) sensors to monitor and improve productivity of manufacturing. This allows for new opportunities to explore algorithms for predicting machine failures from attached sensor data. This paper presents a solution to non-invasively upgrade an existing machine with an Inertial Machine Monitoring System (IMMS) to detect and classify equipment failure or degraded state. We also provide a strategy to optimize the amount, placement locations, and efficiency of the sensors. In experiments, the system collected data from 36 inertial sensors placed at multiple locations on a 3D printer. Normal operation vs. 10 types of real-world abnormal equipment behavior (loose belt, failures of machine components) were detected and classified by Support Vector Machines and Neural Networks. Using under 1 minute of recording while running a test print, a recursively discovered best subset of 4 to 9 sensors yielded 11-way classification accuracy over 99%. Our results suggest that even a small sensor network and short test program can yield effective detection of machine degraded state and can facilitate early remediation.