Digital Twin for Remote Fault Detection of Heavy-Duty Machinery

Remote fault detection is essential for maintaining the performance and reliability of heavy-duty machines. This paper proposes a methodology for remote fault detection using a combination of the IEC 61499 standard and a Python-based runtime environment, DINASORE. The proposed methodology consists of two phases: (1) reference curve acquisition and (2) operation curve acquisition. In the first phase, reference curves are captured for each actuator in the machine when it is new. In the second phase, operation curves are captured for each actuator during machine operation. The operation curves are then compared to the reference curves to detect anomalies, using a combination of Dynamic Time Warp, Pearson Correlation, and Time Series Forest. The proposed methodology was evaluated using a real-world heavy-duty machine, and the results showed that it can be used to assist technicians in remote fault finding. The methodology is also scalable due to the modular nature of IEC 61499 and can be easily adapted to different types of machines or analysis techniques.