Data-driven Method in Detecting CAN Network Intermittent Connection Fault

Abstract

The reliability of the network is critical to the performance and the safety of manufacturing systems. Controller area network (CAN) has been widely adopted due to its electrical robustness, low price and deterministic access delay. However, there are numerous factors that can sabotage the reliability of networks. The fault at intermittent connection (IC) of the network cable is one of the major issues. When multiple IC faults occur in the network, especially on the trunk cable, the troubleshooting for these IC faults becomes very difficult. IC problems may result in delayed or deceptive information of machines and production status which can severely impede manufacturing normal operation. To improve the network reliability and communication quality, a novel data-driven fault detection methodology is developed in this paper, which can locate multiple IC faults in the network. By analyzing IC induced network errors, the characteristics of error frames can be captured. The information extracted from the error frames is utilized to filter the network error and facilitate a two-level procedure to identify the IC faults. Case studies are performed to demonstrate the proposed method. This research provides a multiple IC faults location method which is accurate and easy to implement.