Thermal Fault Detection and Severity Analysis of Mechanical and Electrical Automation Equipment

Abstract

Effectively diagnosing thermal faults in key parts of mechanical and electrical automation equipment before they become too serious is of crucial importance for the safe and continuous operation of these equipment. However, existing algorithms are not able to establish stable connections among sensors, so the overall control of thermal faults is not ideal enough. To cope with this issue, this paper aims to study the thermal fault detection of mechanical and electrical automation equipment and analyze the severity of the faults. At first, this paper studied the heterogeneous Multi-Sensor Information Fusion (MSIF) problem of sensors installed in key parts inside the mechanical and electrical automation equipment, and proposed a MSIF algorithm based on the D-S evidential theory. Then, the paper evaluated the influence of damages caused by thermal faults on the different parts of the equipment, providing evidences for the installation of sensors in key parts of the equipment. At last, experimental results proved the effectiveness of the proposed algorithm, and the thermal fault detection results were attained.