A Robust Neural Network Algorithm For Automotive Air Conditioning Fault Detection

Abstract

Accurate and incipient fault detection of air conditioning systems is highly demanded in a car to prevent energy waste and high maintenance cost. However, most fault detection techniques require experiences of drivers which are usually unavailable. In this study, a novel hybrid method is proposed to detect faults for AC systems in car. Two typical faults in AC system are adopted to investigate. An AC fault detection and diagnosis framework is introduced by combining the RBFNN model and the EWMA. The results show that the proposing algorithm detects typical air conditioning faults in a car with high accuracy.

Dynamic management network system of automobile detection applying edge computing

“…For dynamic and nonlinear systems, floating car data-based methods are more suitable for fault detection than fault diagnosis. 4 To detect potential failures, Theissler recorded data from an in-vehicle network of interconnected vehicle subsystems during road tests, which can prove that the ensemble anomaly detector is robust to different driving scenarios and fault types. 5 Guo et al has researched and developed a multi-site vehicle inspection dynamic management network system, which realizes modules such as monitoring station management and operating vehicle management to modernize vehicle technology management.…”

“…Tran et al developed a prediction model using support vector regression to obtain a fault‐free reference. For dynamic and nonlinear systems, floating car data‐based methods are more suitable for fault detection than fault diagnosis 4 . To detect potential failures, Theissler recorded data from an in‐vehicle network of interconnected vehicle subsystems during road tests, which can prove that the ensemble anomaly detector is robust to different driving scenarios and fault types 5 .…”