Power System Fault Diagnosis Based on Multi-Sensor Data Fusion for Large-Scale Railway Maintenance Equipment

In order to solve the defects in the accuracy, reliability and applicability of the current tamper diesel engine fault diagnosis, this paper establishes the causes of symptoms and the corresponding sample sets, introduces the concepts of fuzzy logic and fuzzy neural network structure for the complexity and fuzziness of engine tampering, and applies them to the turbocharger tampering system. Finally, using Matlab simulation, the results show that the combination of neural network and fuzzy logic can provide a more detailed description of the fault phenomena compared with the commonly used neural network and fuzzy theory fault diagnosis methods, making up for the respective deficiencies of fuzzy theory and neural network. It has good application prospects in the field of fault diagnosis of large railroad maintenance equipment