Application of Combined Wavelet Transformation and Neural Network in Electrical System Malfunction Detection of Engine

Engine failure is a critical issue for drivers and often requires substantial experience to diagnose and resolve effectively. Attempting repairs based on guesswork or uncertain causes can lead to significant time loss and high costs. Recently, Artificial Intelligence (AI) models, particularly those based on Artificial Neural Networks (ANNs), have shown promising performance in fault diagnosis. This study focuses on detecting two common faults in internal combustion engines—cylinder misfire and complete cylinder failure—both typically caused by problems in the ignition system. A model referred to as WANN (Wavelet+ Artificial Neural Network) is proposed, which uses coefficients are derived from vibration signals by wavelet transformation. The WANN achieved over ۹۰% classification accuracy in identifying ignition-related faults. To evaluate the model's generalizability, the WANN is also tested on a different engine, successfully classifying fault types with acceptable accuracy. Notably, the model accurately detected ignition faults in a vehicle-mounted engine, demonstrating its robustness and practical utility.This capability allows mechanics and technicians to accurately pinpoint the fault type, leading to more efficient and cost-effective repairs. Therefore, the proposed method offers a reliable and intelligent solution for diagnosing ignition system faults in automotive applications.

کلیدواژه ها:

Ignition System Malfunction ، Wavelet Transformation ، Artificial Intelligence Model ، Diagnosis ، Internal combustion engine