Fault diagnosis of helicopter accessory gearbox under multiple operating conditions based on feature mode decomposition and multi-scale convolutional neural networks

The expanding global civil helicopter market has intensified the need for advanced fault diagnosis techniques in helicopter engines. Traditional fault classification methods, such as Variational Mode Decomposition (VMD), have limitations in decomposing complex signals and separating different signal components, which can impede accurate fault feature extraction and compromise diagnostic accuracy and reliability. This paper introduces a novel fault diagnosis method that combines Feature Mode Decomposition (FMD) with a Multi-Scale Convolutional Neural Network (MCNN) to address these challenges. The approach begins by collecting signals from helicopter accessory gearboxes under simulated ground operation conditions. The FMD technique is applied to decompose the gear vibration signals, and the decomposed signals from different sensors are reconstructed and normalized. This preprocessed data is then fed into the MCNN network at various scales, enabling simultaneous extraction and fusion of multi-scale features. The final fault classification is performed using a $\mathrm{softmax}$ classifier. Experimental results demonstrate the efficacy of the proposed method in extracting fault features. Under the given conditions, a fault diagnosis accuracy of up to 100 % was achieved for helicopter accessory gearboxes, marking a significant improvement of 3.1 % compared to VMD-based methods. This enhancement in accuracy represents a substantial advancement in aviation safety and reliability. The study showcases the superior performance of FMD in decomposing complex mechanical signals, particularly its ability better to capture both periodic and impulsive characteristics of fault signals. The integration of MCNN allows for more effective multi-sensor data processing, enhancing the model's capacity to detect and classify faults across various scales and conditions. The FMD-MCNN approach improves the accuracy and efficiency of fault diagnosis and demonstrates significant potential for practical application in aviation maintenance technology.