Comparative Analysis of SVM and ANN for Machine Condition Monitoring and Fault Diagnosis in Gearboxes
Asaad Abdulhussein Dubaish, Alaa Abdulhady Jaber
Abstract
In large-scale manufacturing, ensuring the efficient operation of rotating machines is crucial to avoid breakdowns and failures during production.This article introduces a method for detecting gearbox faults by analyzing vibration signals and employing artificial intelligence techniques, with a particular emphasis on comparing these methods.The diagnostic process consists of three stages: extracting features using Wavelet Packet Transform (WPT) and statistical analysis, selecting optimal properties through the gain ratio method, and using Support Vector Machine (SVM) and Artificial Neural Network (ANN) models to distinguish between faults and assess their performance.The diagnostic outcomes demonstrate that both SVM and ANN models accurately identify various fault patterns depending on the operating conditions.Remarkably, the study highlights the ANN model's superiority over the SVM model in classifying gearbox faults, indicating its suitability for gearbox fault diagnosis.This research yields valuable insights into machine condition monitoring, showcasing the ANN model as a robust tool for gearbox fault detection.The findings advocate for the implementation of ANN-based approaches in real-world applications to enhance the reliability of fault detection and prevention in rotating machines.Furthermore, future research directions may explore additional enhancements and optimizations for ANN models, leading to more advanced machine health monitoring systems in the manufacturing industry.