Direct Current Motor Malfunction Detection Through Metastatic Optimized Audio Analysis and Classification

This study investigates the integration of advanced signal processing techniques with the AdaBoost classification algorithm for the purpose of identifying malfunctions in brushless DC motors through audio analysis. Given the increasing significance of brushless DC motors in modern manufacturing and transportation, the timely detection of anomalies becomes crucial for maintaining consistent operational efficiency and preventing potential injuries. Despite regular maintenance practices, the occurrence of unexpected mechanical and electrical failures often eludes operators, leading to the potential escalation of minor malfunctions into catastrophic failures. Consequently, the implementation of a reliable and consistent monitoring system is imperative in practical applications. The proposed solution leverages audio analysis as an effective approach. Through the amalgamation of machine learning and advanced signal processing techniques, this research employs audio signal classification to discern malfunctions in brushless DC motors from audio recordings of their operations. The extraction of dominant frequencies using the Fast Fourier Transform (FFT) enhances the analysis, and the AdaBoost classifier is subsequently employed to assess the proper functioning of the motors. Recognizing the substantial impact of hyperparameter selections on classifier performance, a modified version of the Red Fox Optimization (RFO) algorithm is introduced to meet the specific demands of this study. Results demonstrate that the optimizers, when appropriately constructed, achieve perfect accuracy when applied to a real-world dataset, highlighting the potential practical applicability of the proposed methodology.