Optimizing robotic collection point for accurate mechanical anomaly noise source localization within an indoor sound field environment

Traditional sound source localization faces significant challenges when encountering complex sound fields in industrial environments. However, integrating it into robots offers numerous advantages for monitoring large-scale mechanical equipment. The intricate characteristics of indoor sound fields (ISF) and the sound radiation mechanisms of motors have been thoroughly analyzed by employing modelling and acoustics simulations. Additionally, the acoustic signal qualities at various positions are comprehensively compared and evaluated. To enhance the accuracy of abnormal noise source localization, a novel method of optimal collection point (OCP) of the robot based on the comprehensive feature difference ratio of the multiple metrics has been proposed. The optimal signal quality positions have been precisely identified through this approach. Finally, the superiority and applicability of abnormal noise source localization based on the OCP are validated through experimental tests. The localization error of the abnormal noise source localization under different speed conditions is within 1%, and the localization error under different load conditions with interference can still be within 3%. These provide a new perspective for the localization of mechanical anomaly within complex ISF and facilitate more efficient and reliable equipment monitoring in industrial environments.