Fault Diagnosis Systems for Robots: Acoustic Sensing-Based Identification of Detached Components for Fault Localization

As robotic systems become more prevalent in daily life and industrial environments, ensuring their reliability through autonomous self-diagnosis is becoming increasingly important. This study investigates whether acoustic sensing can serve as a viable foundation for such self-diagnostic systems by examining its effectiveness in localizing structural faults. This study focuses on developing a fault diagnosis framework for robots using acoustic sensing technology. The objective is to design a simple yet accurate system capable of identifying fault locations and types of robots based solely on sound data, without relying on traditional sensors or cameras. To achieve this, sweep signals were applied to a modular robot, and acoustic responses were collected under various structural configurations over five days. Frequency-domain features were extracted using the Fast Fourier Transform (FFT), and classification was performed using five machine learning models: Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), XGBoost, and Multi-Layer Perceptron (MLP). Among these, MLP achieved the highest accuracy (71.4%), followed by SVM (65.7%), LightGBM (62.9%), KNN (60%), XGBoost (57.1%), and RF (51.4%). These results demonstrate the feasibility of diagnosing structural changes in robots using acoustic sensing alone, even with a compact hardware setup and limited training data. These findings suggest that acoustic sensing can provide a practical and efficient approach for robot fault diagnosis, offering potential applications in environments where conventional diagnostic tools are impractical. The study highlights the advantages of incorporating acoustic sensing into fault diagnosis systems and underscores its potential for developing accessible and effective diagnostic solutions for robotics.