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This research addresses the challenge of monitoring large-scale machine halls, particularly in the context of iron making processes. We propose an acoustic sound-based condition monitoring (ASCM) system to detect potential faults and damages in machinery. The study focuses on selecting suitable audio features, integrating physical insights regarding the fault, and determining optimal window lengths for feature extraction. Our fault detection method utilizes outlier detection with a Gaussian mixture model trained on features extracted only from normal operating conditions. We compare conventional audio features with physically motivated features and conduct a window length analysis. The results demonstrate the effectiveness of our approach and the impact of incorporating physically motivated features for fault detection performance.

Acoustic-Based Detection Technique for Identifying Worn-Out Components in Large-Scale Industrial Machinery