Supervised Classification of Industrial Fan Sound Anomalies Using Neural Networks and Engineered Acoustic Features
DOI:
https://doi.org/10.61769/telematika.v20i1.772Keywords:
industrial fan, anomaly detection, acoustic features, neural network, supervised learningAbstract
This study uses a supervised learning approach based on neural networks for anomaly detection in industrial fan systems. Using a subset of the FAN data from the MIMII (malfunctioning industrial machine investigation and inspection) dataset with 530 labelled recordings (383 normal and 147 abnormal), this study extracts acoustic features including mel-frequency cepstral coefficients (MFCC), spectral descriptors (centroid, roll off), and temporal measures (zero-crossing rate, autocorrelation). Univariate statistical tests reveal that several MFCC coefficients and time-domain features differ significantly between classes (p < 0.05). A feed-forward neural network model with two hidden layers of 64 units (ReLU activation) and dropout regularisation was trained using stratified cross-validation with 5-fold, resulting in an average F1 score of 89.9%. The use of several threshold values (τ ∈ {0.3–0.7}) confirmed the robustness of the model, as seen in the test data results with the selected threshold value of τ = 0.5, which achieved a precision of 100%, recall = 93.10%, F1 = 96.43%, and accuracy = 98.11% (identical results were obtained at τ = 0.6–0.7; while τ = 0.3 provided higher recall). The model also produced an AUC-ROC value of 0.9978, which is close to ideal and demonstrates excellent cross-threshold discrimination. These findings demonstrate that combining interpretable acoustic features with a compact neural classifier enables accurate non-invasive anomaly detection for Industry 4.0 applications with minimal hardware requirements.
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