Abstract: Industrial machinery fault detection systems require both high diagnostic accuracy and computational efficiency for real-time deployment. This study presents a novel hybrid approach that integrates the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with You Only Look Once (YOLO) deep learning for efficient audio-based fault detection in industrial machinery. The proposed methodology employs a two-tiered decision fusion strategy: TOPSIS serves as a rapid mathematical pre-filter analyzing seven acoustic features (RMS, ZCR, Spectral Centroid, Spectral Bandwidth, Peak Frequencies, Kurtosis, and Skewness) extracted from preprocessed 1-2 second audio segments, while YOLO performs detailed spectrogram-based visual analysis on flagged segments. The TOPSIS algorithm normalizes feature vectors, calculates closeness coefficients to ideal and negative-ideal solutions, and classifies segments using a threshold of τ = 0.65. Segments identified as normal terminate processing immediately, while potentially abnormal segments proceed to spectrogram generation and YOLO-based detection. Experimental results on 150 industrial audio segments demonstrate that the hybrid system achieves 93.8% detection accuracy while reducing computational overhead by 85.3% compared to full-dataset YOLO analysis. The TOPSIS pre-filter successfully identifies 128 normal segments (85.3%) with a mean closeness coefficient Ci = 0.847 ± 0.025, while 22 abnormal segments (14.7%) with Ci = 0.084 ± 0.033 are forwarded to YOLO for confirmation. The decision fusion logic enables YOLO to override false positives and flag low-confidence cases for expert review, combining the speed of mathematical analysis with the robustness of deep learning. This approach reduces processing time by approximately 6.8×, decreases GPU utilization by 85%, and minimizes storage requirements for spectrogram images, making it suitable for real-time industrial monitoring systems where computational resources are constrained.

Rommel F. Canencia, Ken D. Gorro, Deolinda E. Caparroso, Marlito V. Patunob, Jonathan C. Maglasang and Joecyn N. Archival. “TOPSIS-YOLO Decision Fusion with Mel-Spectrogram Analysis for Engine Fault Detection”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.11 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161180

@article{Canencia2025,
title = {TOPSIS-YOLO Decision Fusion with Mel-Spectrogram Analysis for Engine Fault Detection},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161180},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161180},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {11},
author = {Rommel F. Canencia and Ken D. Gorro and Deolinda E. Caparroso and Marlito V. Patunob and Jonathan C. Maglasang and Joecyn N. Archival}
}