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All Journal Sinergi Jurnal Teknik Mesin Teknomekanik International Journal of Innovation in Mechanical Engineering and Advanced Materials IRA Jurnal Pengabdian Kepada Masyarakat
Rikko Putra Youlia
Universitas Mercu Buana
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Other

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Towards enhanced acoustic fan booster damage detection: a comparative study of feature-based and machine learning approaches Youlia, Rikko Putra; Romahadi, Dedik; Feleke, Aberham Genetu; Nugroho, Irfan Evi; Alina, Alina
SINERGI Vol 30, No 1 (2026)
Publisher : Universitas Mercu Buana

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Abstract

Machine failure detection frequently uses non-destructive monitoring techniques such as vibration analysis. Although vibration analysis can identify machine degradation, the apparatus is often costly and necessitates specialist knowledge. Additionally, many existing methods in audio classification rely on characteristics represented as pictures or vectors, which increases computational complexity. In contrast, this research introduces a novel method that substitutes vibration data with a singular numerical feature derived from audio signals, addressing both cost and complexity issues. Our objective is to develop a rapid and precise audio-based method for detecting machine damage. The acoustic signals from the machine apparatus were classified into three categories: normal, belt damage, and combined belt and bearing defect. The data processing technique involved lowering the sample rate and segmenting the data to improve computational efficiency and classification performance. We use the Welch method and appropriate statistical techniques to analyze Power Spectral Density (PSD). The performance of seven classifier models, KNN, LDA, SVM, NB, ANN, RF, and DT, was evaluated using accuracy, precision, sensitivity, specificity, and F-score. LDA achieved the highest accuracy at 92.83%, followed by ANN (92.75%), NB (92.74%), and DT (92.34%). These models outperformed KNN (89.90%) and RF (89.40%), with SVM recording the lowest accuracy at 85.40%. LDA was highly effective, achieving the highest accuracy with a single average PSD-type feature, showcasing its robustness in machine defect diagnosis. Compared to previous methods, this approach simplifies feature extraction, reduces computational demands, and maintains high diagnostic performance, providing notable benefits in terms of effectiveness and precision. 
Co-Authors Alina, Alina Anggara, Fajar Dedik Romahadi Diah Utami Diah Utami, Diah Fajar Anggara Feleke, Aberham Genetu Hadi Pranoto Hifdzul Luthfan Habibullah Himawan S. Wibisono Hui Xiong Ilhamullah, Ilhamullah Muhammad Imran Nafis, Muhammad Luthfi Nugroho, Irfan Evi Nur Indah, Nur Pangestu, Dimas Aji Rahmatullah, Rizky Romahadi, Dedik RR. Ella Evrita Hestiandari Satryo Budi Utomo Widayat, Puspita Dewi Wiwit Suprihatiningsih Yang Xiawei Yishuang, Tang