Research Article

, 02 Jan 2026 | 10.62346/ijcn_q1_v14_no1_26_03
Year : 2026 | Volume: 14 | Issue: 1 | Pages : 1-6

Acoustic Signal Analysis in Industrial Automation

Dr.P. Satheeshlingam1 *, Nithiya Sri G, Nithya Shri K, Shree Venkatesh B, Rithish K
  • 1Anna University Chennai, Department of ECE K. Ramakrishnan college of Engineering, Tamilnadu, IN
Acoustic Signal Analysis is a cutting-edge technique in industrial automation that leverages sound and vibration patterns to monitor and evaluate machine performance. Each mechanical component emits a distinct acoustic signature during normal operation. Deviations from this signature often indicate faults such as bearing wear, misalignment, or fluid leaks. By capturing these signals and applying Digital Signal Processing techniques—such as Fast Fourier Transform and spectrogram analysis—industries can detect anomalies at an early stage. This non-invasive approach supports predictive maintenance, minimizes downtime, and enhances overall system efficiency. To operationalize this analysis, Python-based signal processing frameworks are employed. Waveform Visualization is a Time-domain plots of machine sound. FFT Analysis is a Frequency-domain transformation to identify dominant frequencies. Spectrogram Generation is a Time-frequency mapping to visualize evolving acoustic patterns. Filtering and Feature Extraction are Isolating relevant frequency bands for fault classification.

References

[1]    S. Mallat, A Wavelet Tour of Signal Processing: The Sparse Way, 3rd ed., Academic Press, 2008.

[2]    B. Boashash, Time-Frequency Signal Analysis and Processing: A Comprehensive Reference, Academic Press, 2016.

[3]    R. B. Randall, Vibration-based Condition Monitoring: Industrial, Aerospace, and Automotive Applications, John Wiley & Sons, 2011.

[4]    M. O. Sonna, “Acoustic signal-based fault detection in rotating machinery,” IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 10, pp. 8341–8350, 2020.

[5]    A. Widodo and B. S. Yang, “Support vector machine in machine condition monitoring and fault diagnosis,” Mechanical Systems and Signal Processing, vol. 21, no. 6, pp. 2560–2574, 2007.

[6]    Python Software Foundation, “NumPy and SciPy Documentation,” Available: https://numpy.org/doc/ and https://docs.scipy.org/doc/.

[7]    M. A. Akande, O. A. Olatunji, and L. S. Nwankwo, “Acoustic emission signal analysis for machine fault detection,” Journal of Sound and Vibration, vol. 475, pp. 115–131, 2020.

[8]    N. Tandon and A. Choudhury, “A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings,” Tribology International, vol. 32, no. 8, pp. 469–480, 1999.

[9]    M. Elforjani and D. Mba, “Condition monitoring of slow-speed rotating machinery using acoustic emission technique,” Applied Acoustics, vol. 71, no. 9, pp. 812–819, 2010.

[10]    C. Li, R.-V. Sanchez, G. Zurita, M. Cerrada, D. Cabrera, and R. E. Vásquez, “Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibration signals,” Mechanical Systems and Signal Processing, vol. 76–77, pp. 283– 293, 2016.


Keywords: Acoustic Signal, DSP, Fourier Transform, Spectrogram Analysis, Frequency Spectrum for evaluating machine health and performance.

Citation: Dr.P. Satheeshlingam*, Dr.P. Satheeshlingam ( 2026), Acoustic Signal Analysis in Industrial Automation. , 14(1): 1-6

Received: 10/12/2025; Accepted: 02/01/2026;
Published: 02/01/2026

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*Correspondence: Dr.P. Satheeshlingam, satheeslingampece@krce.ac.in


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