Impact of Industrial Noise on Production Efficiency
DOI:
https://doi.org/10.62671/perfect.v3i2.281Keywords:
acoustic monitoring, industrial machinery, maintenance, noise control, production efficiency, sound pressure levelAbstract
Industrial noise is a common problem in manufacturing environments because it affects occupational comfort, machine reliability, and production efficiency. Objective: This study examines whether acoustic monitoring and sound pressure level measurement can be used to connect industrial machinery noise with output losses and maintenance needs. Methods: The method combined machine observation, acoustic signal recording, cutter cycle timing, production counting, and comparative analysis of a pasta packaging machine, lathe, multi-spindle drilling machine, and cigarette production machine. Results: The pasta packaging machine showed the clearest relationship between noise behavior and productivity. Its cutter was designed to operate every one and one-half seconds, but the measured acoustic rhythm indicated an average cycle of about one and two-thirds seconds. This timing difference reduced output from four hundred to three hundred sixty-one bags in ten minutes, equal to a loss of about ten percent. Other machines with higher sound pressure levels also showed signs of wear, vibration, and reduced operational stability. Conclusion: Industrial machinery noise is not only a workplace hazard but also a practical indicator of machine condition. The study supports using noise control, maintenance, acoustic monitoring, industrial machinery assessment, and production efficiency evaluation as an integrated approach for improving reliability and manufacturing performance.
References
Aradi, A., & Varga, A. K. (2024). Enhancing Predictive Maintenance in Industrial Systems Through Acoustic Monitoring of Servo Motors Using Machine Learning (AI). In International Conference on Intelligent and Fuzzy Systems (pp. 690–697). Springer. https://doi.org/10.1007/978-3-031-67195-1_76
Cao, X., Lu, Y., Zheng, D., & Qin, P. (2025). Investigating the effects of construction industry noise on workers’ cognitive performance and learning efficiency. Frontiers in Human Neuroscience, 19, 1549824.
Chis, T. V., Cioca, L.-I., Badea, D. O., Cristea, I., Darabont, D. C., Iordache, R. M., Platon, S. N., Trifu, A., & Barsan, V.-A. (2025). Integrated noise management strategies in industrial environments: a framework for occupational safety, health, and productivity. Sustainability, 17(3), 1181.
Chua, A., & Evangelista, D. (2024). Automated Predictive Maintenance System of Lathe Machine Cutting Tool Through Combined Vibration and Sound Pressure Sensors with PLC. 2024 4th International Conference on Robotics, Automation and Artificial Intelligence (RAAI), 134–143.
Di Fiore, E., Ferraro, A., Galli, A., Moscato, V., & Sperlì, G. (2022). An anomalous sound detection methodology for predictive maintenance. Expert Systems with Applications, 209, 118324.
Dong, Y., Li, Y., Liu, Y., Zhao, Y., Zhang, H., Sun, Y., & Li, Z. (2025). Surveillance of noise exposure levels in workplaces in Beijing. Frontiers in Public Health, 13, 1486497. https://doi.org/10.3389/fpubh.2025.1486497
Garcia, J., Rios-Colque, L., Peña, A., & Rojas, L. (2025). Condition Monitoring and Predictive Maintenance in Industrial Equipment: An NLP-Assisted Review of Signal Processing, Hybrid Models, and Implementation Challenges. Applied Sciences, 15(10), 5465. https://doi.org/10.3390/app15105465
Hafiz, N. F. M., Mashohor, S., Azrul, M. H. S. E. M., Ali, A. M., & Rasid, M. F. A. (2025). Machine Learning Framework for Industrial Machine Sound Classification in Predictive Maintenance. IEEE Access.
Hassan, I. U., Panduru, K., & Walsh, J. (2024). Review of Data Processing Methods Used in Predictive Maintenance for Next Generation Heavy Machinery. Data, 9(5), 69. https://doi.org/10.3390/data9050069
Jombo, G., & Zhang, Y. (2023). Acoustic-based machine condition monitoring—methods and challenges. Eng, 4(1), 47–79.
Piankitrungreang, P., Chaiprabha, K., Chungsangsatiporn, W., Ratanasumawong, C., Chancharoen, P., & Chancharoen, R. (2025). Acoustic-Based Machine Main State Monitoring for High-Speed CNC Drilling. Machines, 13(5), 372.
Radonjić, M., Vujnović, S., Krstić, A., & Zečević, Ž. (2022). IoT system for detecting the condition of rotating machines based on acoustic signals. Applied Sciences, 12(9), 4385.
Souza, A. F. de, Verri, F. A. N., Campos, P. H. da S., & Balestrassi, P. P. (2024). Predicting tool life and sound pressure levels in dry turning using machine learning models. The International Journal of Advanced Manufacturing Technology, 135(7), 3777–3793.
Suawa, P. F., Halbinger, A., Jongmanns, M., & Reichenbach, M. (2023). Noise-robust machine learning models for predictive maintenance applications. IEEE Sensors Journal, 23(13), 15081–15092.
Tsuji, K., Imai, S., Takao, R., Kimura, T., Kondo, H., & Kamiya, Y. (2021). A machine sound monitoring for predictive maintenance focusing on very low frequency band. SICE Journal of Control, Measurement, and System Integration, 14(1), 27–38.
Ye, T., Peng, T., & Yang, L. (2025). Review on sound-based industrial predictive maintenance: from feature engineering to deep learning. Mathematics, 13(11), 1724.
Zhang, C., Wang, J., Wang, H., & Zhang, H. (2024). Surveillance of noise exposure level in industrial enterprises—Jiangsu Province, China, 2022. Frontiers in Public Health, 12, 1230481.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Osamah Ibrahim Ali Barka, Abdulqadir M. Alhadar, Musbag Ahedery, Omer I. A. Hmellah, Nuri Salem Ali Abosetha, Ahmed Alnagrat (Author)

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.



