Real-Time Acoustic Anomaly Detection in Vehicles Using AI Processor and Machine Learning

Attila Aradi; Attila Károly Varga

doi:10.30939/ijastech..1769036

Real-Time Acoustic Anomaly Detection in Vehicles Using AI Processor and Machine Learning

Abstract

This study investigates real-time acoustic anomaly detection in vehicles, focusing on diesel particulate filter (DPF) faults in a pickup truck as a case study. Utilizing two WAV audio recordings—one from normal idling and another during a DPF fault—features such as Mel-Frequency Cepstral Coefficients (MFCC) and Discrete Wavelet Transform (DWT) were extracted. Machine learning models, including Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), were deployed on the Hailo-8 AI processor for edge-based, real-time inference. The approach achieved high accuracy in anomaly classification, demonstrating the viability of embedded AI for predictive vehicle maintenance. This non-invasive method enhances fault detection speed and efficiency, reducing downtime and emissions in automotive applications. The edge computing approach demonstrates significant advantages over cloud-based alternatives: sub-millisecond inference latency enables immediate fault detection critical for preventing vehicle damage; continuous operation without network dependency ensures reliability in areas with poor connectivity; local processing eliminates security risks associated with transmitting sensitive vehicle data; and bandwidth requirements are reduced as only anomaly alerts require transmission. Hailo-8's power-efficient architecture enables continuous monitoring without significant battery drain, while its 26 TOPS performance capability ensures real-time processing of complex acoustic signatures. This non-invasive method enhances fault detection speed and efficiency, reducing downtime and emissions in automotive applications while providing a scalable solution deployable across entire vehicle fleets without cloud infrastructure dependencies.

Keywords

References

[1] Erdoğan FA, Küçükmanisa A, Kilimci ZH. Detection of fault from acoustic signals in automobile engines using deep learning techniques. Kocaeli J Sci Eng. 2023;6(2):122-130. https://doi.org/10.34088/kojose.1225591
[2] Nasim F, Masood S, Jaffar A, Ahmad U, Rashid M. Intelligent sound-based early fault detection system for vehicles. Comput Syst Sci Eng. 2023;46(3):3175-3190. https://doi.org/10.32604/csse.2023.034550
[3] Glowacz A. Fault diagnosis of internal combustion engines using acoustic signals and advanced signal processing. Measurement. 2021;167:108478. https://doi.org/10.1016/j.measurement.2020.108478
[4] Wu JD, Liu CH. An expert system for fault diagnosis in internal combustion engine using wavelet packet transform and neural network. Expert Syst Appl. 2009;36(3):4278-4286. https://doi.org/10.1016/j.eswa.2008.03.008
[5] Akbalık F, Yıldız A, Ertuğrul ÖF, Zan H. Engine fault detection by sound analysis and machine learning. Appl Sci. 2024;14(15):6532. https://doi.org/10.3390/app14156532
[6] Zhao X, Yao J, Deng W, Jia M, Liu Z. Intelligent fault diagnosis of gearbox under variable working conditions with adaptive intra-class and interclass convolutional neural network. IEEE Trans Neural Netw Learn Syst. 2022;34(10):6339-6353. https://doi.org/10.1109/TNNLS.2021.3135877
[7] Glowacz A, Glowacz Z. Diagnosis of stator faults of the single-phase induction motor using acoustic signals. Appl Acoust. 2017;117:20-27. https://doi.org/10.1016/j.apacoust.2016.10.012
[8] Hossain MN, Rahman MM, Ramasamy D. Artificial intelligence-driven vehicle fault diagnosis to revolutionize automotive maintenance: a review. Comput Model Eng Sci. 2024;141(2):951-996. https://doi.org/10.32604/cmes.2024.056022

[9] Yan J, Liao JB, Gao JY, Zhang WW, Huang CM, Yu HL. Fusion of audio and vibration signals for bearing fault diagnosis based on a quadratic convolution neural network. Sensors. 2023;23(22):9155. https://doi.org/10.3390/s23229155
[10] Fedorishin D, Birgiolas J, Mohan DD, Forte L, Schneider P, Setlur S. Fine-grained engine fault sound event detection using multimodal signals. arXiv [Preprint]. 2024:arXiv:2403.11037. https://doi.org/10.48550/arXiv.2403.11037
[11] Heo YJ, Kim JH, Park SH, Lee KH. Deep-learning-based approach to anomaly detection techniques for large acoustic data in machine operation. Sensors. 2021;21(16):5446. https://doi.org/10.3390/s21165446
[12] Al-Momani M, Alauthman M, Alweshah M, Atoum J. Machine learning-based anomaly detection for securing in-vehicle networks with autoencoder and LSTM. Electronics. 2024;13(10):1962. https://doi.org/10.3390/electronics13101962
[13] Lei Y, Yang B, Jiang X, Jia F, Li N, Nandi AK. Applications of machine learning to machine fault diagnosis: a review and roadmap. Mech Syst Signal Process. 2020;138:106587. https://doi.org/10.1016/j.ymssp.2019.106587
[14] Tran VT, AlThobiani F, Ball A. An approach to fault diagnosis of reciprocating compressor valves using Teager–Kaiser energy operator and deep belief networks. Expert Syst Appl. 2014;41(9):4113-4122. https://doi.org/10.1016/j.eswa.2013.12.026
[15] Purohit H, Tanabe R, Ichige K, Endo T, Nikaido Y, Suefusa K, et al. MIMII dataset: sound dataset for malfunctioning industrial machine investigation and inspection. In: Proceedings of the Detection and Classification of Acoustic Scenes and Events 2019 Workshop (DCASE2019); 2019. p. 209-213. https://doi.org/10.33682/m76f-d618
[16] El-Sharkawy AM, Abouelazm MN. Deep learning with TinyML driven real-time anomaly detection for predictive maintenance in IoT devices. Ain Shams Eng J. 2025;16(3):102552. https://doi.org/10.1016/j.asej.2024.102552
[17] Michau G, Fink O. Unsupervised fault detection in varying operating conditions via variational autoencoders. Mech Syst Signal Process. 2021;152:107459. https://doi.org/10.1016/j.ymssp.2020.107459
[18] Wang Y, Tian J, Chen Z, Liu X. Acoustic emission signal analysis based on deep learning for pipeline leak detection. Mech Syst Signal Process. 2022;163:108151. https://doi.org/10.1016/j.ymssp.2021.108151
[19] Samek W, Montavon G, Lapuschkin S, Anders CJ, Müller KR. Explaining deep neural networks and beyond: a review of methods and applications. Proc IEEE. 2021;109(3):247-278. https://doi.org/10.1109/JPROC.2021.3060483
[20] Khan S, Yairi T. A review on the application of deep learning in system health management. Mech Syst Signal Process. 2018;107:241-265. https://doi.org/10.1016/j.ymssp.2017.11.024
[21] Abdeljaber O, Avci O, Kiranyaz S, Gabbouj M, Inman DJ. Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. J Sound Vib. 2017;388:154-170. https://doi.org/10.1016/j.jsv.2016.10.043
[22] Lu C, Wang ZY, Qin WL, Ma J. Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification. Signal Process. 2017;130:377-388. https://doi.org/10.1016/j.sigpro.2016.07.028
[23] Zhao R, Yan R, Chen Z, Mao K, Wang P, Gao RX. Deep learning and its applications to machine health monitoring. Mech Syst Signal Process. 2019;115:213-237. https://doi.org/10.1016/j.ymssp.2018.05.050

Details

Primary Language

English

Subjects

Automotive Engineering (Other)

Journal Section

Research Article

Authors

Attila Aradi ^*
0000-0001-5353-3503
Hungary

Attila Károly Varga This is me
0000-0002-9419-4103
Hungary

Publication Date

December 17, 2025

Submission Date

August 21, 2025

Acceptance Date

December 16, 2025

Published in Issue

Year 2025 Volume: 9 Number: 1st Future of Vehicles Conf.

DOI

https://doi.org/10.30939/ijastech..1769036

IZ

https://izlik.org/JA57SD63ES

Cite

RIS / Bibtex

APA

Aradi, A., & Varga, A. K. (2025). Real-Time Acoustic Anomaly Detection in Vehicles Using AI Processor and Machine Learning. International Journal of Automotive Science And Technology, 9(1st Future of Vehicles Conf.), 96-100. https://doi.org/10.30939/ijastech..1769036

AMA

1.Aradi A, Varga AK. Real-Time Acoustic Anomaly Detection in Vehicles Using AI Processor and Machine Learning. IJASTECH. 2025;9(1st Future of Vehicles Conf.):96-100. doi:10.30939/ijastech.1769036

Chicago

Aradi, Attila, and Attila Károly Varga. 2025. “Real-Time Acoustic Anomaly Detection in Vehicles Using AI Processor and Machine Learning”. International Journal of Automotive Science And Technology 9 (1st Future of Vehicles Conf.): 96-100. https://doi.org/10.30939/ijastech. 1769036.

EndNote

Aradi A, Varga AK (December 1, 2025) Real-Time Acoustic Anomaly Detection in Vehicles Using AI Processor and Machine Learning. International Journal of Automotive Science And Technology 9 1st Future of Vehicles Conf. 96–100.

IEEE

[1]A. Aradi and A. K. Varga, “Real-Time Acoustic Anomaly Detection in Vehicles Using AI Processor and Machine Learning”, IJASTECH, vol. 9, no. 1st Future of Vehicles Conf., pp. 96–100, Dec. 2025, doi: 10.30939/ijastech..1769036.

ISNAD

Aradi, Attila - Varga, Attila Károly. “Real-Time Acoustic Anomaly Detection in Vehicles Using AI Processor and Machine Learning”. International Journal of Automotive Science And Technology 9/1st Future of Vehicles Conf. (December 1, 2025): 96-100. https://doi.org/10.30939/ijastech. 1769036.

JAMA

1.Aradi A, Varga AK. Real-Time Acoustic Anomaly Detection in Vehicles Using AI Processor and Machine Learning. IJASTECH. 2025;9:96–100.

MLA

Aradi, Attila, and Attila Károly Varga. “Real-Time Acoustic Anomaly Detection in Vehicles Using AI Processor and Machine Learning”. International Journal of Automotive Science And Technology, vol. 9, no. 1st Future of Vehicles Conf., Dec. 2025, pp. 96-100, doi:10.30939/ijastech. 1769036.

Vancouver

1.Attila Aradi, Attila Károly Varga. Real-Time Acoustic Anomaly Detection in Vehicles Using AI Processor and Machine Learning. IJASTECH. 2025 Dec. 1;9(1st Future of Vehicles Conf.):96-100. doi:10.30939/ijastech. 1769036