@article {Gerdes:2025:0736-2935:1191,
title = "Acoustics Resonance Spectrometry (ARS) for Advanced Streaming Prognostics of Drivetrain System",
journal = "INTER-NOISE and NOISE-CON Congress and Conference Proceedings",
parent_itemid = "infobike://ince/incecp",
publishercode ="ince",
year = "2025",
volume = "271",
number = "1",
publication date ="2025-07-25T00:00:00",
pages = "1191-1206",
itemtype = "ARTICLE",
issn = "0736-2935",
url = "https://ince.publisher.ingentaconnect.com/content/ince/incecp/2025/00000271/00000001/art00019",
doi = "doi:10.3397/NC_2025_0213",
author = "Gerdes, Matthew and Wang, Yawen and Wang, Guang and Wei, Xinqi and Rios, Levi",
abstract = "Failure prognostics is a crucial research direction in modern industrial practices. However, predicting failures from measured data remains challenging due to the high-frequency sampling rates in the time domain and the low sensitivity of statistical moments and frequency-domain analysis
to subtle degradation. To address this problem, this paper proposes the use of Acoustic Resonance Spectrometry (ARS) as a machine learning-based approach for predictive monitoring of acoustic signals. By leveraging ARS, real-time analysis becomes feasible, as the high-frequency sampling rate
is effectively reduced. First, the algorithmic framework of ARS is introduced to generate a multivariate dataset. Then, the generated data is fed into a machine learning model known as the Multivariate State Estimation Technique (MSET) for anomaly detection in time-series data. Finally, experimental
studies validate the proposed approach, demonstrating its effectiveness in accurately and efficiently predicting failures.",
}