Identification of different manifestations of nonlinear stick-slip phenomena during creep groan braking noise by using the unsupervised learning algorithms k-means and self-organizing map

Prezelj, Jurij (avtor), Murovec, Jure (avtor), Huemer-Kals, Severin (avtor), Häsler, Karl (avtor), Fischer, Peter (avtor)

Creep groan is a friction-induced, low-frequency vibration and noise phenomenon of a vehicleʼs brake system which is excited by a repeating stick-slip effect. Together with high influences of design and operational parameters, the non-linear stick-slip leads to an interesting bifurcation behaviour of creep groan. For objective rating procedures, detection and classification methods considering this bifurcation behaviour are necessary. Within this study, an approach based on acoustic emission is presented. The approach harnesses high-frequency acceleration contents that accompany creep groanʼs characteristic stick-slip transitions. Whereas low-frequency vibration contents below 500 Hz are mainly defined by the characteristics of the brake system and the suspension of the vehicle, vibrations in the high-frequency range above 10 kHz exhibit patterns of waveforms similar to the patterns of acoustic emission bursts. By applying non-overlapping high- and low-pass filters, a novel signal, enveloping these bursts, was created. This envelope bursts signal enables a precise detection and quantification of stick-slip transitions directly in time domain, and led to the development of a whole new set of vibration signal features. These nine signal features were used to feed the unsupervised classification algorithms k-means and Kohonenʼs self-organizing map, which delivered robust and meaningful results. Four different creep groan classes were detected, where each has shown to be linked to a specific creep groan manifestation: Low-frequency groan, high-frequency groan and two transition phenomena with two/three stick-slip events per cycle were found. Classification results and their linked mechanical behaviour suggest an interaction between two significant vibration patterns during creep groan, probably a longitudinal and a torsional displacement of the axle. Aside of deeper insights in creep groanʼs bifurcation behaviour, the presented study enables not only the identification of creep groan, but also the automatic classification of its manifestations in real-time, and therefore provides further possibilities for creep groan control methods.

• Jezik: Angleški jezik
• Ključne besede: brake NVH, signal processing, acoustic emission, signal features, unsupervised classification, real-time AE envelope
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FS - Fakulteta za strojništvo
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2022
• Št. strani: Str. 1-17
• Številčenje: Vol. 166, art. 108349
• PID: 20.500.12556/RUL-130987
• UDK: 534.83:681.8
• ISSN pri članku: 0888-3270
• DOI: 10.1016/j.ymssp.2021.108349
• COBISS.SI-ID: 77015299
• Datum objave v RUL: 21.09.2021

PREZELJ, Jurij, MUROVEC, Jure, HUEMER-KALS, Severin, HÄSLER, Karl in FISCHER, Peter, 2022, Identification of different manifestations of nonlinear stick-slip phenomena during creep groan braking noise by using the unsupervised learning algorithms k-means and self-organizing map. Mechanical systems and signal processing [na spletu]. 2022. Vol. 166,  108349, p. 1–17. [Dostopano 3 maj 2025]. DOI 10.1016/j.ymssp.2021.108349. Pridobljeno s: https://repozitorij.uni-lj.si/IzpisGradiva.php?lang=slv&id=130987

Gradivo je del revije

• Naslov: Mechanical systems and signal processing
• Skrajšan naslov: Mech. syst. signal process.
• Založnik: Elsevier
• ISSN: 0888-3270
• COBISS.SI-ID: 169243

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
• Začetek licenciranja: 14.08.2021

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: hrup, zavore, digitalna obdelava signalov, akustična emisija, psihoakustične značilke, nenadzorovano učenje, vibracije

Projekti

• Financer: ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
• Številka projekta: P2-0401
• Naslov: Energetsko strojništvo

• Financer: Drugi - Drug financer ali več financerjev
• Program financ.: Mercedes-Benz AG