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. (1st March 2022) - Prezelj, Jurij; Murovec, Jure; Huemer-Kals, Severin; Häsler, Karl; Fischer, Peter
Highlights: Transform creep groan accelerations to enveloped bursts signal. Detect stick-slip events in time domain based on these acoustic emission contents. Unsupervised classification with k-means and Kohonen's self-organizing map. Distinguish low-frequency, high-frequency, and two kinds of transition groan. Detect and classify disk brake creep groan in real-time. Abstract: Creep groan is a friction-induced, low-frequency vibration and noise phenomenon of a vehicle's 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 groan's 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 Kohonen's 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 groan's 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.