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

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

Psychoacoustic characteristics of different brake creep groan classes and their subjective noise annoyance in vehicle and half‑axle tests

Brake creep groan is a severely annoying noise and vibration phenomenon. Especially on the Asian market, customer feedback about creep groan is common, indicating creep groan’s impact towards the quality impression of a car. Hence, treatment of these stick–slip-related creep groan phenomena is necessary. As numerous design conflicts exist for brake and axle, a complete mitigation of the phenomenon is often not possible. A reduction of creep groan’s annoyance by changing the noise’s level and characteristics is therefore typically aspired. One approach towards this goal could include the usage of psychoacoustics: This work deals with psychoacoustic characteristics of different creep groan classes. Low-frequency groan, high-frequency groan, and transition groan classes are compared regarding loudness, sharpness, roughness, fluctuation strength, and tonality. Standard statistic methods as well as machine learning approaches are applied on signals from vehicle tests and half-axle tests. Test results depict the different characteristics of each creep groan class. By mapping the results to the subjective rating of trained test drivers, the annoyance of different classes is compared. Low-frequency groan, dominated by longitudinal axle vibrations, is found to be least annoying. This low annoyance is best depicted by the psychoacoustic parameters loudness and roughness. Presented results allow an optimization of brake system design to reduce creep groan’s annoyance, leading to higher customer satisfaction and a more goal-oriented treatment of this NVH problem