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.

蠕动呻吟是车辆制动系统由摩擦引起的低频振动和噪声现象，由重复的粘滑效应激发。加上设计和操作参数的高影响，非线性粘滑导致蠕变呻吟的有趣分叉行为。对于客观的评级程序，考虑到这种分叉行为的检测和分类方法是必要的。在这项研究中，提出了一种基于声发射的方法。该方法利用伴随蠕变呻吟特征粘滑过渡的高频加速内容。而低于 500 Hz 的低频振动含量主要由制动系统和车辆悬架的特性定义，10 kHz 以上的高频范围内的振动表现出类似于声发射爆发模式的波形模式。通过应用不重叠的高通和低通滤波器，创建了一个包含这些突发的新信号。这种包络脉冲信号可以直接在时域中精确检测和量化粘滑转变，并导致开发了一套全新的振动信号特征。这九个信号特征用于提供无监督分类算法 k-means 和 Kohonen 的自组织图，从而提供稳健且有意义的结果。检测到四种不同的蠕动呻吟类别，其中每一种都表明与特定的蠕变呻吟表现有关：低频呻吟、发现了高频呻吟和两个过渡现象，每个周期有两个/三个粘滑事件。分类结果及其相关的机械行为表明蠕变呻吟期间两种显着振动模式之间存在相互作用，可能是轴的纵向位移和扭转位移。除了对蠕变呻吟的分叉行为有更深入的了解之外，本研究不仅可以识别蠕变呻吟，还可以对其表现形式进行实时自动分类，因此为蠕变呻吟控制方法提供了进一步的可能性。