A robust sequential hypothesis testing method for brake squeal localisation

Abstract

This contribution deals with the in situ detection and localisation of brake squeal in an automobile. As brake squeal is emitted from regions known a priori, i.e., near the wheels, the localisation is treated as a hypothesis testing problem. Distributed microphone arrays, situated under the automobile, are used to capture the directional properties of the sound field generated by a squealing brake. The spatial characteristics of the sampled sound field is then used to formulate the hypothesis tests. However, in contrast to standard hypothesis testing approaches of this kind, the propagation environment is complex and time-varying. Coupled with inaccuracies in the knowledge of the sensor and source positions as well as sensor gain mismatches, modelling the sound field is difficult and standard approaches fail in this case. A previously proposed approach implicitly tried to account for such incomplete system knowledge and was based on ad hoc likelihood formulations. The current paper builds upon this approach and proposes a second approach, based on more solid theoretical foundations, that can systematically account for the model uncertainties. Results from tests in a real setting show that the proposed approach is more consistent than the prior state-of-the-art. In both approaches, the tasks of detection and localisation are decoupled for complexity reasons. The localisation (hypothesis testing) is subject to a prior detection of brake squeal and identification of the squeal frequencies. The approaches used for the detection and identification of squeal frequencies are also presented. The paper, further, briefly addresses some practical issues related to array design and placement.

Estimation of Microphone Clusters in Acoustic Sensor Networks Using Unsupervised Federated Learning

“…The estimation of source-dominated microphone clusters in ASNrelated scenarios has been previously explored using various approaches and with different target applications. These have ranged from using coherence models [12] and energy decay information to eigenvectors [13] and spectral features [2,4,5,14]. In conjunction with unsupervised fuzzy clustering, the latter works obtain robust clustering and subsequently improved signal classification results.…”

“…The continuously decreasing cost of acoustic sensors and the rise in popularity of wireless networks and mobile devices have helped to establish the technological infrastructure needed by (wireless) acoustic sensor networks (ASNs). These are useful in a larger number of applications, ranging from smart-homes and ambient-assisted living [1] to machine fault diagnosis [2] and surveillance [3].…”

Impact analysis of contact symmetrical caliper structure on brake squeal

“…Many scholars have proposed a lot of methods to analyze the brake noise problem in recent years. Madhu et al (2019) proposed a new positioning algorithm to detect brake noise based on the previous most advanced technology; the proposed method treats the problem as a multi-hypothesis testing problem. Denimal et al (2019) proposed to use the kriging method to consider different uncertain parameters, evaluate the impact of structural modification on the stability of the brake system, and obtain the information for selecting the optimal brake design.…”