Work

Row-Sparse Bayesian Integrated Detection of Duct Acoustic Modes in the Presence of Faulty Microphones

Year: 2026

Type: article

Abstract: Duct acoustic mode identification is easily biased by faulty microphones, whose abnormal channel deviations degrade the performance of traditional discrete Fourier transform and compressive-sensing methods. To address this issue, this paper proposes a row-sparse Bayesian integrated detection (RBID) method for simultaneous faulty-microphone localization and duct mode identification. In the proposed framework, abnormal channel deviations and mode coefficients are jointly modeled under a multi-snapshot row-sparse hierarchical prior, and their posterior estimates are inferred within a Bayesian framework. Faulty microphones are then automatically localized from the estimated abnormal deviations, after which a second-stage Bayesian compressive-sensing identification is performed using the remaining healthy channels. Numerical simulations show that, when the proportion of faulty microphones does not exceed 10%, the proposed method achieves fault-detection accuracies above 90% and dominant-mode amplitude errors below 1 dB. Experiments on a 1.5-stage axial compressor further show that, after excluding the detected faulty channels, the dominant-mode amplitude error is around 0.5 dB. The proposed method is effective for duct mode identification under a relatively small proportion of persistent or quasi-persistent faulty microphones, while reducing the need for case-by-case tuning of regularization coefficients.

Source: Journal of Theoretical and Computational Acoustics

DOI: 10.1142/s2591728526500106

Authors: Weiwei Wang, Chenyu Zhang, Youhong Xiao, Ran Wang, Liang Yu, Guangming Dong