A method for signal components identification in acoustic signal with non-Gaussian background noise using clustering of data in time-frequency domain

TL;DR

This paper introduces a clustering-based method to identify signal components in acoustic signals contaminated with non-Gaussian noise, improving fault detection in industrial environments.

Contribution

It proposes a novel approach using spectral vector density analysis and DBSCAN clustering to separate signal components from complex noise.

Findings

Effective separation of signal and noise demonstrated on real industrial data

Improved fault detection accuracy in non-Gaussian noise conditions

Validated method using envelope spectrum-based indicator (ENVSI)

Abstract

This paper presents a novel method for fault detection in vibration/acoustic signals contaminated with non-Gaussian noise, specifically addressing the challenge of random impulsive and wideband disturbances in industrial measurements. While damage detection in Gaussian noise environments is well understood, high-amplitude non-cyclic impulsive disturbances arising from random aspects of industrial processes, such as non-uniform operations and random impacts, pose significant analytical challenges. The proposed method analyzes the distribution densities of spectral vectors derived from spectrograms. It considers a simple additive model consisting of the signal of interest (SOI) and Gaussian and non-Gaussian noise. Using the density-based spatial clustering algorithm (DBSCAN), the method isolates distinct classes of spectral vectors from the spectrogram, effectively separating different signal behaviors and extracting fault-related information. The effectiveness of the proposed method was validated using an envelope spectrum-based indicator (ENVSI) and successfully demonstrated on real signals from an industrial machine with a faulty bearing.