Identification of fault frequency variation in the envelope spectrum in the vibration-based local damage detection in possible changing load/speed conditions

TL;DR

This paper introduces an automatic statistical method to detect variations in fault frequency in vibration signals, improving local damage diagnosis under changing load and speed conditions.

Contribution

It proposes a novel procedure combining peak detection and statistical testing to evaluate fault frequency stability, addressing challenges posed by load and speed fluctuations.

Findings

Fault frequency variation can be detected using the proposed statistical approach.

Order analysis is recommended when fault frequency distribution deviates from Gaussian.

Simulation and industrial tests validate the effectiveness of the method.

Abstract

The problem of local damage diagnosis (based on the detection of impulsive and periodic signals) is discussed. Both features should be checked, as fault frequency must be linked to the true value calculated for a given machine and speed. The precise estimation of the fault frequency is hard due to several factors. If a speed fluctuation exists, it is solved by order analysis. A wider perspective is proposed here, namely, an automatic statistical approach to analyze the distribution of estimated fault frequencies. We propose a procedure to evaluate whether the fault frequency is constant or not. The algorithm uses frequency estimation based on peak detection in the envelope spectrum and statistical testing. We present simulation studies and industrial examples. We have found that if the fault frequency is not constant and its distribution does not follow Gaussian shape with minor variance, then one should use more advanced techniques, e.g. order analysis.