Grzegorz Żak

Data-Driven Iterative Vibration Signal Enhancement Strategy Using Alpha Stable Distribution

The authors propose a novel procedure for enhancement of the signal to noise ratio in vibration data acquired from machines working in mining industry environment. Proposed method allows performing data-driven reduction of the deterministic, high energy, and low frequency components. Furthermore, it provides a way to enhance signal of interest. Procedure incorporates application of the time-frequency decomposition, -stable distribution based signal modeling, and stability parameter in the time domain as a stoppage criterion for iterative part of the procedure. An advantage of the proposed algorithm is data-driven, automative detection of the informative frequency band as well as band with high energy due to the properties of the used distribution. Furthermore, there is no need to have knowledge regarding kinematics, speed, and so on. The proposed algorithm is applied towards real data acquired from the belt conveyor pulley drive’s gearbox.

Measures of Dependence for -Stable Distributed Processes and Its Application to Diagnostics of Local Damage in Presence of Impulsive Noise

Local damage detection in rotating machinery is simply searching for cyclic impulsive signal in noisy observation. Such raw signal is mixture of various components with specific properties (deterministic, random, cyclic, impulsive, etc.). The problem appears when the investigated process is based on one of the heavy-tailed distributions. In this case the classical measure can not be considered. Therefore, alternative measures of dependence adequate for such processes should be considered. In this paper we examine the structure of dependence of alpha-stable based systems expressed by means of two measures, namely, codifference and covariation. The reason for using alpha-stable distribution is simple and intuitive: signal of interest is impulsive so its distribution is heavy-tailed. The main goal is to introduce a new technique for estimation of covariation. Due to the complex nature of such vibration signals applying novel methods instead of classical ones is recommended. Classical algorithms usually are based on the assumption that theoretical second moment is finite, which is not true in case of the data acquired on the faulty components. Main advantage of our proposed algorithm is independence from second moment assumption.

A New Technique for Local Damage Detection Based on Statistical Properties of Vibration Signal

In this paper novel methods concerning informative band selection are presented. Given the specific behavior of signals coming from the faulty component, it is reasonable to provide methods allowing for distinction between healthy and damaged components. The proposed approach is based on the measurement of the distance between empirical distribution and Gaussian one in terms of comparing statistical properties and power law tail index behavior. The choice of the mentioned statistics is motivated by their properties when applied to the impulsive signals. The introduced methodology is illustrated by analysis of the real vibration signals acquired from healthy and faulty drive pulley bearing of the belt conveyor. The results provide a way to easily select an informative frequency band and distinguish between healthy and faulty component.