Novel adaptation of the spectral kurtosis for vibration diagnosis of gearboxes in non-stationary conditions

Abstract

In this paper, the adaptation of spectral kurtosis technology is proposed, demonstrated and experimentally validated. Raw data signals were collected from a single-stage gearbox run in different combinations of speed and load, after which time synchronous averaging was used to leave the classical residual signal once meshing harmonics were removed. Each data file is split into many individual realisations based on the time taken for the time synchronous average to converge on stable values, after which the short-time Fourier transform is used to calculate the spectral kurtosis for each realisation. The effects of adapting spectral kurtosis technology parameters such as the resolution and threshold used in creating a Wiener filter are evaluated, showing the effects on the consistent frequency bands identified throughout the realisations. Taking a baseline set of processing parameters, the probability of correct diagnosis was calculated using a three-stage decision-making technique incorporating the k-nearest neighbour and cluster analysis methods. Adaptation of the spectral kurtosis technology is then shown to dramatically improve the probability of correct diagnosis, highlighting that each speed and load case requires different resolution and threshold values to return the optimal results.

Optimal SES Selection Based on SVD and Its Application to Incipient Bearing Fault Diagnosis

“…Due to the fast computation efficiency and excellent performance to indicate impulsiveness in the vibration signal, kurtosis is commonly regarded as an object function for fault diagnosis [19]. Moreover, kurtosis-based indexes have been employed to enhance the available techniques in rolling fault diagnosis, such as spectral kurtosis [20,21], kurtogram [22], and protrugram [23]. e protrugram method, proposed by Barszcz and JabŁoński [23], utilizes kurtosis of envelope spectrum amplitudes to describe the cyclostationarity and further selects optimal resonant frequency band as well as to detect transient impulses with vibration signal in low signal-tonoise ratios.…”

Time-Varying Spectral Kurtosis: Generalization of Spectral Kurtosis for Local Damage Detection in Rotating Machines under Time-Varying Operating Conditions

“…Researchers have already attempted this as a base for analysis; however, the approach and purpose were completely different and were not related to multidimensional filtration. Gelman et al proposed to use this approach to statistically assess the presence of constant carrier frequencies that are not changing over time and consistently raise the kurtosis value, so that the frequencies detected this way can be cross-referenced with the frequencies of rotation that are typical for particular machine elements [ 45 ]. In the context of the method proposed in this paper, this effect can be easily achieved by setting a significantly longer time window for TVSK, which will become apparent later in the article.…”

Fault Diagnosis of Rotating Machinery: A Review and Bibliometric Analysis

“…The concept of entropy is introduced in feature extraction to express the nonlinear and non-stationary dynamic characteristics of vibration signals. Many methods, such as Shannon entropy [154], approximate entropy [155], sample entropy [156], fuzzy entropy [157], and multi-scale entropy [158], have been integrated. Permutation entropy was proposed by Bandt and Pompe [159] to measure the complexity of time series.…”