An Adaptive Spectral Kurtosis Method Based on Optimal Filter

Yang, Yanli; Yu, Ting

doi:10.1155/2017/6987250

Cited by 5 publications

(5 citation statements)

References 18 publications

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“…In [ 47 ], the authors presented a method that uses SK in an adaptive way. Unfortunately, in this method, the aspect of adaptiveness was not considered in the time domain; therefore, the method is time-invariant and is aimed to help establish an informative frequency band in the signal based on the local maxima of SK.…”

Section: Discussionmentioning

confidence: 99%

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

Wodecki

2021

Sensors

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Vibration-based local damage detection in rotating machines (i.e., rolling element bearings) is typically a problem of detecting low-energy cyclic impulsive modulations in the measured signal. This can be challenging as both the amplitude of a single damage-related impulse and the distance between impulses might be changing in time. From the signal processing point of view, this means time varying regarding the the signal-to-noise ratio, location of information in the frequency domain, and loss of periodicity (this remains cyclic but not periodic). One of the many attempted approaches to this problem is filtration using custom filters derived in a data-driven fashion. One of the methods to obtain such filters is a selector approach, where the value of a certain statistic is calculated for individual frequency bands of a signal that results in the magnitude response of a filter. In this approach, each chosen statistic will yield different results, and the obtained filter will be focused on different frequency bands focusing on different behaviors. One of the most popular and powerful selectors is spectral kurtosis as popularized by Antoni, which uses kurtosis as an operational statistic. Unfortunately, after closer inspection, it is easy to notice that, although selectors can significantly enhance the signal, they accumulate a great deal of noise and other background content of signals, which occupies the space (or rather time) in between the impulses. Another disadvantage is that such filters are time-invariant, because, in the principle of their construction, they are not adaptive, and even slight changes in the signal yield suboptimal results either by missing relevant data when the conditions in the signal change (i.e., informative impulses widen in bandwidth), or by accumulating unnecessary noise when the relevant information is not present (in between impulses or in frequency bands that impulses no longer occupy). To address this issue, I propose generalization of the selector approach using the example of spectral kurtosis. This assumes creating a time-varying selector that can be seen as a spatial filter in the time-frequency domain. The time-varying SK (TVSK) is estimated for segments of the signal, and, instead of a vector of SK-based filter coefficients, one obtains a TVSK-based matrix of coefficients that takes into account the time-varying properties of the signal. The obtained structure is then binarized and used as a filter. The presented method is tested using a simulated signal as well as two real-life signals measured on heavy-duty bearings in two different types of machine.

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Section: Discussionmentioning

confidence: 99%

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

Wodecki

2021

Sensors

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“…is paper came up with a rolling bearing diagnosis approach based on ARLW filtering with the help of WCA, which can detect the fault information by demodulation analysis of the determined resonance frequency band. e proposed approach has three improvements: (1) ARLW is used as a bandpass filter to process the initial signal, which can filter out noise efficiently and extract more fault information; (2) WCA is employed to optimize the ARLW parameters in parallel adaptively, which can avoid artificial interference and improve robustness; (3) e proposed SEFER index can evaluate the quality of the bandpass filters constructed for different fault types. e proposed approach and the traditional resonance demodulation methods, such as EEMD-FSK approach and WPT-FSK approach, are all conducted to analyze the simulation signal, the artificial single fault signal, the artificial compound fault signal, and the life cycle fault signal.…”

Section: Discussionmentioning

confidence: 99%

“…Rolling bearing is broadly used in rotating equipment, and its fault acts on the safe operation of the whole equipment [1][2][3]. At the beginning of the rolling bearing fault, the impact component of vibration signal collected by the sensor is weak and often submerged in strong background noise, bringing challenges to the diagnostic process [4].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Asymmetric Real Laplace Wavelet Filtering and Its Application on Rolling Bearing Early Fault Diagnosis

Wan

Peng

2019

Shock and Vibration

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The early fault of rolling bearing is weak and may not be readily detected. To overcome this issue, the present paper comes up with a rolling bearing fault-diagnosing approach based on adaptive asymmetric real Laplace wavelet (ARLW) filtering, which is on the strength of water cycle optimization algorithm (WCA). Firstly, ARLW is introduced to filter the initial vibration signal since its waveform has the same asymmetric structure as the fault impact. Secondly, the optimum center frequency and bandwidth of ARLW is found out adaptively by applying the WCA through the proposed square envelope fault energy ratio (SEFER). Finally, envelope analysis is conducted to the narrowband signal obtained by the optimum ARLW filtering, and its envelope spectrum presents the rolling bearing fault characteristic frequency apparently. The proposed approach and two existing approaches are all tested in four signal analysis cases. The results are analyzed, and the conclusion is that the approach proposed by the present paper can detect the early fault of rolling bearing more accurately. The present research is valuable for diagnosing the early fault of rolling bearing.

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“…Consider a simulation signal from a rolling element bearing. The signal is written as [23] Fig. 2 Flowchart of the proposed algorithm…”

Section: Analysis Of Simulation Signalsmentioning

confidence: 99%

“…Consider a simulation signal from a rolling element bearing. The signal is written as [23]

\begin{matrix} right leftthickmathspace.5em \\ y (t) & = p (t) + x (t) \\ = \underset{i}{false\sum} A_{i} e^{- β false(t - i T_{p} - τ_{i} false)} \sin ω_{r} false(t - i T_{p} - τ_{i} false) u false(t - i T_{p} - τ_{i} false) \\ + \underset{l}{false\sum} A_{l} \sin false(2 π f_{l} t false) \end{matrix}

where

A_{i}

is the amplitude of the fault impulse,

β

denotes the structural damping characteristic,

T_{p}

denotes the time period corresponding to the fault characteristic frequency,

τ_{i}

represents the effect of random slippage of the rollers,

ω_{r}

denotes the excited resonance frequency,

u false(t false)

is a unit step function and

x false(t false) = \sum_{l} A_{l} \sin (2 π f_{l} t)

is the harmonic interference. Some parameters are listed in Table 1.…”

Section: Experiments and Analysismentioning

confidence: 99%

Modulated signal detection method for fault diagnosis

Yang

2020

IET Science, Measurement &Amp; Technology

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Envelope analysis is a dominant approach in detecting modulated fault signals in rotating machinery. The Hilbert transform is a popular method used to obtain envelopes. However, envelopes based on the Hilbert transform are only suitable for narrowband signals. Signals are usually filtered before envelope demodulation, which leads to the challenging issue of filter selection. A modulated fault signal detection method using the upper and lower envelopes of the signals is proposed. This method can demodulate the modulated fault signals directly from the raw data, which can include wideband and narrowband signals. The proposed method uses a simple algebraic operation instead of a transform function, so it is convenient in practical applications. It is tested with some simulated signals and some real-measured data. The test results show that the method can effectively demodulate a modulated fault signal directly from the raw data.

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An Adaptive Spectral Kurtosis Method Based on Optimal Filter

Cited by 5 publications

References 18 publications

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

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

Adaptive Asymmetric Real Laplace Wavelet Filtering and Its Application on Rolling Bearing Early Fault Diagnosis

Modulated signal detection method for fault diagnosis

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