A novel Pareto-based Bayesian approach on extension of the infogram for extracting repetitive transients

Gu, Xiaohui; Yang, Shaopu; Liu, Yongqiang; Hao, Rujiang

doi:10.1016/j.ymssp.2017.12.034

Cited by 20 publications

(13 citation statements)

References 28 publications

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“…In AFECGP, eight terminals are employed in the terminal set as listed in Table II. The Signal terminal represents the input raw signal, which is a 1-D array with a length of m. The n terminal represents the order of the KthM function, which is an integer in the range of [2,4].…”

Section: Terminal Setmentioning

confidence: 99%

“…Decomposing the vibration signals into multiple subband signals can reduce the difficulty of analysis. Wavelet Transform and Wavelet Packet Transform can decompose signals into a set of wavelet details, but the performance is affected by the predefined wavelet basis and levels [4]. The recursive decomposition methods on the basis of the signal waveforms, such as Empirical Mode Decomposition (EMD), Intrinsic Time-scale Decomposition, Local Mean Decomposition, and their improved versions [5], [6], [7], [8], [9], [10], have been developed and used to decompose signals.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Feature Extraction and Construction Using Genetic Programming for Rotating Machinery Fault Diagnosis

Peng

Wan

et al. 2021

IEEE Trans. Cybern.

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Feature extraction is an essential process in the intelligent fault diagnosis of rotating machinery. Although existing feature extraction methods can obtain representative features from the original signal, domain knowledge and expert experience are often required. In this paper, a novel diagnosis approach based on evolutionary learning, namely automatic feature extraction and construction using genetic programming (AFECGP), is proposed to automatically generate informative and discriminative features from original vibration signals for identifying different fault types of rotating machinery. To achieve this, a new program structure, a new function set and a new terminal set are developed in AFECGP to allow it to detect important subband signals, extract and construct informative features, automatically and simultaneously. More importantly, AFECGP can produce a flexible number of features for classification. Having the generated features, k-Nearest Neighbors is employed to perform fault diagnosis. The performance of the AFECGP based fault diagnosis approach is evaluated on four fault diagnosis datasets of varying difficulty and compared with fourteen baseline methods. The results show that the proposed approach achieves better fault diagnosis accuracy on all the datasets than the competitive methods and can effectively identify different fault conditions of rolling bearing, gear, and rotor.

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Section: Terminal Setmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Feature Extraction and Construction Using Genetic Programming for Rotating Machinery Fault Diagnosis

Peng

Wan

et al. 2021

IEEE Trans. Cybern.

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“…In order to demonstrate the noise immunity and anti-periodic interference of the Mkurtogram, a simulated out race bearing fault signal with the is formed with Gaussian white noise, abnormal impulsive noise and gear meshing harmonic interferences. In this case, the above mentioned multiple interferences are added to the simulated signal referring to [33,34]. Then, the simulated bearing signal can be modeled as:…”

Section: Case 1: a Numerical Experiments With Multiple Interferencesmentioning

confidence: 99%

“…After that, some excellent improvements were proposed to extend the original Infogram. The pareto optimal solution [33] and a grey strategy [34] have been utilized to balance the contributions due to the spectral negentropys generated from two evaluation domains, differ from simply synthesize by the equal weight adopted in original Infogram. Nonetheless, the above-improved methods still inherit the drawbacks that the negentropy of the squared envelope is prone to be disturbed by the large impulse interference and the negentropy of the SES is easily affected by discrete harmonic components.…”

Section: Introductionmentioning

confidence: 99%

The Mkurtogram: A Novel Method to Select the Optimal Frequency Band in the AC Domain for Railway Wheelset Bearings Fault Diagnosis

Liu

Yang

et al. 2020

Applied Sciences

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A wheelset bearing is one of the main components of the train bogie frame. The early fault detection of the wheelset bearing is quite important to ensure the safety of the train. Among numerous diagnostic methods, envelope analysis is one of the most effective approaches in the detection of bearing faults which has been amply applied, but its validity greatly depends on the informative frequency band (IFB) determined. For the wheelset bearing faulty signal, it is often difficult to identify the IFB and extract fault characteristics due to the influence of complex operating conditions. To address this problem, a novel method to select optimal IFB, called the Mkurtogram, is proposed for railway wheelset bearings fault diagnosis. It takes the multipoint kurtosis (Mkurt) of unbiased autocorrelation (AC) of the squared envelope signal generated from sub-bands as assessment indicator for the first time. The fundamental concept which inspires this proposed method is to make full use of regular periodicity of AC of squared envelope signal. In the AC domain, the impulsiveness and periodicity, two distinctive signatures of the repetitive transients, have achieved a united representation by Mkurt. A simulated signal with multiple interferences and two experimental signals collected from wheelset bearings are applied to verify its performances and advantages. The results indicate that the proposed method is more effective to extract the wheelset bearings fault feature under complex interferences. It can not only decrease the influence of large impulse interference and the discrete harmonics interference, but also effectively overcome the influence of amplitude fluctuation caused by variable working conditions. Moreover, based on the periodic directivity of Mkurt, the proposed method also can be applied to the compound faults diagnosis of the wheelset bearing.

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“…The newly proposed infogram by Antoni [23] intended to capture the signature of repetitive transients in both time and frequency domains, which extended the domain of applicability of the kurtogram significantly. Gu et al [24] combined the infogram and a novel Pareto-based Bayesian approach to extract repetitive transients. Other improved indexes were also put forward, such as protrugram, which was first time proposed by Barszcz and JabŁoński [25] as an alternative to kurtogram.…”

Section: Introductionmentioning

confidence: 99%

Impulsive Gear Fault Diagnosis Using Adaptive Morlet Wavelet Filter Based on Alpha-Stable Distribution and Kurtogram

2019

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The vibration signals extracted from gearbox are usually masked in heavy background noise. Analyzing the feature components of these signals is challenging and crucial to incipient fault diagnosis. The Kurtogram has been verified as a very powerful and practical tool in the mechanical fault diagnosis due to its advantages in detecting and characterizing transients in signals. However, the accuracy of the kurtogram is limited because it is based on short-time Fourier transform (STFT) or FIR filter in extracting transient characteristics from a noisy signal. Therefore, it is necessary to develop a more accurate filter to overcome its shortcomings and further improve its fault detection accuracy. Wavelet transform has been widely applied in the past as a powerful tool to analyze the non-stationary signals, and an alpha-stable distribution model is often used to describe the statistical features of non-Gaussian signals. In this paper, a Morlet wavelet filter is optimized based on the kurtogram and the alpha parameter of the alpha-stable distribution model. Through the analysis of simulation signals at different fault degrees and operating conditions, it can be concluded that alpha-stable distribution has better performance than kurtosis in measuring the non-Gaussian characteristics of an impulsive gear fault signal. The results obtained from the simulation and practical experiments confirm the superiority of the proposed method for incipient gear fault diagnosis. INDEX TERMS Incipient fault diagnosis, kurtogram, Morlet wavelet, adaptive filter, alpha-stable distribution.

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A novel Pareto-based Bayesian approach on extension of the infogram for extracting repetitive transients

Cited by 20 publications

References 28 publications

Automatic Feature Extraction and Construction Using Genetic Programming for Rotating Machinery Fault Diagnosis

Automatic Feature Extraction and Construction Using Genetic Programming for Rotating Machinery Fault Diagnosis

The Mkurtogram: A Novel Method to Select the Optimal Frequency Band in the AC Domain for Railway Wheelset Bearings Fault Diagnosis

Impulsive Gear Fault Diagnosis Using Adaptive Morlet Wavelet Filter Based on Alpha-Stable Distribution and Kurtogram

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