An Automatic Filtering Method Based on an Improved Genetic Algorithm—With Application to Rolling Bearing Fault Signal Extraction

Liao, Zhiqiang; Song, Liuyang; Peng, Chen; Zuo, Shuguang

doi:10.1109/jsen.2017.2738152

Cited by 31 publications

(20 citation statements)

References 25 publications

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“…Liu et al [143] proposed adaptive SR to detect bearing faults based on the quantum particle swarm algorithm. Liao et al [144] present an automatic filtering method based on improved genetic algorithm to extract fault signals and reduce the noise for fault type identification of rolling bearings.…”

Section: Other Fault Frequency Based Methodsmentioning

confidence: 99%

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

Wei

et al. 2019

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169

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Rotating machinery is widely applied in various types of industrial applications. As a promising field for reliability of modern industrial systems, early fault diagnosis (EFD) techniques have attracted increasing attention from both academia and industry. EFD is critical to provide appropriate information for taking necessary maintenance actions and thereby prevent severe failures and reduce financial losses. A massive amounts of research work has been conducted in last two decades to develop EFD techniques. This paper reviews and summarizes the research works on EFD of gears, rotors, and bearings. The main purpose of this paper is to serve as a guidemap for researchers in the field of early fault diagnosis. After a brief introduction of early fault diagnosis techniques, the applications of EFD of rotating machine are reviewed in two aspects: fault frequency-based methods and artificial intelligence-based methods. Finally, a summary and some new research prospects are discussed.

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Section: Other Fault Frequency Based Methodsmentioning

confidence: 99%

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

Wei

et al. 2019

Entropy

169

View full text Add to dashboard Cite

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“…e influence of the threshold will be described in the later section. So according to the table, Symptom Parameters 14,13,4,11,1,7,8,12,18,9,10, and 17 are dominant symptom parameters. Other Symptom Parameters 6, 3, 5, 16, 2, and 15 are labelled as insensitive parameters and should be removed from fault diagnosis data sets.…”

Section: Evaluation Criterion To Select the Dominant Symptommentioning

confidence: 99%

“…Before the symptom parameters calculation, signal filtering should be performed to decrease the noise influences. e GA filtering has been published in our previous works [1]. As shown in Figure 1(a), in the signal frequency domain, the fault signal and noise are other existence, and the high-pass, low-pass, and bandpass filtering do not work in this situation.…”

Section: Dominant Symptom Parameters Selection Scheme Of Bearing Faulmentioning

confidence: 99%

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Bearing Fault Dominant Symptom Parameters Selection Based on Canonical Discriminant Analysis and False Nearest Neighbor Using GA Filtering Signal

Zuo

Liao

2020

Mathematical Problems in Engineering

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Symptom parameter is a popular method for bearing fault diagnosis, and it plays a crucial role in the process of building a diagnosis model. Many symptom parameters have been performed to extract signal fault features in time and frequency domains, and the improper selection of parameter will significantly influence the diagnosis result. For dealing with the problem, this paper proposes a novel dominant symptom parameters selection scheme for bearing fault diagnosis based on canonical discriminant analysis and false nearest neighbor using GA filtered signal. The original signal was filtered by a genetic algorithm (GA) at first and then mapped to the new characteristic subspace through the canonical discriminant analysis (CDA) algorithm. The map distance in the new characteristic subspace is calculated by the false nearest neighbor (FNN) method to interpret the dominance of symptom parameters. The dominant symptom parameters brought to the bearing diagnosis system can improve the diagnosis result. The effectiveness of the proposed method has been demonstrated by the diagnosis model and by comparison with other methods.

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“…σ 1,i and σ 2,i are standard deviations of normal state and abnormal state, respectively. Moreover, in order to facilitate the calculation of the inverse fast Fourier transformation (FFT), we introduce a binary string inspired with [23]. Therefore, if DI i is bigger than the synthetic detection index (SDI), the according spectrum data is saved and assigned to a unit vector that has the same length with the according spectrum part.…”

Section: Basic Concept Of the Statistical Filtermentioning

confidence: 99%

Stepwise Intelligent Diagnosis Method for Rotor System with Sliding Bearing Based on Statistical Filter and Stacked Auto-Encoder

et al. 2020

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Since the raw signal collected from the sliding bearing is contaminated with background noise, and it is difficult to obtain high-precision results for the traditional methods due to the low signal-to-noise ratio (SNR). Therefore, a stepwise intelligent diagnosis method based on statistical filter and stacked auto-encoder (SAE) that is established with several auto-encoders is proposed to identify several faults of sliding bearing in a rotor system. Firstly, the statistical filter is utilized to reduce the interference information for the different abnormal states and to increase the SNR. Secondly, the stepwise intelligent diagnosis based on SAE is performed to learn the useful fault features, and it can automatically complete the fault diagnosis which is contributed with the superiority binary classification to fully mine the relationship between the fault characteristics and the health condition of bearing. Finally, the diagnosis of the oil whirl and structural faults in a rotor system is cited as an example to demonstrate the effectiveness of proposed method. It can effectively illustrate the advantages of the stepwise diagnosis method to obtain the maximum diagnostic accuracy.

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An Automatic Filtering Method Based on an Improved Genetic Algorithm—With Application to Rolling Bearing Fault Signal Extraction

Cited by 31 publications

References 25 publications

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

Bearing Fault Dominant Symptom Parameters Selection Based on Canonical Discriminant Analysis and False Nearest Neighbor Using GA Filtering Signal

Stepwise Intelligent Diagnosis Method for Rotor System with Sliding Bearing Based on Statistical Filter and Stacked Auto-Encoder

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