Bayesian wavelet PCA methodology for turbomachinery damage diagnosis under uncertainty

Xu, Shengli; Jiang, Xiang; Huang, J. H.; Yang, Shuhua; Wang, Xiaofang

doi:10.1016/j.ymssp.2016.04.031

Cited by 27 publications

(21 citation statements)

References 24 publications

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“…Wang et al [2] proposed a PCA-based optimal sensor selection method for the condition monitoring of a distributed power generation system involving wind turbines. Xu et al [3] presented a Bayesian wavelet PCA methodology for turbomachinery damage diagnosis under uncertainty. Sattar et al [4] analyzed acoustic big data from ocean environments for fish sounds.…”

Section: Related Workmentioning

confidence: 99%

“…Samples from multiple sensors (including the 200 data sets and the 200 synchronous images for each class listed in Table 1) were used for pattern detection. For detecting class D, the indicator and edge descriptors were set to 3 and { 4 , 5 , 6 , 7 }; and were obtained from the PCAbased and the ARG-EFD-based algorithm, respectively, for class D samples; the SVM classification of both = 3 and = { 4 , 5 , 6 , 7 } showed that the samples belonged to class D. The procedure was complete for a given sample when no data set and image remained in the data/image queue. and were reset, and other samples were detected similarly.…”

Section: Detectionmentioning

confidence: 99%

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PCA/SVM-Based Method for Pattern Detection in a Multisensor System

Lin

2018

Mathematical Problems in Engineering

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This paper presents a multivariate analysis framework for pattern detection in a multisensor system; the proposed principal component analysis (PCA)/support vector machine-(SVM-) based supervision scheme can identify patterns in the multisensory system. Although the PCA and SVM are commonly used in pattern recognition, an effective methodology using the PCA/SVM for multisensory system remains unexplored. Pattern detection in a multisensor system has long been a challenge. For example, object inspections in multisensor systems are difficult to perform because inspectors might fail to use multiple sensing devices when concurrently detecting different patterns. Therefore, to resolve this issue, this study proposes a novel framework for establishing indicators and corresponding thresholds to identify patterns in the system; it employs a feature-based scheme that integrates principal component analysis (PCA) with an SVM for effectively detecting patterns in the system. Experiments were conducted using a tactile and optical measurement system. The experimental results demonstrated that the proposed method can effectively identify patterns in multisensor systems by using a feature-based algorithm that combines PCA and SVM classification for detecting various patterns. Moreover, the proposed framework established alarm indicators and corresponding thresholds that can be used for pattern detection.

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Section: Related Workmentioning

confidence: 99%

Section: Detectionmentioning

confidence: 99%

PCA/SVM-Based Method for Pattern Detection in a Multisensor System

Lin

2018

Mathematical Problems in Engineering

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“…PCA [9,10] is a dimension reduction technique which is a statistical analysis method, using the correlation matrix to analyze the relationship between variables, and eventually using several factors to represent many original factors. The contribution rate of each principal component reflects the percentage of the original variable information of each principal component, and that determines the proportion of the principal component in the final evaluation value.…”

Section: Principal Component Analysismentioning

confidence: 99%

Decision-making Research of the Urban Design Competitiveness Based on Principal Component Analysis

Yang¹,

An²

2017

dtssehs

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Abstract. This paper uses principal component analysis (PCA) to explore decision-making of urban design competitiveness (UDC) which enhances the objectivity of decision-making and provide theoretical reference for the urban design competitiveness. First of all, we pan-elect the indexes of competitiveness of the urban design competitiveness according to study the present situation of domestic and foreign research and competitiveness index system. Secondly, we construct evaluation index system of urban design competitiveness by using the system principle, representative principle and operation principle. And then a comprehensive evaluation function is got by establishing the evaluation matrix, calculating the correlation coefficient matrix of the matrix and getting the contribution rate of the principal component. Finally, we use the score of Beijing, Shanghai and Guangzhou to assign to validate the correctness of decision-making. To some extent, the research can help the relevant departments to better plan the development process of the design industry, and it is of great help to improve design industry competitiveness.

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“…Additionally, neural network was further used as a classifier to categorize the bearing faults. To investigate the fault diagnosis of impeller in centrifugal compressor, PCA was also adopted to decrease the dimensionality of multiple time series by Jiang's group [7]. Sun et al [8] analyzed the defects of conventional fault diagnosis methods and introduced the data mining technology into fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Feature Frequency Extraction Based on Principal Component Analysis and Its Application in Axis Orbit

Zhao

2018

Shock and Vibration

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Vibration-based diagnosis has been employed as a powerful tool in maintaining the operating efficiency and safety for large rotating machinery. However, the extraction of malfunction features is not accurate enough by using traditional vibration signal processing techniques, owing to their intrinsic shortcomings. In this paper, the relationship between effective eigenvalues and frequency components was investigated, and a new characteristic frequency separation method based on PCA (CFSM-PCA) was proposed. Certain feature frequency could be purified by reconstructing the specified eigenvalues. Furthermore, three significant perspectives were studied via the distribution of effective eigenvalues, and theoretical derivations were subsequently illustrated. More importantly, this proposed scheme could also be used to synthesize axis orbits of larger machines. Purified curves were so explicit and the CFSM-PCA exhibited higher efficiency than harmonic wavelet and wavelet packet.

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Bayesian wavelet PCA methodology for turbomachinery damage diagnosis under uncertainty

Cited by 27 publications

References 24 publications

PCA/SVM-Based Method for Pattern Detection in a Multisensor System

PCA/SVM-Based Method for Pattern Detection in a Multisensor System

Decision-making Research of the Urban Design Competitiveness Based on Principal Component Analysis

Feature Frequency Extraction Based on Principal Component Analysis and Its Application in Axis Orbit

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