Correlation Coefficient of Simplified Neutrosophic Sets for Bearing Fault Diagnosis

Shi, Lei

doi:10.1155/2016/5414361

Cited by 17 publications

(10 citation statements)

References 27 publications

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“…Regarding intervalvalued neutrosophic hesitant fuzzy sets, Ye (2016) also put forward their correlation coefficients for MDM problems. Based on simplified neutrosophic sets (SNSs) (Ye, 2014b) implying SvNSs and IvNSs, Shi (2016) proposed the correlation coefficient of SNSs and applied it to the vibration fault diagnosis of rolling bearing with SNS information. Şahin and Liu (2017) presented a single-valued neutrosophic correlation coefficient for MDM problems.…”

Section: Introductionmentioning

confidence: 99%

Q-Indeterminate Correlation Coefficient Between Simplified Neutrosophic Indeterminate Sets and Its Multicriteria Decision-Making Method

Rui

et al. 2021

Journal of Civil Engineering and Management

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Owing to the indeterminacy, incompleteness, and inconsistency of decision makers’ arguments/cognitions regarding complicated decision-making problems, the truth, falsity, and indeterminacy degrees given by decision makers may imply the partial certainty and partial uncertainty information. In this case, a simplified neutrosophic set (SNS) cannot express the uncertainty degrees of the truth, falsity, indeterminacy arguments. To depict the hybrid information of SNS and neutrosophic (indeterminate) numbers (NNs) together, this study presents a simplified neutrosophic indeterminate set (SNIS) to describe the uncertainty degrees of the truth, falsity, indeterminacy, and then based on the de-neutrosophication technology using the parameterized SNSs of SNISs we introduce the q-indeterminate correlation coefficients of SNISs with a parameter q ∈ [0, 1]. Next, a simplified neutrosophic indeterminate multicriteria decision-making method using the qindeterminate correlation coefficients of SNISs is established along with decision makers’ risk attitudes, such as the small risk for q = 0, the moderate risk for q = 0.5, and the large risk for q = 1, to carry out multicriteria decision-making problems in SNIS setting. Eventually, the proposed decision-making approach is applied in an example of selecting a satisfactory slope design scheme for an open pit mine to indicate the practicality and flexibility in SNIS setting.

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Section: Introductionmentioning

confidence: 99%

Q-Indeterminate Correlation Coefficient Between Simplified Neutrosophic Indeterminate Sets and Its Multicriteria Decision-Making Method

Rui

et al. 2021

Journal of Civil Engineering and Management

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“…On the other hand, correlation coefficient finds the relationship between variables. Shi et al [41] performed bearing fault diagnosis using correlation coefficient and simplified neutrosophic sets. Despite the good discriminant information extraction capabilities and low computational cost of cross-correlation, to the best of our knowledge, preprocessing the time-domain vibration signal using cross-correlation for CP fault diagnosis has not been reported so far.…”

Section: Introductionmentioning

confidence: 99%

Discriminant Feature Extraction for Centrifugal Pump Fault Diagnosis

et al. 2020

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Raw statistical features can imitate the amplitude, average, energy and time, and frequency series distribution of a raw vibration signal. However, these raw statistical features are either not very sensitive to weak incipient faults or are unsuitable for more severe faults, thus affecting the fault detection and classification accuracy. To tackle this problem, this paper proposes a discriminant feature extraction method for Centrifugal Pump (CP) fault diagnosis. In order to obtain the discriminant feature pool, the proposed method is divided into three phases. In the first phase, a healthy baseline signal is selected. In the second phase, the healthy baseline signal is cross-correlated with the CP vibration signals of different classes, and a set of new features are extracted from the resulting correlation sequence. In the third phase, raw hybrid features in time, frequency, and the time-frequency domain are extracted from both the healthy baseline signals and the CP vibration signals of different classes. The correlation coefficient is calculated between the raw hybrid feature pools, which results in a new set of discriminant features. Discriminant features help the machine learning classifiers to effectively detect and classify the data into its respective classes. Furthermore, the proposed method combines all these features into a single feature vector that forms a vulnerable feature pool. The vulnerable feature pool describes the CP's vulnerability to a fault and is provided as an input to a multiclass support vector machine (MSVM) for CP fault detection and classification. The experimental results illustrate that the accuracy obtained from the proposed method shows promising improvements over the state-of-the-art conventional methods.

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“…Cui et al [8] proposed a rolling bearing fault diagnosis method based on local and correlation analysis and carried out feature selection by calculating intrinsic mode function (IMF) correlations. Shi [9] proposed a new correlation coefficient of simplified neutrosophic sets (SNSs) to diagnose bearing fault types and extract the feature vector corresponding to the energy feature value of each frequency band for fault feature extraction. Zheng et al [10] proposed a new rolling bearing fault diagnosis method based on multi-scale fuzzy entropy (MFE), Laplacian score (LS) and variable predictive model-based class discrimination (VPMCD).…”

Section: Introductionmentioning

confidence: 99%

A Bearing Fault Diagnosis Method Based on Feature Selection Feedback Network and Improved D-S Evidence Fusion

Tang

Wang

et al. 2020

IEEE Access

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Bearings running state affects the normal operation of mechanical equipment. It is of great theoretical and practical value to carry out bearing fault diagnosis. In bearing fault diagnosis research, the extraction and selection of fault features can help improving the accuracy of bearing fault diagnosis. However, these researches suffer from the following weaknesses. (1) High dimension of the selected features.(2) Uncertainty of single sensor for data sampling. Therefore, in this paper, a feature selection feedback network (FSFN) is proposed to overcome the first weakness. At the same time, we proposed an improved Dempster-Shafer (IDS) evidence theory fusion method based on the kappa coefficient to deal with the second weakness. Extensive evaluations of the proposed method on the CUT-2 experimental platform dataset showed that FSFN can not only reduce the dimension of the final selected feature without decreasing the diagnostic accuracy but also shorten the time of feature selection. Moreover, compared with the existing DS evidence theory fusion method, IDS can achieve higher average fusion precision and improve the accuracy and reliability of bearing fault diagnosis.

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Correlation Coefficient of Simplified Neutrosophic Sets for Bearing Fault Diagnosis

Cited by 17 publications

References 27 publications

Q-Indeterminate Correlation Coefficient Between Simplified Neutrosophic Indeterminate Sets and Its Multicriteria Decision-Making Method

Q-Indeterminate Correlation Coefficient Between Simplified Neutrosophic Indeterminate Sets and Its Multicriteria Decision-Making Method

Discriminant Feature Extraction for Centrifugal Pump Fault Diagnosis

A Bearing Fault Diagnosis Method Based on Feature Selection Feedback Network and Improved D-S Evidence Fusion

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