2019
DOI: 10.1016/j.measurement.2019.01.020
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Detection and identification of windmill bearing faults using a one-class support vector machine (SVM)

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Cited by 96 publications
(33 citation statements)
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“…As a result, the value range of the anomaly score is enlarged. In order to make the anomaly scores generated by different OCSVM models have the same data change interval, the anomaly scores generated by OCSVM can be further transformed as follows: (11) wheref (x) is the transformed anomaly score, min f (x) and max f (x) are the minimum and maximum values of f (x). The transformed anomaly scoref (x) is between 0 and 1.…”
Section: Mineral Target Extractionmentioning
confidence: 99%
See 1 more Smart Citation
“…As a result, the value range of the anomaly score is enlarged. In order to make the anomaly scores generated by different OCSVM models have the same data change interval, the anomaly scores generated by OCSVM can be further transformed as follows: (11) wheref (x) is the transformed anomaly score, min f (x) and max f (x) are the minimum and maximum values of f (x). The transformed anomaly scoref (x) is between 0 and 1.…”
Section: Mineral Target Extractionmentioning
confidence: 99%
“…Roodposhti et al established an OCSVM model to map drought sensitivity in atmospheric researches [10]. Saari et al established an OCSVM model to detect windmill bearing faults [11]. Harrou et al established an OCSVM model to detect anomalies in photovoltaic systems [12].…”
Section: Introductionmentioning
confidence: 99%
“…Furthermore, the model-based technique was being transformed into the data-driven technique by that computation power has been increasing for more than ten years [12]. There are various data-driven methods for system diagnosis, such as principal component analysis [13], support vector machine [14], nearest prototype classifier [15], k-means clustering [16] and neural network [17], etc. In last few years, several articles have been devoted to the study of system diagnosis using deep learning [18][19][20][21].…”
Section: Introductionmentioning
confidence: 99%
“…As the most mainstream classification method, intelligent classification algorithm has achieved good results in the field of equipment fault diagnosis. Common intelligent classification algorithms include neural network [1,2], support vector machine (SVM) [3][4][5], kernel extreme learning machine (KELM) [6,7], deep learning [8][9][10] and other methods. Wang [2] proposed a fault diagnosis method based on RDGWPR-MSE and PNN, which is used to realize the automatic fault identification of electric submersible pump.…”
Section: Introductionmentioning
confidence: 99%
“…Wang and Yan [4] used the energy of IMF component after SVD decomposition as feature parameters to input and train SVM model, so as to realize bearing fault diagnosis. Saari et al [5] proposed a fault diagnosis method for wind turbine bearing based on one class SVM. Iosifidis A et al [7] studied the classification method of KELM, and achieved good results.…”
Section: Introductionmentioning
confidence: 99%