Detection and identification of windmill bearing faults using a one-class support vector machine (SVM)

Saari, Juhamatti; Strömbergsson, Daniel; Lundberg, Jan; Thomson, Allan

doi:10.1016/j.measurement.2019.01.020

Cited by 93 publications

(33 citation statements)

References 15 publications

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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%

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A Bat-Optimized One-Class Support Vector Machine for Mineral Prospectivity Mapping

Yongliang

Zhao

2019

Minerals

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One-class support vector machine (OCSVM) is an efficient data-driven mineral prospectivity mapping model. Since the parameters of OCSVM directly affect the performance of the model, it is necessary to optimize the parameters of OCSVM in mineral prospectivity mapping. Trial and error method is usually used to determine the “optimal” parameters of OCSVM. However, it is difficult to find the globally optimal parameters by the trial and error method. By combining OCSVM with the bat algorithm, the intialization parameters of the OCSVM can be automatically optimized. The combined model is called bat-optimized OCSVM. In this model, the area under the curve (AUC) of OCSVM is taken as the fitness value of the objective function optimized by the bat algorithm, the value ranges of the initialization parameters of OCSVM are used to specify the search space of bat population, and the optimal parameters of OCSVM are automatically determined through the iterative search process of the bat algorithm. The bat-optimized OCSVMs were used to map mineral prospectivity of the Helong district, Jilin Province, China, and compared with the OCSVM initialized by the default parameters (i.e., common OCSVM) and the OCSVM optimized by trial and error. The results show that (a) the receiver operating characteristic (ROC) curve of the trial and error-optimized OCSVM is intersected with those of the bat-optimized OCSVMs and (b) the ROC curves of the optimized OCSVMs slightly dominate that of the common OCSVM in the ROC space. The area under the curves (AUCs) of the common and trial and error-optimized OCSVMs (0.8268 and 0.8566) are smaller than those of the bat-optimized ones (0.8649 and 0.8644). The optimal threshold for extracting mineral targets was determined by using the Youden index. The mineral targets predicted by the common and trial and error-optimized OCSVMs account for 29.61% and 18.66% of the study area respectively, and contain 93% and 86% of the known mineral deposits. The mineral targets predicted by the bat-optimized OCSVMs account for 19.84% and 14.22% of the study area respectively, and also contain 93% and 86% of the known mineral deposits. Therefore, we have 0.93/0.2961 = 3.1408 < 0.86/0.1866 = 4.6088 < 0.93/0.1984 = 4.6875 < 0.86/0.1422 = 6.0478, indicating that the bat-optimized OCSVMs perform slightly better than the common and trial and error-optimized OCSVMs in mineral prospectivity mapping.

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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%

A Bat-Optimized One-Class Support Vector Machine for Mineral Prospectivity Mapping

Yongliang

Zhao

2019

Minerals

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“…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%

Stacked Auto-Encoder Based CNC Tool Diagnosis Using Discrete Wavelet Transform Feature Extraction

Kim

Lee

Jeon³

et al. 2020

Processes

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Machining processes are critical and widely used components in the manufacturing industry because they help to precisely make products and reduce production time. To keep the previous advantages, a machine tool should be installed at the designated place and condition of the machine tool should be maintained appropriately to working environment. In various maintenance methods for keeping the condition of machine tool, condition-based maintenance can be robust to unpredicted accidents and reduce maintenance costs. Tool monitoring and diagnosis are some of the most important components of the condition based maintenance. This paper proposes stacked auto-encoder based CNC machine tool diagnosis using discrete wavelet transform feature extraction to diagnose a machine tool. The diagnosis model, which only uses cutting force data, cannot sufficiently reflects tool condition. Hence, we modeled diagnosis model using features extracted from a cutting force, a current signal, and coefficients of the discrete wavelet transform. The experimental results showed that the model which uses feature data has better performance than the model that uses only cutting force data. The feature based models are lower false negative rate (FNR) and false positive rate. Moreover, squared prediction error using normalized residual vector also reduced FNR because normalization reduces weight bias.

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“…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%

Gearbox fault diagnosis through quantum particle swarm optimization algorithm and kernel extreme learning machine

Meng¹,

Kang²,

Chi³

et al. 2020

J. vibroeng.

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Gearbox is the key component of mechanical transmission system. Accurate fault diagnosis of gearbox is of great significance to ensure the operation of rotating machinery. Based on the comprehensive simulation test-bed in the laboratory, a gearbox fault diagnosis method based on QPSO-KELM is proposed. Firstly, the fault pre planting experiments of gear fault, bearing fault and gear bearing mixed fault are carried out on the comprehensive simulation test-bed. Then, the vibration signals collected are preprocessed by TSA to eliminate noise. The time domain, frequency domain and NASA feature parameters of the preprocessed signals are taken as training samples and test samples of QPSO-KELM. The experimental results show that the proposed method can effectively solve the problem of gearbox fault pattern recognition, and the fault diagnosis accuracy is higher than traditional methods, so the research has certain reference significance and engineering application value.

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Detection and identification of windmill bearing faults using a one-class support vector machine (SVM)

Cited by 93 publications

References 15 publications

A Bat-Optimized One-Class Support Vector Machine for Mineral Prospectivity Mapping

A Bat-Optimized One-Class Support Vector Machine for Mineral Prospectivity Mapping

Stacked Auto-Encoder Based CNC Tool Diagnosis Using Discrete Wavelet Transform Feature Extraction

Gearbox fault diagnosis through quantum particle swarm optimization algorithm and kernel extreme learning machine

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