Rolling element bearing fault detection in industrial environments based on a K-means clustering approach

Yiakopoulos, Christos; Gryllias, Konstantinos; Antoniadis, Ioannis

doi:10.1016/j.eswa.2010.08.083

Cited by 203 publications

(101 citation statements)

References 21 publications

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“…In these cases, the data are unlabeled and the procedure tries to find hidden structure from the data. In [44], a K-means clustering approach is proposed, while in [45] a weighted local and global regressive mapping algorithm is proposed and compared with other unsupervised learning algorithms, such as locality preserving projection, Isomap, principal component analysis, and Sammon mapping.…”

Section: Index Terms-diagnosticmentioning

confidence: 99%

Robust Detection of Incipient Faults in VSI-Fed Induction Motors Using Quality Control Charts

García-Escudero

Duque-Pérez

Fernandez-Temprano

et al. 2017

IEEE Trans. on Ind. Applicat.

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Abstract-A considerable amount of papers has been published in recent years proposing supervised classifiers to diagnose the health of a machine. The usual procedure with these classifiers is to train them using data acquired through controlled experiments, expecting them to perform well on new data, classifying correctly the condition of a motor. But, obviously, the new motor to be diagnosed cannot be the same that has been used during the training process; it may be a motor with different characteristics and fed from a completely different source. These different conditions between the training process and the testing one can deeply influence the diagnosis. To avoid these drawbacks, in this paper, a new method is proposed, which is based on robust statistical techniques applied in quality control applications. The proposed method is based on the online diagnosis of the operating motor and can detect deviations from the normal operational conditions. A robust approach has been implemented using high-breakdown statistical techniques, which can reliably detect anomalous data that often cause an unexpected overestimation of the data variability, reducing the ability of standard procedures to detect faulty conditions in earlier stages. A case study is presented to prove the validity of the proposed approach. Motors of different characteristics, fed from the power line and several different inverters, are tested. Three different fault conditions are provoked: a broken bar, a faulty bearing, and mixed eccentricity. Experimental results prove that the proposed approach can detect incipient faults.

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Section: Index Terms-diagnosticmentioning

confidence: 99%

Robust Detection of Incipient Faults in VSI-Fed Induction Motors Using Quality Control Charts

García-Escudero

Duque-Pérez

Fernandez-Temprano

et al. 2017

IEEE Trans. on Ind. Applicat.

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“…( ) ( ) [13], [14], [15], [16] Skewness Skewness measures the asymmetry of probability density function (pdf) of vibration signal.…”

Section: Variancementioning

confidence: 99%

“…( ) ( ) [14], [15], [16], [17], [18] Kurtosis Kurtosis measures the degree of flatness of the probability density function (pdf) near its centre. In high speed bearing rotation, the kurtosis value of bearing normal is wellrecognized as 3.…”

Section: Variancementioning

confidence: 99%

An application of nonlinear feature extraction - A case study for low speed slewing bearing condition monitoring and prognosis

Caesarendra

Kosasih

Tieu

et al. 2013

2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics

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Moodie, C. (2013). An application of nonlinear feature extraction -a case study for low speed slewing bearing condition monitoring and prognosis. 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM) (pp. 1713-1718). United States: IEEE.An application of nonlinear feature extraction -a case study for low speed slewing bearing condition monitoring and prognosis AbstractThis paper presents the application of four nonlinear methods of feature extraction in slewing bearing condition monitoring and prognosis: these are largest Lyapunov exponent, fractal dimension, correlation dimension, and approximate entropy methods. Although correlation dimension and approximate entropy methods have been used previously, the largest Lyapunov exponent and fractal dimension methods have not been used in vibration condition monitoring to date. The vibration data of the laboratory slewing bearing testrig run at 1 rpm was acquired daily from February to August 2007 (138 days). As time progressed, a more accurate observation of the alteration of bearing condition from normal to faulty was obtained using nonlinear features extraction. These findings suggest that these methods provide superior descriptive information about bearing condition than time-domain features extraction, such as root mean square (RMS), variance, skewness and kurtosis.

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“…As a squared error-based clustering method, the K-means algorithm can not only be simply implemented in solving many practical problems but also can be applied directly to industrial environments without the need to be trained by data measured on a machine under a fault condition [20][21][22]. As an unsupervised method, K-means has been used to detect faults in rolling element bearing and used in the crack fault classification for planetary gearbox [23]. In addition, it has been used to investigate the best signals from force, electrical current, and electrical voltage for a condition monitoring system.…”

Section: Introductionmentioning

confidence: 99%

Machine Tool Volumetric Error Features Extraction and Classification Using Principal Component Analysis and K-Means

Xing

Mayer

Achiche

2018

JMMP

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Volumetric errors (VE) are related to the machine tool accuracy state. Extracting features from the complex VE data provides with a means to characterize this data. VE feature classification can reveal the machine tool accuracy states. This paper presents a study on how to use principal component analysis (PCA) to extract the features of VE and how to use the K-means method for machine tool accuracy state classification. The proposed data processing methods have been tested with the VE data acquired from a five-axis machine tool with different states of malfunction. The results indicate that the PCA and K-means are capable of extracting the VE feature information and classifying the fault states including the C axis encoder fault, uncalibrated C axis encoder fault, and pallet location fault from the machine tool normal states. This research provides a new way for VE features extraction and classification.

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Rolling element bearing fault detection in industrial environments based on a K-means clustering approach

Cited by 203 publications

References 21 publications

Robust Detection of Incipient Faults in VSI-Fed Induction Motors Using Quality Control Charts

Robust Detection of Incipient Faults in VSI-Fed Induction Motors Using Quality Control Charts

An application of nonlinear feature extraction - A case study for low speed slewing bearing condition monitoring and prognosis

Machine Tool Volumetric Error Features Extraction and Classification Using Principal Component Analysis and K-Means

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