A summary of fault modelling and predictive health monitoring of rolling element bearings

El‐Thalji, Idriss; Jantunen, Erkki

doi:10.1016/j.ymssp.2015.02.008

Cited by 365 publications

(198 citation statements)

References 174 publications

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“…Scientists developed methods of condition monitoring of electrical motors and various devices [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. Many data processing methods (such as FFT, Wavelets, classifiers) were developed in the literature [21][22][23][24][25][26].…”

Section: Fig 1 Induction Motorsmentioning

confidence: 99%

Recognition of Acoustic Signals of Induction Motors with the Use of MSAF10 and Bayes Classfier

Głowacz¹

2016

Archives of Metallurgy and Materials

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Condition monitoring of deterioration in the metallurgical equipment is essential for faultless operation of the metallurgical processes. These processes use various metallurgical equipment, such as induction motors or industrial furnaces. These devices operate continuously. Correct diagnosis and early detection of incipient faults allow to avoid accidents and help reducing financial loss. This paper deals with monitoring of rotor electrical faults of induction motor. A technique of recognition of acoustic signals of induction motors is presented. Three states of induction motor were analyzed. Studies were carried out for methods of data processing: Method of Selection of Amplitudes of Frequencies (MSAF10) and Bayes classifier. Condition monitoring is helpful to protect induction motors and metallurgical equipment. Further researches will allow to analyze other metallurgical equipment.

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Section: Fig 1 Induction Motorsmentioning

confidence: 99%

Recognition of Acoustic Signals of Induction Motors with the Use of MSAF10 and Bayes Classfier

Głowacz¹

2016

Archives of Metallurgy and Materials

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“…However, failure mechanism changes with respect to failure evolution. Therefore, statistical methods cannot fully represent the wear process, especially in the case of wear evolution stages [10]. Artificial intelligence techniques are suitable for addressing the complex and large-scale nonlinear problems without any statistical assumptions about the data [11].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

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Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few comprehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition monitoring and fault diagnosis. The recent research and development of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are discussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mechanism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suitable method for a specific situation and pointing out potential research directions.

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“…Vibroacoustic signals are precisely related to characteristic, for these machines, parts or units operation frequencies such as shafts [5][6][7], gears [8], bearings [9][10][11][12] and so on. Thus, the measurement of for example absolute vibrations, apart from general evaluation of the technical state of examined machines, also allows us to identify irregularities in action of their individual components, caused by for instance unbalanced rotors, damaged bearings, gear tooth damage or misaligned couplings.…”

Section: Introductionmentioning

confidence: 99%

“…The choice of number and type of symptoms used to identify the technical state of the machine usually depends on its construction and particular purpose, technical conditions and the mounting options of transducers and measurement systems. In the simplest case, one feature may be assessed -for example the RMS value of velocity, acceleration or displacement of the vibrations, the level of acoustic emission or the amount of shock pulses [9]. In the majority of advanced diagnostic systems, recorded vibration signals are processed in order to determine a number of measures, which represent the monitored objects in a multidimensional-non-orthogonal space called symptom space [1].…”

Section: Introductionmentioning

confidence: 99%

The visualization of shaft vibration using the algorithm of phase-amplitude data interpolation

Piątkowski

2016

J. Appl. Math. Comput. Mech.

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Abstract. In many cases of observations of phenomena and physical processes we use data measured in multidimensional spaces. However, in practice, for the economic or technical reasons, we cannot obtain equally numerous data sets for all dimensions. In this case we can say that we are trying to assess or classify this process by using a few parts of its projection. For processing and computing of an input data a different solutions of interpolation and/or approximation are often used. But data, which have phase-amplitude nature, should be treated in a specific way. The lack of proper interpretation of phase shifts in many cases can result in obtaining the significant distortions of an analyzed process image. In this paper the algorithm of phase-amplitude interpolation is presented. This algorithm has been used for creating the contours charts, as projections of rotating shaft vibrations, in transient operating conditions of the machine.

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A summary of fault modelling and predictive health monitoring of rolling element bearings

Cited by 365 publications

References 174 publications

Recognition of Acoustic Signals of Induction Motors with the Use of MSAF10 and Bayes Classfier

Recognition of Acoustic Signals of Induction Motors with the Use of MSAF10 and Bayes Classfier

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

The visualization of shaft vibration using the algorithm of phase-amplitude data interpolation

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