Using bispectral distribution as a feature for rotating machinery fault diagnosis

Jiang, Lingli; Liu, Yilun; Li, Xuejun; Tang, Siwen

doi:10.1016/j.measurement.2011.03.024

Cited by 41 publications

(22 citation statements)

References 15 publications

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“…As shown in this table, [22][23][24][25]. In this study, ROC curves and AUC values are calculated by using TP, TN, FP and FN are true positives, true negatives, false positives and false negatives, respectively [22][23][24][25][26][27].…”

Section: Obtained Resultsmentioning

confidence: 99%

“…The iterative process is finished when some conditions (for example, number of individuals in the population, etc.) are satisfied [24].…”

Section: Genetic Algorithmmentioning

confidence: 97%

“…To obtain a good classification performance of ELM, these parameters should be determined properly. In here, the hepatitis diseases database is used [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

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An Automatic Diagnosis System for Hepatitis Diseases Based on Genetic Wavelet Kernel Extreme Learning Machine

Avcı¹

2016

Journal of Electrical Engineering and Technology

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-Hepatitis is a major public health problem all around the world. This paper proposes an automatic disease diagnosis system for hepatitis based on Genetic Algorithm (GA) Wavelet Kernel (WK) Extreme Learning Machines (ELM). The classifier used in this paper is single layer neural network (SLNN) and it is trained by ELM learning method. The hepatitis disease datasets are obtained from UCI machine learning database. In Wavelet Kernel Extreme Learning Machine (WK-ELM) structure, there are three adjustable parameters of wavelet kernel. These parameters and the numbers of hidden neurons play a major role in the performance of ELM. Therefore, values of these parameters and numbers of hidden neurons should be tuned carefully based on the solved problem. In this study, the optimum values of these parameters and the numbers of hidden neurons of ELM were obtained by using Genetic Algorithm (GA). The performance of proposed GA-WK-ELM method is evaluated using statical methods such as classification accuracy, sensitivity and specivity analysis and ROC curves. The results of the proposed GA-WK-ELM method are compared with the results of the previous hepatitis disease studies using same database as well as different database. When previous studies are investigated, it is clearly seen that the high classification accuracies have been obtained in case of reducing the feature vector to low dimension. However, proposed GA-WK-ELM method gives satisfactory results without reducing the feature vector. The calculated highest classification accuracy of proposed GA-WK-ELM method is found as 96.642 %.

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Section: Obtained Resultsmentioning

confidence: 99%

“…The iterative process is finished when some conditions (for example, number of individuals in the population, etc.) are satisfied [24].…”

Section: Genetic Algorithmmentioning

confidence: 97%

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An Automatic Diagnosis System for Hepatitis Diseases Based on Genetic Wavelet Kernel Extreme Learning Machine

Avcı¹

2016

Journal of Electrical Engineering and Technology

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“…For automatic detection of technical condition of the The purpose of the introduced neural network is to carry out automatic detection of technical condition of the electric drive without involvement of the specialist expert [7][8][9][10][11][12][13][14][15][16][17][18][19]. As basic data values, wavelet coefficients at a scale are used ( fig.…”

Section: Identification Of Neural Network For Status Classificationmentioning

confidence: 99%

Artificial Intelligence Method for Electric Drives Mode Operating and Technical Condition Determination

et al. 2017

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Abstract. This paper proposes artificial intelligence method to determinate the status of electromechanical equipment by analysing the changes in the readings o parameters of its operating mode. As experimental results have revealed the dependence of wavelet transformation coefficients on the characteristic scales of functional and faulty motors under different loads. based on which a neural classification network is developed to reveal the current state of the electromechanical equipment. Further studies have shown that any mother wavelet function can be used to implement the proposed method. The researches of the state of the drive under various loads confirm the correctness of the theoretical calculations and the adequacy of the model.

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“…There are more works that are dealing with damage detection in rotating machines, as for: rotors [2], ball bearings [3], electrical motor [4,5] or motor winding [6], driving tooth wheels [7] or mechanical transmission [8], rotating machinery [9], wind turbines [10].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Damage detection in rotating machinery by means of entropy-based parameters

Tocarciuc¹,

Bereteu²,

Drăgănescu³

2014

AIP Conference Proceedings

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Abstract. The paper is proposing two new entropy-based parameters, namely Renyi Entropy Index (REI) and SharmaMittal Entropy Index (SMEI), for detecting the presence of failures (or damages) in rotating machinery, namely: belt structural damage, belt wheels misalignment, failure of the fixing bolt of the machine to its baseplate and eccentricities (i.e.: due to detaching a small piece of material or bad mounting of the rotating components of the machine). The algorithms to obtain the proposed entropy-based parameters are described and test data is used in order to assess their sensitivity. A vibration test bench is used for measuring the levels of vibration while artificially inducing damage. The deviation of the two entropy-based parameters is compared in two states of the vibration test bench: not damaged and damaged. At the end of the study, their sensitivity is compared to Shannon Entropic Index.

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Using bispectral distribution as a feature for rotating machinery fault diagnosis

Cited by 41 publications

References 15 publications

An Automatic Diagnosis System for Hepatitis Diseases Based on Genetic Wavelet Kernel Extreme Learning Machine

An Automatic Diagnosis System for Hepatitis Diseases Based on Genetic Wavelet Kernel Extreme Learning Machine

Artificial Intelligence Method for Electric Drives Mode Operating and Technical Condition Determination

Damage detection in rotating machinery by means of entropy-based parameters

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