Algorithms for statistical moment evaluation for machine health monitoring

Martin, H. R.; Ismail, F.; Sakuta, A.

doi:10.1016/0888-3270(92)90034-g

Cited by 9 publications

(6 citation statements)

References 1 publication

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“…Common techniques used for machine fault diagnosis include time and frequency-domain analyses. Statistical information of the time-domain signal can be applied as trend parameters [19,49,50]. They can indicate the shape of the amplitude probability distributions.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Two Classifiers; K-Nearest Neighbor and Artificial Neural Network, for Fault Diagnosis on a Main Engine Journal-Bearing

Moosavian

Ahmadi

Tabatabaeefar

et al. 2013

Shock and Vibration

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Vibration analysis is an accepted method in condition monitoring of machines, since it can provide useful and reliable information about machine working condition. This paper surveys a new scheme for fault diagnosis of main journal-bearings of internal combustion (IC) engine based on power spectral density (PSD) technique and two classifiers, namely, K-nearest neighbor (KNN) and artificial neural network (ANN). Vibration signals for three different conditions of journal-bearing; normal, with oil starvation condition and extreme wear fault were acquired from an IC engine. PSD was applied to process the vibration signals. Thirty features were extracted from the PSD values of signals as a feature source for fault diagnosis. KNN and ANN were trained by training data set and then used as diagnostic classifiers. Variable K value and hidden neuron count (N) were used in the range of 1 to 20, with a step size of 1 for KNN and ANN to gain the best classification results. The roles of PSD, KNN and ANN techniques were studied. From the results, it is shown that the performance of ANN is better than KNN. The experimental results dèmonstrate that the proposed diagnostic method can reliably separate different fault conditions in main journal-bearings of IC engine.

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Section: Introductionmentioning

confidence: 99%

Comparison of Two Classifiers; K-Nearest Neighbor and Artificial Neural Network, for Fault Diagnosis on a Main Engine Journal-Bearing

Moosavian

Ahmadi

Tabatabaeefar

et al. 2013

Shock and Vibration

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“…The main feature extraction methods include time-domain, frequency-domain, and time-frequency domain methods. Time-domain methods are focused on extracting statistical features such as root mean square (RMS), peak values (P), standard deviation (StDev), kurtosis parameter (Kurt), and skewness (Sk) of the time waveform to determine transient phenomena that originate from a faulty gearbox (McFadden and Smith, 1985; Martin et al., 1992). Time synchronous average (TVA) has been widely used to extract the time waveform from the vibration signal synchronous with the shaft rate of rotation.…”

Section: Introductionmentioning

confidence: 99%

A novel gearbox fault feature extraction and classification using Hilbert empirical wavelet transform, singular value decomposition, and SOM neural network

Merainani

Rahmoune

Benazzouz

et al. 2017

Journal of Vibration and Control

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There are growing demands for condition monitoring and fault diagnosis of rotating machinery to lower unscheduled breakdown. Gearboxes are one of the fundamental components of rotating machinery; their faults identification and classification always draw a lot of attention. However, non-stationary vibration signals and low energy of weak faults makes this task challenging in many cases. Thus, a new fault diagnosis method which combines the Hilbert empirical wavelet transform (HEWT), singular value decomposition (SVD), and self-organizing feature map (SOM) neural network is proposed in this paper. HEWT, a new self-adaptive time-frequency analysis was applied to the vibration signals to obtain the instantaneous amplitude matrices. Then, the singular value vectors, as the fault feature vectors were acquired by applying the SVD. Last, the SOM was used for automatic gearbox fault identification and classification. An electromechanical model comprising an induction motor coupled with a single stage spur gearbox is considered where the vibration signals of four typical operation modes were simulated. The conditions include the healthy gearbox, input shaft slant crack, tooth cracking, and tooth surface pitting. Obtained results show that the proposed method effectively identifies the gearbox faults at an early stage and realizes automatic fault diagnosis. Moreover, performance evaluation and comparison between the proposed HEWT–SVD method and Hilbert–Huang transform (HHT)–SVD approach show that the HEWT–SVD is better for feature extraction.

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“…However, their characteristic signatures are known to be weak, covered by the natural frequency of the machine and often overwhelmed by noise with obvious non-linear and non-stationary behavior. 6 To overcome this problem, many methods for gear fault diagnosis have been proposed such as fast kurtogram, [7][8][9] autogram, 10,11 empirical wavelet transform (EWT), 12,13 and empirical mode decomposition (EMD). 14,15 These methods have been successfully established to give powerful proofs that help to make the right and the earliest decision for maintenance and troubleshooting.…”

Section: Introductionmentioning

confidence: 99%

New method for gear fault diagnosis using empirical wavelet transform, Hilbert transform, and cosine similarity metric

Bettahar

Rahmoune

Benazzouz

et al. 2014

Advances in Mechanical Engineering

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In this article, a new feature extraction method is proposed for gear fault diagnosis by combining the empirical wavelet transform, Hilbert transform, and cosine similarity metric. In the first place, a number of empirical mode components acquisitions are done, using empirical wavelet transform. Since different empirical modes have different sensitivities to fault, not all of them are needed for further analysis. Therefore, the most sensitive empirical modes are selected using the cosine similarity metric method. Hilbert transform was then used to obtain the envelope for amplitude modulation. Finally, spectral analysis using fast Fourier transform is applied on the obtained envelope. Gear test rig with gears under different fault states has revealed an effective outcome and a solid stability of this new approach. The obtained results show that our approach is efficiently able to detect and expose the gear faults signatures, that is, it highlights their frequencies and the corresponding harmonics with respect to the rotary frequency. Furthermore, this proposed method demonstrates more satisfactory and advantageous performances compared to those of fast kurtogram, or the autogram.

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Algorithms for statistical moment evaluation for machine health monitoring

Cited by 9 publications

References 1 publication

Comparison of Two Classifiers; K-Nearest Neighbor and Artificial Neural Network, for Fault Diagnosis on a Main Engine Journal-Bearing

Comparison of Two Classifiers; K-Nearest Neighbor and Artificial Neural Network, for Fault Diagnosis on a Main Engine Journal-Bearing

A novel gearbox fault feature extraction and classification using Hilbert empirical wavelet transform, singular value decomposition, and SOM neural network

New method for gear fault diagnosis using empirical wavelet transform, Hilbert transform, and cosine similarity metric

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