Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition

Purushotham, V.; Narayanan, Shrikanth; Prasad, Suryanarayana A.N.

doi:10.1016/j.ndteint.2005.04.003

Cited by 248 publications

(124 citation statements)

References 14 publications

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“…Each signal has 2048 samples. Were used five levels of decomposition using Db4 mother wavelet function [4,5]. Also for two different situations, one defective inner ring-fault belt and the second no defects, are presented details coefficients on the levels of decomposition in scale of colors.…”

Section: Fig3 Pulse Hardware Toolsmentioning

confidence: 99%

Vibration Analysis for Detection and Localization the Faults of Rotating Machinery Using Wavelet Techiniques

Pricop¹

2016

SBNA

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Section: Fig3 Pulse Hardware Toolsmentioning

confidence: 99%

Vibration Analysis for Detection and Localization the Faults of Rotating Machinery Using Wavelet Techiniques

Pricop¹

2016

SBNA

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“…Orthogonal wavelet transforms are normally applied for the compression and feature selection of signals. DWT is derived from discrete CWT, and is shown as the following expression [10]:…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Fatigue Feature Extraction Analysis Based on a K-Means Clustering Approach

Yunoh

Abdullah²,

Saad³

et al. 2015

J. Mech. Eng. Sci.

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This paper focuses on clustering analysis using a K-means approach for fatigue feature dataset extraction. The aim of this study is to group the dataset as closely as possible (homogeneity) for the scattered dataset. Kurtosis, the wavelet-based energy coefficient and fatigue damage are calculated for all segments after the extraction process using wavelet transform. Kurtosis, the wavelet-based energy coefficient and fatigue damage are used as input data for the K-means clustering approach. K-means clustering calculates the average distance of each group from the centroid and gives the objective function values. Based on the results, maximum values of the objective function can be seen in the two centroid clusters, with a value of 11.58. The minimum objective function value is found at 8.06 for five centroid clusters. It can be seen that the objective function with the lowest value for the number of clusters is equal to five; which is therefore the best cluster for the dataset.

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“…Wavelet-based techniques meet this challenge in a variety of applications presented in the following. (Purushotham et al, 2005) have applied the DWT towards the detection of localized bearing defects. The vibration signals were decomposed up to 4 levels using "db2" mother wavelet.…”

Section: Bearingsmentioning

confidence: 99%

Utilising the Wavelet Transform in Condition-Based Maintenance: A Review with Applications

Λούτας¹,

Kostopoulos²

2012

Advances in Wavelet Theory and Their Applications in Engineering, Physics and Technology

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Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition

Cited by 248 publications

References 14 publications

Vibration Analysis for Detection and Localization the Faults of Rotating Machinery Using Wavelet Techiniques

Vibration Analysis for Detection and Localization the Faults of Rotating Machinery Using Wavelet Techiniques

Fatigue Feature Extraction Analysis Based on a K-Means Clustering Approach

Utilising the Wavelet Transform in Condition-Based Maintenance: A Review with Applications

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