Condition monitoring of naturally damaged slow speed slewing bearing based on ensemble empirical mode decomposition

Caesarendra, Wahyu; Kosasih, P.B.; Tieu, Anh Kiet; Moodie, Craig; Choi, Byeong-Keun

doi:10.1007/s12206-013-0608-7

Cited by 60 publications

(39 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A bearing fault such as outer race, inner race or rolling element fault has occurred when one of the IMF frequencies is identical to one of the bearing fault frequencies (for example as shown in Table 1) [34]. Table 2, 3 and 4, respectively.…”

Section: Braun and Feldmanmentioning

confidence: 99%

Circular domain features based condition monitoring for low speed slewing bearing

Caesarendra

Kosasih

Moodie

2014

Mechanical Systems and Signal Processing

Self Cite

View full text Add to dashboard Cite

This paper presents a novel application of circular domain features calculation based condition monitoring method for low rotational speed slewing bearing. The method employs data reduction process using piecewise aggregate approximation (PAA) to detect frequency alteration in the bearing signal when the fault occurs. From the processed data, circular domain features such as circular mean, circular variance, circular skewness and circular kurtosis are calculated and monitored. It is shown that the slight changes of bearing condition during operation can be identified more clearly in circular domain analysis compared to time domain analysis and other advanced signal processing methods such as wavelet decomposition and empirical mode decomposition (EMD) allowing the engineer to better schedule the maintenance work. Four circular domain features were shown to consistently and clearly identify the onset (initiation) of fault from the peak feature value which is not clearly observable in time domain features. The application of the method is demonstrated with simulated data, laboratory slewing bearing data and industrial bearing data from Coal Bridge Reclaimer used in a local steel mill.

show abstract

Section: Braun and Feldmanmentioning

confidence: 99%

Circular domain features based condition monitoring for low speed slewing bearing

Caesarendra

Kosasih

Moodie

2014

Mechanical Systems and Signal Processing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Owing to the strong background noise interference, extracting combined fault features from vibration signals has long been the focus of current studies. A common approach is to diagnose the faults of time-domain vibration signals after the denoising process [2][3][4][5], aiming to increase the signal-to-noise ratio (SNR) and reduce the noise interference.…”

Section: Introductionmentioning

confidence: 99%

Combined Failure Diagnosis of Slewing Bearings Based on MCKD‐CEEMD‐ApEn

Wang

Liu

et al. 2018

Shock and Vibration

View full text Add to dashboard Cite

Large-size and heavy-load slewing bearings, which are mainly used in heavy equipment, comprise a subgroup of rolling bearings. Owing to the complexity of the structures and working conditions, it is quite challenging to effectively diagnose the combined failure and extract fault features of slewing bearings. In this study, a method was proposed to denoise and classify the combined failure of slewing bearings. First, after removing the mean, the vibration signals were denoised by maximum correlated kurtosis deconvolution. The signals were then decomposed into several intrinsic mode functions (IMFs) by complementary ensemble empirical mode decomposition (CEEMD). Appropriate IMFs were selected based on the correlation coefficient and kurtosis. The approximate entropy values of the selected IMFs were regarded as the characteristic vectors and then inputted into the support vector machine (SVM) based on multiclass classification for training. The practical combined failure signals of the 3 conditions were finally recognized and classified using SVMs. The study also compared the proposed method with 5 other methods to demonstrate the superiority and effectiveness of the proposed method.

show abstract

“…The raw vibration data are oftentimes pre-processed to aid the detection process. Envelope analysis (Sawalhi et al, 2007;Wang and Lee, 2013) and wavelet-based decompositions (Caesarendra et al, 2013;Lou and Loparo, 2004;Smith et al, 2007;Purushotham et al, 2005;Altmann and Mathew, 2001;Abbasion et al, 2007) are the commonly used pre-processing methods. With the increasing technology, several vibration-based PR methods are used also to diagnose machinery faults (Rauber et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Extracting accurate time domain features from vibration signals for reliable classification of bearing faults

Tahir

2018

Int. j. adv. appl. sci.

View full text Add to dashboard Cite

Identification of localized faults in rolling element bearing (REB) frequently utilizes vibration-based pattern recognition (PR) methods. Time domain (TD) statistical features are often part of the diagnostic models. The extracted statistical values are, however, influenced by the fluctuations present in random vibration signals. These inaccurate values consequently affect the diagnostic capability of the supervised learning based classifiers. This study examines the sensitivity of TD features to signal fluctuations. Vibration data is acquired from different REBs containing localized faults using a test rig, and a central tendency (CT) based feature extraction (CTBFE) method is proposed. The CTBFE ensures the supply of reliable feature values to the PR models. The method selects the fault related appropriate portion of a vibration signal prior to extract TD features. Variety of classifiers is used to judge the effect of CTBFE method on their fault classification accuracies, which are enhanced considerably. The results are also compared with a similar sort of existing method, where the proposed method provides better results and feasibility for on-line applications.

show abstract

Condition monitoring of naturally damaged slow speed slewing bearing based on ensemble empirical mode decomposition

Cited by 60 publications

References 16 publications

Circular domain features based condition monitoring for low speed slewing bearing

Circular domain features based condition monitoring for low speed slewing bearing

Combined Failure Diagnosis of Slewing Bearings Based on MCKD‐CEEMD‐ApEn

Extracting accurate time domain features from vibration signals for reliable classification of bearing faults

Contact Info

Product

Resources

About