Adaptive Empirical Mode Decomposition for Bearing Fault Detection

Tuan, Van Phu; Nguyen, Le Cuong

doi:10.5545/sv-jme.2015.3079

Cited by 14 publications

(9 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another great example of the utilization of the classification methods is the bearing-fault classification [6] and [7]. Bearing-fault detection is a very popular problem in mechanical engineering since bearings are one of the most utilized rotational mechanical elements [8] and [9].…”

Section: Introductionmentioning

confidence: 99%

“…Studies on bearing-fault classification differ in two ways. The first type of studies covers different signal-processing techniques for the classification of bearing faults [7] and [13] or for feature extraction and selection from vibrational data, which are then used to enhance the results of an applied classification method [14]. Other studies mostly utilize the different classification methods to obtain better classification results [6] and [15].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification

Panić

Klemenc

Nagode

2020

SV-JME

View full text Add to dashboard Cite

Condition monitoring and fault detection are nowadays popular topic. Different loads, enviroments etc. affect the components and systems differently and can induce the fault and faulty behaviour. Most of the approaches for the fault detection rely on the use of the good classification method. Gaussian mixture model based classification are stable and versatile methods which can be applied to a wide range of classification tasks. The main task is the estimation of the parameters in the Gaussian mixture model. Those can be estimated with various techniques. Therefore, the Gaussian mixture model based classification have different variants which can vary in performance. To test the performance of the Gaussian mixture model based classification variants and general usefulness of the Gaussian mixture model based classification for the fault detection, we have opted to use the bearing fault classification problem. Additionally, comparisons with other widely used non-parametric classification methods are made, such as support vector machines and neural networks. The performance of each classification method is evaluated by multiple repeated k-fold cross validation. From the results obtained, Gaussian mixture model based classification methods are shown to be competitive and efficient methods and usable in the field of fault detection and condition monitoring. Highlights• Gaussian-mixture-model-based classification was applied to the bearing-fault classification. • To discriminate the faulty from non-faulty bearings only simple statistics from vibrational data was used. • Two different datasets, the Case Western Rice University dataset and Bearing vibration data collected under time-varying rotational speed conditions dataset are used. • The Gaussian-mixture-model-based classification method showed to be a competitive and efficient method.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification

Panić

Klemenc

Nagode

2020

SV-JME

View full text Add to dashboard Cite

show abstract

“…In order to obtain the useful information generated by planetary gears, some time-frequency analysis methods are proposed. The time-frequency analysis method which has the best application effect is empirical mode decomposition (EMD) [3] proposed by Huang et al [4]. A series of IMFs with strict definitions can be obtained from the original vibration signal [5].…”

Section: Introductionmentioning

confidence: 99%

Research of Planetary Gear Fault Diagnosis Based on Multi-Scale Fractal Box Dimension of CEEMD and ELM

Chen¹,

Li²,

Li³

2017

SV-JME

View full text Add to dashboard Cite

The planetary gear is the most critical part of a drive transmission system, and its faults will affect the reliability of the equipment, and even cause accidents. Therefore, it is of great significance to study the fault diagnosis of the planetary gear. A method of planetary gear fault diagnosis based on the multi-scale fractal box dimension of complementary ensemble empirical mode decomposition (CEEMD) and extreme learning machine (ELM) is proposed. The original vibration signal is decomposed by CEEMD, and a series of intrinsic mode functions (IMFs) are obtained. Some effective IMFs are extracted, and their reconstructed signal associated with the feature information is obtained. The reconstructed signal is analysed with multi-scale analysis, and the fault feature information contained in the signals with different scales is quantified and extracted via a fractal box dimension. The status recognition of planetary gear is achieved by combining ELM. The experiments show that the proposed method is effective at diagnosing planetary gear faults. Highlights • A method of planetary gear fault diagnosis based on the multi-scale fractal box dimensions of CEEMD and ELM is proposed.• The effective IMFs which associate with the shock feature information are selected by the kurtosis criterion.• The multi-scale fractal box dimension of the reconstructed signal is defined as the fault feature information.• The status recognition of a planetary gear is achieved by combining ELM.

show abstract

“…The features that are commonly extracted have been generated from the time domain [2], the frequency domain [3], or the time-frequency domain [4]. Next, the extracted features are fed into classifiers such as a support vector machine (SVM) [5] and [6], a decision tree [7], a BP neural network [8], etc.…”

Section: Introductionmentioning

confidence: 99%

An Improved Bearing Fault Diagnosis Method using One-Dimensional CNN and LSTM

2018

SV-JME

View full text Add to dashboard Cite

Adaptive Empirical Mode Decomposition for Bearing Fault Detection

Cited by 14 publications

References 16 publications

Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification

Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification

Research of Planetary Gear Fault Diagnosis Based on Multi-Scale Fractal Box Dimension of CEEMD and ELM

An Improved Bearing Fault Diagnosis Method using One-Dimensional CNN and LSTM

Contact Info

Product

Resources

About