Mechanical Fault Diagnosis Based on LMD-Approximate Entropy and LSSVM

Dong, Zengshou; Tian, Xue-Qin; Zeng, Jianchao

doi:10.11591/telkomnika.v11i2.2028

Cited by 5 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Notwithstanding EMD LSSVM-based prediction can achieve sufficient accuracy, comparisons between LMDand EMD-LSSVM recently had been made by Dong et al [17]. In their works, LMD-LSSVM outperformed EMD-LSSVM by lower error rate and training duration of LMD.…”

Section: Discussionmentioning

confidence: 99%

Enhanced Online LS-SVM Using EMD Algorithm for Prices Prediction of Building Materials

Ying

Chien

Ting

et al. 2014

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

View full text Add to dashboard Cite

-Cost estimation is economically critical before starting off a construction project. One of the essential assignments for materials' prices prediction is to control the cost of inventory. Even though the prediction system based on support vector machine (SVM) recently has been emerged as a favourable choice, the prediction accuracy of SVM is usually deteriorated with nonstationary price data. Thus the way to explore workable price prediction still remains a challenge to be resolved for materials' cost control. In this paper, an enhanced online least squares support vector machine (LS-SVM) is proposed to predict the trend of building materials prices. Our design is to incorporate with empirical mode decomposition (EMD) to deconstruct nonlinear and nonstationary data for the set of intrinsic mode functions (IMFs), which are represented in sinusoidlike waveforms. Superior prediction, therefore, can be attained by predicting IMFs with online LS-SVMs. According to our simulation results, proposed EMD designs notably improve prediction accuracy from online LS-SVM and are workable for the cost estimation of building materials.

show abstract

Section: Discussionmentioning

confidence: 99%

Enhanced Online LS-SVM Using EMD Algorithm for Prices Prediction of Building Materials

Ying

Chien

Ting

et al. 2014

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

View full text Add to dashboard Cite

show abstract

“…Approximate entropy (ApEn) [18][19][20] is a nonlinear dynamic parameter used to quantify the regularity and unpredictability of time series fluctuations. The more complex the time series, the larger the approximate entropy.…”

Section: Feature Extraction Of Multi-physical Quantity Signals Of Rol...mentioning

confidence: 99%

Bearing fault feature selection method based on dynamic time warped related searches

Wang¹,

Wang²,

Chen³

et al. 2022

J. vibroeng.

View full text Add to dashboard Cite

Traditional feature selection algorithms rarely consider the dynamic misalignment between different time series, and have poor fault tolerance and robustness. In this paper, a fault feature selection method for rolling bearings based on Dynamic Time Warped Related Searches (DTWRS) is proposed. Firstly, the bearing fault feature set is constructed, and the dynamic time warping algorithm is used to calculate the shortest cumulative distance between feature of different faults, and this distance is used as the correlation evaluation standard. Then, two new search rules, dynamic time warping difference and dynamic time warping entropy, are proposed based on the minimum redundancy between bearing fault features and the maximum correlation between fault features and feature categories, use these two search rules to judge the ability of the feature to express the fault, define the quality of the fault feature and sort from good to bad according to the level of ability. Finally, in this order, the number of features is gradually increased and input to the fault classifier, and the sensitive fault feature set is obtained based on the principle of the highest recognition rate and the least number of features. The experimental results show that the fault feature selection method of rolling bearing based on DTWRS can increase the accuracy of fault diagnosis while minimizing the number of features, and improve the efficiency and effect of fault diagnosis.

show abstract

“…It is non-extensive in which there are two identical systems, then the number of entropy is not the same as both added together. TE mathematically is expressed as (17).…”

Section: Tsallis Entropymentioning

confidence: 99%

“…The results indicated that MSE produces more consistent features compared to the spectral method. Similar to biological signal analysis, wavelet entropy is used for transformer design [15], while fault diagnosis was analyzed using sample entropy [16] and approximate entropy [17]. The performance comparison of multiscale entropy for various entropy measurement techniques has never previously been done.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Multiscale Entropy for Lung Sound Classification

Rizal¹,

Hidayat²,

Nugroho³

2018

IJEECS

View full text Add to dashboard Cite

Lung sound is a biological signal used to determine the health level of the respiratory tract. Various digital signal processing techniques have been developed for the automatic lung sound classification. Entropy is one of the parameters used to measure the biomedical signal complexity. Multiscale entropy is introduced to measure the entropy of a signal at a particular scale range. Over time, various multiscale entropy techniques are used to measure the signal complexity on biological signal and other physical signals. In this paper, a number of multiscale entropy techniques for the lung sound classification are discussed. The results showed that Multiscale Permutation Entropy (MPE) could produce the highest accuracy of 97.98% for five classes of lung sound data. Results achieved for the scale 1-10 producing ten features for each lung sound data. This result is better than other seven entropies. The use of Permutation entropy (PE) on a multiscale scheme was to obtain a better accuracy compared to PE on one scale only

show abstract

Mechanical Fault Diagnosis Based on LMD-Approximate Entropy and LSSVM

Cited by 5 publications

References 10 publications

Enhanced Online LS-SVM Using EMD Algorithm for Prices Prediction of Building Materials

Enhanced Online LS-SVM Using EMD Algorithm for Prices Prediction of Building Materials

Bearing fault feature selection method based on dynamic time warped related searches

Comparison of Multiscale Entropy for Lung Sound Classification

Contact Info

Product

Resources

About