Bearings Prognostic Using Mixture of Gaussians Hidden Markov Model and Support Vector Machine

Sloukia, Fatima Ezzahra; Bouarfa, Rajae; Medromi, Hicham; Wahbi, M.

doi:10.5121/ijnsa.2013.5308

Cited by 8 publications

(8 citation statements)

References 23 publications

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“…The elastic net [37] is the combination of Lasso regularization [34] and ridge regularization [38] . Although the lasso regularization can usually work well for data without…”

Section: Elastic Net Based Model Regularization Algorithmmentioning

confidence: 99%

A Regularized LSTM Method for Predicting Remaining Useful Life of Rolling Bearings

Liu

Meng

Wei

et al. 2021

Int. J. Autom. Comput.

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Rotating machinery is important to industrial production. Any failure of rotating machinery, especially the failure of rolling bearings, can lead to equipment shutdown and even more serious incidents. Therefore, accurate residual life prediction plays a crucial role in guaranteeing machine operation safety and reliability and reducing maintenance cost. In order to increase the forecasting precision of the remaining useful life (RUL) of the rolling bearing, an advanced approach combining elastic net with long short-time memory network (LSTM) is proposed, and the new approach is referred to as E-LSTM. The E-LSTM algorithm consists of an elastic mesh and LSTM, taking temporal-spatial correlation into consideration to forecast the RUL through the LSTM. To solve the over-fitting problem of the LSTM neural network during the training process, the elastic net based regularization term is introduced to the LSTM structure. In this way, the change of the output can be well characterized to express the bearing degradation mode. Experimental results from the real-world data demonstrate that the proposed E-LSTM method can obtain higher stability and relevant values that are useful for the RUL forecasting of bearing. Furthermore, these results also indicate that E-LSTM can achieve better performance.

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“…The elastic net [37] is the combination of Lasso regularization [34] and ridge regularization [38] . Although the lasso regularization can usually work well for data without…”

Section: Elastic Net Based Model Regularization Algorithmmentioning

confidence: 99%

A Regularized LSTM Method for Predicting Remaining Useful Life of Rolling Bearings

Liu

Meng

Wei

et al. 2021

Int. J. Autom. Comput.

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“…Yan et al 1 utilized the short-time Fourier transform (STFT)-based energy and back propagation (BP) neural network for the degradation performance assessment. Sloukia et al 2 used wavelet packet decomposition to extract the energy of the signal as the feature of the vibration signal, which can be used to estimate the fault. Tobon-Mejia et al 3 combined wavelet packet decomposition–based energy feature with mixture of Gaussians hidden Markov models for the estimation of the Remaining Useful Life.…”

Section: Introductionmentioning

confidence: 99%

An effective health indicator for rolling elements bearing based on data space occupancy

Zhang

et al. 2016

Structural Health Monitoring

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An effective degradation indicator created from the general features is still a hotspot for the condition monitoring of bearing. Since the single general feature–based indicator cannot fully describe the degradation performance, some new indicators based on the characteristics of multiple general features are proposed using pattern recognition and dimensional reduction algorithms. Although most of these indicators can show the incipient fault and good degradation tendency only across the failure stage, also with oscillation, seldom of them can give the whole health condition of the entire useful life. Hence, further research is still needed to reduce the oscillation impact on the sensitivity of the indicator and make remarkable tendency of the degradation. For that purpose, a new indicator is proposed. For the capability of preserving the full structure of data manifold, Orthogonal Neighbourhood Preserving Projections is used for the data preprocessing to obtain the low-dimensional features with the main information remained. Since the preprocessed data can describe different health conditions, K-means clustering algorithm is used to classify the data into different sets according to different health conditions without human intervention. Then, the summation of mean maximum radius of the data set can be used as the health indicator. In order to further decrease the disturbance of the background noise, local outlier coefficient algorithm is used to eliminate the outliers’ influence on the effectiveness of the health indicator. The experimental results illustrate that the proposed indicator can show distinct tendency of different health conditions with less influence of the randomness and more sensitive to the incipient fault.

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“…The Prognostics Data Repository from NASA was used in IEEE contest in 2012 which rewarded the team with the minor error percentage in ball bearing RUL estimation. Sloukia et al [4] used Mixture of Gaussians-Hidden Markov Models (MoG-HMM) and Support Vector Machine (SVM) with accuracy superior to 99%. Another approach used by Mosallam et al [5], is Empirical Mode Decomposition (EMD) to discover the trend in the failure evolution with an absolute error of 0.0751.…”

Section: Introductionmentioning

confidence: 99%

Gaussian processes in ball bearing prognostics

López

Ramírez

Riaza

2017

DYNA

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In this work, vibration analysis and Gaussian Processes techniques are used in useful life prognostics of ball bearings. The database is provided by The Prognostics Data Repository from NASA, and shows the failure evolution in ball bearings. The data basis also provides training and validation data sets for ball bearing useful life prediction. Several time and frequency characteristics are extracted from ball bearing vibration signals for trending analysis, and finally one of these is taken as input for the Gaussian process and describe, with a probabilistic strategy, the failure evolution system. No dimensionality reduction algorithm is used in this paper, only the evaluation of trends in failure evolution is taken for decision. This data basis was used in 2012 IEEE classification contest. Several participants used classification techniques based on time-frequency transformation and Artificial Intelligence algorithms but none of them used Gaussian Processes in a classification scheme. Although, the present work does not have the best results in classification it does show a major simplicity in formulation and implementation than most of the classification schemes.

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Bearings Prognostic Using Mixture of Gaussians Hidden Markov Model and Support Vector Machine

Cited by 8 publications

References 23 publications

A Regularized LSTM Method for Predicting Remaining Useful Life of Rolling Bearings

A Regularized LSTM Method for Predicting Remaining Useful Life of Rolling Bearings

An effective health indicator for rolling elements bearing based on data space occupancy

Gaussian processes in ball bearing prognostics

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