Evaluation using online support-vector-machines and fuzzy reasoning. Application to condition monitoring of speeds rolling process

Bouhouche, Salah; Yazid, Laib Laksir; Sissaoui, Hocine; Bast, Jürgen

doi:10.1016/j.conengprac.2010.05.010

Cited by 26 publications

(4 citation statements)

References 21 publications

(17 reference statements)

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“…A multiscale kernel PLS model has also been developed for fault diagnosis of nonlinear processes . Besides, several developed kernel methods have been extended for monitoring of batch processes. , Other related kernel-based methods that have been developed for nonlinear process monitoring include those reported by Ge and Song, ,, Bouhouche et al, Saravanan et al, etc.…”

Section: State-of-the-art Of Data-based Process Monitoringmentioning

confidence: 99%

Review of Recent Research on Data-Based Process Monitoring

Song

Gao

2013

Ind. Eng. Chem. Res.

857

438

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Data-based process monitoring has become a key technology in process industries for safety, quality, and operation efficiency enhancement. This paper provides a timely update review on this topic. First, the natures of different industrial processes are revealed with their data characteristics analyzed. Second, detailed terminologies of the data-based process monitoring method are illustrated. Third, based on each of the main data characteristics that exhibits in the process, a corresponding problem is defined and illustrated, with review conducted with detailed discussions on connection and comparison of different monitoring methods. Finally, the relevant research perspectives and several promising issues are highlighted for future work.

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Section: State-of-the-art Of Data-based Process Monitoringmentioning

confidence: 99%

Review of Recent Research on Data-Based Process Monitoring

Song

Gao

2013

Ind. Eng. Chem. Res.

857

438

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“…The MIMOLSSVM model, based on the principle of structural risk minimization [64][65][66], is a powerful tool for implementing interval prediction via the multi-output pattern. However, the application of MIMOLSSVM to uncertainty modeling is rarely implemented, despite the fact thatit has excellent nonlinear system modeling capabilities, in particular for uncertainty mining.…”

Section: 3multi-input Multi-output Least Squares Support Vector Machinementioning

confidence: 99%

An analysis-forecast system for uncertainty modeling of wind speed: A case study of large-scale wind farms

Wang

Niu

et al. 2018

Applied Energy

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The uncertainty analysis and modeling of wind speed, which has an essential influence onwind power systems, is consistently considered a challenging task.However, most investigationsthus far were focusedmainly on point forecasts, which in reality cannot facilitate quantitative characterization of the endogenous uncertainty involved.An analysis-forecast system that includes an analysis module and a forecast module and can provide appropriate scenarios for the dispatching and scheduling of a power system is devised in this study; this system superior to those presented in previous studies.In order to qualitatively and quantitatively investigate the uncertainty of wind speed, recurrence analysis techniques are effectively developedfor application in the analysis module. Furthermore, in order to quantify the uncertainty accurately, a novel architecture aimed at uncertainty mining is devised for the forecast module, where a non-parametric model optimized by animproved multi-objective water cycle algorithm is considered a predictor for producing intervals for each mode component after feature selection. The results of extensive in-depthexperiments showthat the devised systemis not only superior to the considered benchmark models, but also has good potential practical applications in wind power systems.

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“…SVMs have been found to be remarkably effective in many practical applications. This method is widely used in areas such as pattern recognition [19], time-series forecasting [20], diagnostics [21][22][23][24][25], robotics [26], signal processing [25,27], speech and word recognition [28], machine vision [29], and financial forecasting [30]. In SVMs, the kernel parameters have an influence on the generalization performance, and the regularization constant determines the trade-off between minimizing the training error and minimizing the model complexity.…”

Section: Introductionmentioning

confidence: 99%

A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM

Cheng

et al. 2014

Shock and Vibration

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This study investigates a novel method for roller bearing fault diagnosis based on local characteristic-scale decomposition (LCD) energy entropy, together with a support vector machine designed using an Artificial Chemical Reaction Optimisation Algorithm, referred to as an ACROA-SVM. First, the original acceleration vibration signals are decomposed into intrinsic scale components (ISCs). Second, the concept of LCD energy entropy is introduced. Third, the energy features extracted from a number of ISCs that contain the most dominant fault information serve as input vectors for the support vector machine classifier. Finally, the ACROA-SVM classifier is proposed to recognize the faulty roller bearing pattern. The analysis of roller bearing signals with inner-race and outer-race faults shows that the diagnostic approach based on the ACROA-SVM and using LCD to extract the energy levels of the various frequency bands as features can identify roller bearing fault patterns accurately and effectively. The proposed method is superior to approaches based on Empirical Mode Decomposition method and requires less time.

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Evaluation using online support-vector-machines and fuzzy reasoning. Application to condition monitoring of speeds rolling process

Cited by 26 publications

References 21 publications

Review of Recent Research on Data-Based Process Monitoring

Review of Recent Research on Data-Based Process Monitoring

An analysis-forecast system for uncertainty modeling of wind speed: A case study of large-scale wind farms

A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM

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