Rotating machinery prognostics: State of the art, challenges and opportunities

Heng, Aiwina Soong Yin; Zhang, Sheng; Tan, Andy; Mathew, Joseph

doi:10.1016/j.ymssp.2008.06.009

Cited by 987 publications

(685 citation statements)

References 91 publications

Supporting

Mentioning

632

Contrasting

Unclassified

Order By: Relevance

“…This is a central point of this paper. Three main categories of prognostic approaches are generally distinguished: experience-based, model-based, and data-driven approaches (Byington et al (2002); Heng et al (2009) ;Vachtsevanos et al (2006)). Briefly, experiencebased approaches are used in statistical reliability applications to predict failure probability at any time.…”

Section: Introductionmentioning

confidence: 99%

Features Selection Procedure for Prognostics: An Approach Based on Predictability

Javed

Gouriveau²,

Zemouri³

et al. 2012

IFAC Proceedings Volumes

View full text Add to dashboard Cite

Prognostic aims at estimating the remaining useful life (RU L) of a degrading equipment, i.e at predicting the life time at which a component or a system will be unable to perform a desired function. This task is achieved through essential steps of data acquisition, feature extraction and selection, and prognostic modeling. This paper emphasizes on the selection phase and aims at showing that it should be performed according to the predictability of features: as there is no interest in retaining features that are hard to be predicted. Thereby, predictability is defined and a feature selection procedure based on this concept is proposed. The effectiveness of the approach is judged by applying it on a real-world case: through comparison is made in order to show that the better predictable features lead to better RU L estimation.

show abstract

Section: Introductionmentioning

confidence: 99%

Features Selection Procedure for Prognostics: An Approach Based on Predictability

Javed

Gouriveau²,

Zemouri³

et al. 2012

IFAC Proceedings Volumes

View full text Add to dashboard Cite

show abstract

“…Prognostic plays a vital role in predicting RUL and optimizing machine usage in engineering practice [5]. Effective prognosis can avoid machine downtime that results in significant losses and reduce the risk of severe accidents [89].…”

Section: Prognostics Using Computational Intelligencementioning

confidence: 99%

“…Feature extraction and fault diagnosis are usually used in detecting the abnormal state, determining the fault location, and predicting the failure extent [4]. Prognostic techniques relate to the remaining useful life (RUL) prediction, which is used in planning an effective maintenance strategy that can improve system reliability [5]. The realization of the importance of optimal sensor placement in condition monitoring system and optimal sensor placement methods are also investigated.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

View full text Add to dashboard Cite

Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few comprehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition monitoring and fault diagnosis. The recent research and development of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are discussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mechanism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suitable method for a specific situation and pointing out potential research directions.

show abstract

“…Most of these approaches focus on fault diagnostics and prognostics and fall into three main categories: statistical-based, model-based, and data driven-based. The applications of these approaches for rotating machinery could be found in the studies of Jardine (Jardine et al, 2006) and Heng (Heng et al, 2009). …”

Section: Introductionmentioning

confidence: 99%

An intelligent condition-based maintenance platform for rotating machinery

Tran

Yang

2012

Expert Systems with Applications

View full text Add to dashboard Cite

Maintenance is of necessity for sustaining machinery availability and reliability in order to ensure productivity, product quality, on-time delivery, and safe working environment. The costly maintenance strategies such as corrective maintenance and scheduled maintenance have been progressively replaced by superior maintenance strategies in which condition-based maintenance (CBM) is one of the delegates. This strategy commonly consists of sequent modules such as data acquisition, signal processing, feature extraction and feature selection, condition monitoring, etc. However, approaches in literature which have been developed for each module and implemented for different applications are standalone instead of a comprehensive system. Furthermore, these approaches have been demonstrated in a laboratory environment without any industrial validations. For these reasons, an intelligent algorithm based CBM platform is proposed in this paper to be applied for rotating machinery easily and effectively. Subsequently, two case-studies are presented in order to evaluate the effectiveness of this platform in industrial applications.

show abstract

Rotating machinery prognostics: State of the art, challenges and opportunities

Cited by 987 publications

References 91 publications

Features Selection Procedure for Prognostics: An Approach Based on Predictability

Features Selection Procedure for Prognostics: An Approach Based on Predictability

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

An intelligent condition-based maintenance platform for rotating machinery

Contact Info

Product

Resources

About