A data driven degradation-based model for the maintenance of turnouts: a case study

Letot, Christophe; Dersin, Pierre; Pugnaloni, Michele; Dehombreux, Pierre; Fleurquin, Guillaume; Douziech, Cyril; La-Cascia, Piero

doi:10.1016/j.ifacol.2015.09.650

Cited by 27 publications

(16 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, it was compared to a hidden-Markov model-based method on real data collected from a railway S&C system. Basically, the proposed SSBP method had three steps: (1) clustering that was achieved based on the k-means clustering algorithm; (2) cluster evaluation in which a Calinski-Harabasz (CH) index [125] was chosen for validation (it was used to give the optimal number of clusters and health states); (3) finally, RUL prediction was achieved using the transition probabilities between health states. In this study, nine sensors were installed in the S&C system to collect the force, current, and voltage signals, in addition to two proximity sensors for each rail, and two displacement sensors for each drive rod.…”

Section: Fp Methodsmentioning

confidence: 99%

On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview

et al. 2019

View full text Add to dashboard Cite

Railway switch and crossing (S&C) systems have a very complex structure that requires not only a large number of components (such as rails, check rails, switches, crossings, turnout bearers, slide chair, etc.) but also different types of components and technologies (mechanical devices to operate switches, electrical and/or electronic devices for control, etc.). This complexity of railway S&C systems makes them vulnerable to failures and malfunctions that can ultimately cause delays and even fatal accidents. Thus, it is crucial to develop suitable condition monitoring techniques to deal with fault detection and diagnosis (FDD) in railway S&C systems. The main contribution of this paper is to present a comprehensive review of the existing FDD techniques for railway S&C systems. The aim is to overview the state of the art in rail S&C and in doing so to provide a platform for researchers, railway operators, and experts to research, develop and adopt the best methods for their applications; thereby helping ensure the rapid evolution of monitoring and fault detection in the railway industry at a time of the increased interest in condition based maintenance and the use of high-speed trains on the rail network.

show abstract

Section: Fp Methodsmentioning

confidence: 99%

On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The health state division can be defined as an assessment process of a component health status by dividing the HI into discrete degradation levels (e.g., healthy, faulty minor, and faulty critical). A data-driven health assessment methodology was proposed in [24] for failure prognostics of point machines next to health state division. The health state division was carried out by fitting two different degradation functions, and then the divided states were used in RUL estimation.…”

Section: Introductionmentioning

confidence: 99%

Railway Point Machine Prognostics Based on Feature Fusion and Health State Assessment

Atamuradov

Medjaher

Camci

et al. 2019

IEEE Trans. Instrum. Meas.

View full text Add to dashboard Cite

This paper presents a condition monitoring approach for point machine prognostics to increase the reliability, availability, and safety in railway transportation industry. The proposed approach is composed of three steps: 1) health indicator (HI) construction by data fusion, 2) health state assessment, and 3) failure prognostics. In Step 1, the time-domain features are extracted and evaluated by hybrid and consistency feature evaluation metrics to select the best class of prognostics features. Then, the selected feature class is combined with the adaptive feature fusion algorithm to build a generic point machine HI. In Step 2, health state division is accomplished by time-series segmentation algorithm using the fused HI. Then, fault detection is performed by using a support vector machine classifier. Once the faulty state has been classified (i.e., incipient/starting fault), the single spectral analysis recurrent forecasting is triggered to estimate the component remaining useful life. The proposed methodology is validated on in-field point machine sliding-chair degradation data. The results show that the approach can be effectively used in railway point machine monitoring.

show abstract

“…The authors utilized k‐means clustering in the point machine health states identification and calculated the state transition probabilities for prognostics by using hidden Markov models (HMMs). In the same domain, a data–driven‐based point MHA methodology was proposed in a previous research for failure prognostics. In this work, the point machine degradation levels were extracted and the remaining‐useful‐life (RUL) was calculated using the state transition time values.…”

Section: Introductionmentioning

confidence: 99%

Feature selection and fault‐severity classification–based machine health assessment methodology for point machine sliding‐chair degradation

Atamuradov

Medjaher

Camci

et al. 2019

Quality & Reliability Eng

View full text Add to dashboard Cite

In this paper, we propose an offline and online machine health assessment (MHA) methodology composed of feature extraction and selection, segmentation‐based fault severity evaluation, and classification steps. In the offline phase, the best representative feature of degradation is selected by a new filter‐based feature selection approach. The selected feature is further segmented by utilizing the bottom‐up time series segmentation to discriminate machine health states, ie, degradation levels. Then, the health state fault severity is extracted by a proposed segment evaluation approach based on within segment rate‐of‐change (RoC) and coefficient of variation (CV) statistics. To train supervised classifiers, a priori knowledge about the availability of the labeled data set is needed. To overcome this limitation, the health state fault‐severity information is used to label (eg, healthy, minor, medium, and severe) unlabeled raw condition monitoring (CM) data. In the online phase, the fault‐severity classification is carried out by kernel‐based support vector machine (SVM) classifier. Next to SVM, the k‐nearest neighbor (KNN) is also used in comparative analysis on the fault severity classification problem. Supervised classifiers are trained in the offline phase and tested in the online phase. Unlike to traditional supervised approaches, this proposed method does not require any a priori knowledge about the availability of the labeled data set. The proposed methodology is validated on infield point machine sliding‐chair degradation data to illustrate its effectiveness and applicability. The results show that the time series segmentation‐based failure severity detection and SVM‐based classification are promising.

show abstract

A data driven degradation-based model for the maintenance of turnouts: a case study

Cited by 27 publications

References 8 publications

On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview

On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview

Railway Point Machine Prognostics Based on Feature Fusion and Health State Assessment

Feature selection and fault‐severity classification–based machine health assessment methodology for point machine sliding‐chair degradation

Contact Info

Product

Resources

About