Prognostics and health management for maintenance practitioners - Review, implementation and tools evaluation.

In literature, prognostics and health management (PHM) systems have been studied by many researchers from many different engineering fields to increase system reliability, availability, safety and to reduce the maintenance cost of engineering assets. Many works conducted in PHM research concentrate on designing robust and accurate models to assess the health state of components for particular applications to support decision making. Models which involve mathematical interpretations, assumptions and approximations make PHM hard to understand and implement in real world applications, especially by maintenance practitioners in industry. Prior knowledge to implement PHM in complex systems is crucial to building highly reliable systems. To fill this gap and motivate industry practitioners, this paper attempts to provide a comprehensive review on PHM domain and discusses important issues on uncertainty quantification, implementation aspects next to prognostics feature and tool evaluation. In this paper, PHM implementation steps consists of; (1) critical component analysis, (2) appropriate sensor selection for condition monitoring (CM), (3) prognostics feature evaluation under data analysis and (4) prognostics methodology and tool evaluation matrices derived from PHM literature. Besides PHM implementation aspects, this paper also reviews previous and on-going research in high-speed train bogies to highlight problems faced in train industry and emphasize the significance of PHM for further investigations

Railway point machine prognostics based on feature fusion and health state assessment

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

Degradation-level assessment and online prognostics for sliding chair failure on point machines

This paper presents a degradation-level assessment and failure prognostics methodology for degrading systems. The proposed methodology consists of offline and online phases. In the offline phase, different time-domain health indicators (HIs) are extracted and the best indicator of degradation is selected by filter-based methods. Then, a degradation model is defined and its parameters are estimated using the selected HI. In the online phase, the k-means clustering is utilized to detect a change(s) in the system’s health state and to trigger failure prognostics for remaining useful life (RUL) prediction. The degradation model parameters are updated as new data are available, and the RUL is predicted iteratively. The proposed methodology is implemented on point machine sliding chair degradation using in-field condition monitoring (CM) data. The results show that the methodology can be effectively used in machine degradation-level assessment and in online RUL predictions

Improving the performance of railway track-switching through the introduction of fault tolerance

In the future, the performance of the railway system must be improved to accommodate increasing passenger volumes and service quality demands. Track switches are a vital part of the rail infrastructure, enabling traffic to take different routes. All modern switch designs have evolved from a design first patented in 1832. However, switches present single points of failure, require frequent and costly maintenance interventions, and restrict network capacity. Fault tolerance is the practice of preventing subsystem faults propagating to whole-system failures. Existing switches are not considered fault tolerant. This thesis describes the development and potential performance of fault-tolerant railway track switching solutions. The work first presents a requirements definition and evaluation framework which can be used to select candidate designs from a range of novel switching solutions. A candidate design with the potential to exceed the performance of existing designs is selected. This design is then modelled to ascertain its practical feasibility alongside potential reliability, availability, maintainability and capacity performance. The design and construction of a laboratory scale demonstrator of the design is described. The modelling results show that the performance of the fault tolerant design may exceed that of traditional switches. Reliability and availability performance increases significantly, whilst capacity gains are present but more marginal without the associated relaxation of rules regarding junction control. However, the work also identifies significant areas of future work before such an approach could be adopted in practice

Advanced monitoring of rail breakage in double-track railway lines by means of PCA techniques

This work describes a classifier designed to identify rail breakages in double-track railway lines, completing the electronic equipment carried out by authors. The main objective of this proposal is to guarantee the integrity of tracks before the railway traffic starts working. In addition, it facilitates maintenance tasks providing information about possible breakages. The detection of breakages is based on the analysis of eight currents provided by the electronic equipment, one per rail, at the ends of the section (emitting and receiving nodes). The imbalance that occurs among the value of these currents implies that there is at least a breakage in the track section under analysis. This analysis is conducted according to three phases. The first one identifies whether there is a breakage, and, in that case, the damaged track is identified. The second phase provides information about which rail is broken (internal, external or both of them) in the previously identified track. Finally, if there is only one breakage, the third phase estimates its most likely zone along the track section. This situation is considered as a classification problem, and solved by means of the Principal Component Analysis technique. This means that a significant number of measurements is required for every breakage pattern (types of breakages) to be considered. Due to the difficulty of having real data, the proposal has been validated using an 8km-long double-track hardware simulator specially designed by the authors, with specific localizations for breakages