Railway catenary is the main infrastructure that delivers electric power for train operation. It is a structure commonly constructed along the railway line with contact wires suspended above the track. One or multiple pantographs mounted on the roof of a moving train collects electric current from the catenary through the sliding contact with a contact wire. With the increase of train speed and traffic density in recent years, the catenary is subject to higher impacts from pantographs, leading to critical failures such as the breakage of contact wire. This results in not only an increasing cost for reactive maintenance, but also disruptions of train service that affect many passengers.
To reduce the life cycle cost and failure rate of catenary in practice, planned and predictive maintenance is desired based on the condition monitoring of catenary. However, the monitoring data are underutilized to effectively assess the catenary condition and facilitate maintenance decision-making. This dissertation contributes in improving the dynamic condition assessment of catenary using the data from condition monitoring. New performance indicators (PIs) of catenary are defined in a way that is adaptive to the variations of monitoring data measured under different circumstances, such as the changes of catenary structure, pantograph type and train speed. The relationship between the monitoring data and the contact wire irregularities is studied using historical data and simulations. Data-based approaches are developed for the quantitative assessment of dynamic catenary condition.
First, an intrinsic wavelength contained in the pantograph-catenary contact force is identified and defined as the catenary structure wavelength (CSW). It is caused by the periodic variations of contact wire stiffness attributed to the cyclic structure of catenary that must regulate the height of contact wire in every spans and interdropper distances. An approach that adaptively extracts the CSWs of pantograph-catenary contact force is proposed based on the empirical mode decomposition algorithm. It extracts the CSW signals corresponding to the span lengths and interdropper distances, respectively, summing to form a characteristic signal of CSWs. The residual signal of the contact force excluding the CSWs is regarded as the non-CSW signal. The mean and standard deviation of the CSWs signal are used as PIs to indicate the condition of the main catenary geometric parameters. A PI based on the quadratic time-frequency representation of the non-CSW signal is proposed for detecting and localizing the local irregularities of contact wire. The proposed PIs are tested by simulation and measurement data and proven effective and adaptive owning to the use of CSWs and non-CSW signal.
Second, the concept of CSW is expanded to the pantograph head acceleration from which the CSWs and non-CSW signal can also be extracted using the same approach developed for the contact force. Considering the characteristics of pantograph head acceleration, the wavelet packet entropy of the CSWs and non-CSW signal is proposed as PIs for detecting contact wire irregularities with different lengths. The entropy of CSWs is used for detecting irregularities with a length longer than 5 m, while the entropy of non-CSW signal is for the short-length local irregularities. An approach to detect and verify contact wire irregularities using the measurement data of pantograph head vertical acceleration from frequent inspections is proposed. The approach is tested using historical inspection data from which irregularities at all lengths are detected and verified. Maintenance resources can thus be specifically allocated to verified detection results to save cost and time.
Third, through analyzing historical inspection data and data-based simulation results, it is found that while the contact wire irregularity deteriorates the pantograph-catenary interaction, the formation of irregularity is also associated with the effects of the interaction like variations of contact and friction forces. Concretely, the contact wire height irregularity with an amplitude of 8 mm can cause considerable increase in the standard deviation of pantograph-catenary contact force. In addition, the irregularity with a certain wavelength can induce the dynamic response with the same wavelength in the contact force. This in turn makes the irregularity part deteriorating faster than the other parts of catenary. At a smaller scale, when the wear irregularity of contact wire has an average wire thickness loss of about 1.5 mm, it can also increase the standard deviation of contact force by more than 5%. Due to the fixing effect at the registration arms and droppers, the wear irregularity commonly contains structural wavelengths of catenary including span lengths and interdropper distances. It is also found that the wear irregularity tends to grow and spread toward in the common or dominant running direction of trains in the specific line. Nevertheless, an existing defect may not affect every pantograph passage and every type of data measured. It is thus advised to measure multiple types of data and perform more frequent inspections to avoid undetected defects.
Last, a data-driven approach using the Bayesian network (BN) to fuse the available inspection data of catenary into an integrated PI is proposed. The BN topology is first structured based on the physical relations between five data types including the train speed, dynamic stagger and height of contact wire, pantograph head acceleration, and pantograph-catenary contact force. Then, tailored PIs are individually defined and extracted from the five types of data as the BN input. As the output of BN, an integrated PI is defined as the overall condition level of catenary considering all defects that can be reflected by the five types of data. Finally, using historical inspections data and maintenance records from a section of high-speed line, the BN parameters are estimated to establish a probabilistic relationship between the input and the output PI. By testing the BN-based approach using new inspection data from the same railway line, it is shown that the integrated PI can adequately represent the catenary condition, leading to considerable reduction in the false alarm rate of catenary defect detection compared with the current practice. The approach can also work acceptably with noisy or partly missing data.
In summary, this dissertation answers how to adequately transform the condition monitoring data of catenary into quantitative assessments of the dynamic catenary condition. The proposed approaches are intended for generic implementations in railway catenaries worldwide.