To study the railway overhead wire's flexural wave motion that significantly affects the quality of current collection between pantograph and catenary, dynamic responses of a real catenary excited by different excitations are measured and analyzed by using a set of noncontact photogrammetric devices together with the finite-element simulation. Based on the measured and simulated results, the wave motion along the contact wire is investigated and some findings have been obtained. First, it is the first two dominant modal components in the catenary displacement responses that induce a beat phenomenon, and this beat phenomenon is increasingly obvious near the registration arm. Moreover, with the simulated displacement contour, catenary local vibration characteristics affected by the travelling wave can be clearly interpreted. Furthermore, the wave group velocity of contact wire in catenary is determined and verified to be approximately 139.18 m/s; in addition, treating the contact wire in the overhead wire system as an ideal string or a tensioned Euler beam will overestimate the corresponding group velocity with a relative error by about 13.21%.

This paper describes an impact detection system using strain signals based on fibre optic sensors(FBG) for the real-time monitoring of the catenary system. The proposed detection system consists of three subsystems: a measuring system, a data processing and analysis system, and a status display and data access system. Because the strain signals obey the normal distribution, to monitor the catenary system in real time, a novel method that combines mobile standard deviation with the mobile Pauta criterion is proposed to distinguish real impact from the strain signal background. The use of this adaptive judging method reduces the misjudgment rate of impacts and improves the impact recognition accuracy. These impacts can be identified by the data analysis system, which provides impact location and their causes using the features of the catenary system. This method can simplify the detection system compared with the traditional location method. An application to a commercial metro line system indicated that the impacts on the catenary system were main caused by overlaps, expansion joints or steady arms, and were verified by correspondence with the floor plan of the catenary and manual inspection results. These results verified the reliability and effectiveness of the proposed impact detection system.

The health condition of rolling-element bearings is important for machine performance and operating safety. Due to external interferences, the impulse-related fault information is always buried in the raw vibration signal. To solve this problem, a hybrid time-frequency analysis method combining ensemble local mean decomposition (ELMD) and the Teager-Kaiser energy operator (TKEO) is proposed for the fault diagnosis of high-speed train bearings. The ELMD method is a significant improvement over local mean decomposition (LMD) for addressing the mode-mixing problem. The TKEO method is effective for separating amplitude-modulated (AM) and frequency-modulated (FM) signals from a raw signal. But it is only valid for monocomponent AM-FM signals. The proposed time-frequency method integrates the advantages of ELMD and TKEO to detect localized defects in rolling-element bearings. First, a raw signal is decomposed into an ensemble of PFs and a residual component using ELMD. A novel sensitive parameter (SP) is introduced to select the sensitive PF that contains the most fault-related information. Subsequently, the TKEO is applied to extract both the amplitude and frequency modulations from the selected PF. The experimental results of rolling element and outer race fault signals confirmed that the proposed method could effectively recover fault information from raw signals contaminated by strong noise and other interferences.

Local means decomposition is an adaptive and nonparametric time–frequency decomposition method for nonstationary and nonlinear signals. However, in practice, local means decomposition is susceptible to mode mixing phenomena and produces different scale oscillations in one mode or similar scale oscillations in different modes, rendering the decomposition results difficult to interpret in terms of physical meansing. The noise-assisted ensemble local means decomposition method not only effectively resolved mode mixing but also generated a new problem, which tolerates residual noise in signal reconstruction. Targeting these shortcomings, this article proposes complementary ensemble local means decomposition, a novel noise-assisted time–frequency analysis method. First, an ensemble of white noise is added to the original signal via complementary positive and negative pairs. Second, local means decomposition is applied to decompose the noisy signals into a series of product functions, and the final results are obtained by averaging. The simulation results confirm that complementary ensemble local means decomposition offers an innovative improvement over ensemble local means decomposition in terms of eliminating residual noise. The superiority of the proposed method was further validated on fault signals obtained from faulty railway bearings (rolling element and outer race fault signals).