Algorithm and modelling technique for enhanced real-time health monitoring system for freight wagons
Creators: name: Li, Chunsheng
Publisher: name: Central Queensland University
Year: 2018
Subjects: Heavy haul wagon
Health monitoring
Fault detection and isolation
Signal-based
Sensor network
On-line fault diagnosis

description: A survey of literature and existing technologies indicated that algorithms and systems for on-board monitoring devices for freight wagons are still not well developed. This was found to be primarily due to the difficulty of developing a practical on-board monitoring system due to the lack of power and electrical communications on standard freight wagons. In addition, large wagon fleet sizes make it imperative that any such system be both robust and cheap. Consequently, it was observed that traditional approaches to freight wagon health monitoring are performed from track side instruments as the wagons pass by. As it could be seen that any freight monitoring system must require only limited instrumentation, and to ensure that the research in this thesis would have practical usefulness the target design was constrained to using just two tri-axial accelerometers. Various approaches to inferring wagon dynamic behaviour and safety from just acceleration measurements were reviewed. These included model-based health monitoring methods and signal-based fault detection and isolation (FDI) methods. Model-based health monitoring methods, such as inverse modelling methods, Kalman Filter based methods, etc., utilise various estimation models to analyse sensor-collected acceleration signals for either real-time wheel-rail dynamic predications or real-time suspension parameter estimation. Conversely, signal-based FDI methods analyse sensor-collected acceleration signals directly via various signal-processing methods to detect the occurring faults directly. From the review it was discovered that linearisation of the models was required by model-based health monitoring methods which affects the accuracy of the results given the highly non-linear nature of railway vehicle suspensions and dynamic responses. The signal-based FDI methods, however, were found to be impeded by a different problem, that of requiring a large prebuilt database to cover all possible fault conditions. Such a database is time-consuming and difficult to build for the proposed wagon application. It was realised that advantage could be taken in the heavy haul train context from the fact that such trains are generally made up of near identical wagons and usually near identical service condition. It was proposed that the problem of a prebuilt database construction could be solved via a “Self-collaborate” system of sharing and comparing data between wagons via local communications. A signal-based method was therefore proposed based on the concept of cross-correlation and comparisons between adjacent heavy haul wagons. To generate data to test the proposed method, simulations were completed using a realistic and detailed MBS model of a typical 40t axle load heavy haul wagon. Simulations were undertaken using the Gensys vehicle dynamics software package. The wagon was modelled with the following detail: all vehicle components, including a carbody, two bolsters, four sideframes and four wheelsets, were given six degrees of freedom (DOFs), and all bolster springs and wedge damper springs were modelled as nonlinear stiffness elements. The track and operational conditions chosen for testing the approach were for a tight curve at prescribed curve speed and straight track at full speed. The FRA Class 4 track irregularity was assumed for the track surface. A method using cross-correlation of acceleration measurements to calculate Fault Indicators (FIs) was developed. This method was tested for various wagon suspension faults including changes to spring stiffness and damping. Two categories of cross-correlation analyses were made, including the cross-correlation analyses of acceleration signals between different directions from same sensors (Category 1) and the cross-correlation analyses of acceleration signals in the same directions between front and rear sensor (Category 2). Analysis results showed that, among all the proposed Fault Indicators, cross-correlation of vertical accelerations between the front and rear sensor was the most sensitive FI to both bolster spring faults and wedge damper faults. Though the considered faults did not cause more than 10% variations in wheel-rail dynamic parameters, namely wheel unloading and derailment index, the proposed FIs indicated over 300% variation, indicating high sensitivity. The sensitivity of these results and the robustness of the proposed method were further explored with variations in track condition and areas of further work recommended.