An efficient condition monitoring strategy of railway vehicle suspension based on recursive least-square algorithm

This paper presents a model-based strategy for condition monitoring of suspensions in a railway bogie. This approach is based on recursive least-square (RLS) algorithm focusing on the ‘Input-output’ model. RLS is able to identify the unknown parameters from a noisy input-output system by memorizing the correlation properties. The identification of the suspension parameter is achieved by establishing the relationship between the excitation and response of a bogie. A fault detection method for vertical primary suspensions of one bogie is illustrated as an example of this scheme. Numerical simulation results from the rail vehicle dynamics software ‘ADTreS’ are utilized as ‘virtual measurements’, considering a trailed car of Italian ETR500 high-speed train. The test data from an E464 locomotive are also employed to validate the feasibility of this strategy for the real situation. Results of the parameter identification performed indicate that estimated suspension parameters are consistent or approximate with the values for reference, thereby supporting the application of this fault diagnosis technique to the future condition monitoring system of the rail vehicle suspensio