High-speed train set robustness filtering fault detection method considering event driving and output saturation

Abstract

The invention discloses a high-speed train set robustness filtering fault detection method considering event driving and output saturation and belongs to the signal processing field. The method comprises the following steps of establishing a high-speed train set state space model considering the output saturation and designing a trigger strategy; designing the robust filter of event driving and designing a filter gain in an off-line mode: setting the initial value of the filter, calculating a middle variable, calculating one step prediction state estimation error covariance upper bound, calculating the filter gain, calculating a state estimation error covariance upper bound, and calculating a state estimation value in an online mode: setting a state estimation initial value, calculating one step state prediction value, calculating information, and calculating a state estimation value; and designing a fault detection strategy, calculating a residual error, calculating a fault detectionstatistics amount, calculating a residual error secondary moment upper bound, calculating a fault detection threshold and setting a fault detection logic. By using the fault detection method, a systemfault can be detected in an online mode, and the safety and reliability of a high-speed train set are increased.

Description

technical field [0001] The invention belongs to the field of signal processing, and in particular relates to a robust filter fault detection method for multiple train units considering event driving and output saturation. Background technique [0002] Whether the EMU can work stably and reliably is directly related to driving safety. Therefore, for the failure of the EMU, the safety-oriented principle is often adopted, that is, once a failure of the EMU is detected, it will be stopped midway. However, this measure will cause the line to be out of service, causing huge economic losses, and also brings serious safety hazards to the operation of other trains. In fact, some faults will not endanger driving safety in a short period of time. If these faults can be effectively detected online, there is no need to stop immediately and repairs can be carried out after entering the station. However, the existing fault detection methods rely heavily on the model uncertainty structure,...

AI Technical Summary

Problems solved by technology

However, the existing fault detection methods rely heavily on the model uncertainty structure, and cannot effectively detect system faults when the sensor has the limitation of random saturation effect and the network has an event-driven mechanism.

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