Fault parameter estimation method based on unscaled Kalman filter with two time scales

Abstract

The invention discloses a system fault parameter estimation method based on a dual-time scale unscaled Kalman filter, which comprises the following steps of: discretizing a system state space equationof an electric walking vehicle system to obtain a time-discretized system state space equation; adding parameters to be estimated to system state variables to obtain an augmented system state space equation, wherein the parameters to be estimated are parameters in a fault set; an unscaled Kalman filter algorithm with two time scales being used to estimate the parameters and the state variables ofthe original system, In the macro-scale, only the original system state variables before augmentation are estimated by unscented Kalman filter, and in the micro-scale, the augmented system state variables being estimated by the unscented Kalman filter. The invention solves the problems of poor real-time fault diagnosis and delayed fault source positioning, reduces the calculation amount when fault diagnosis is performed, and improves the operation efficiency.

Description

technical field [0001] The invention relates to the field of fault parameter estimation, in particular to a system fault parameter estimation method based on dual-time-scale unscented Kalman filtering. Background technique [0002] In the context of the aging population and the increasing number of disabled people in all countries in the world, electric scooter products that provide convenient travel for the elderly and disabled people who have difficulty traveling have attracted widespread attention worldwide in recent years. The popularity of electric scooters has also brought some safety issues. As a means of transportation specially designed for the elderly and the disabled, once a failure occurs during road driving, it may cause serious consequences. Therefore, a real-time The fault diagnosis system of electric scooter is imminent. [0003] At present, methods for nonlinear system fault parameter estimation include extended Kalman filter method, particle filter method ...

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Problems solved by technology

[0004] In order to overcome the defects of the above-mentioned prior art, the present invention provides a system fault parameter estimation method based on dual-time-scale unscented Kalman filter, which solves the problems of poor real-time fault diagnosis and untimely fault source location, and reduces the calculation of fault diagnosis volume, improving the operational efficiency

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