Abstract
Normal operation of the pressure sensor is important for the safe operation of the locomotive electro-pneumatic brake system. Sensor fault diagnosis technology facilitates detection of sensor health. However, the strong nonlinearity and variable process noise of the brake system make the sensor fault diagnosis become challenging. In this paper, an adaptive unscented Kalman filter- (UKF-) based fault diagnosis strategy is proposed, aimed at detecting bias faults and drift faults of the equalizing reservoir pressure sensor in the brake system. Firstly, an adaptive UKF based on the Sage-Husa method is applied to accurately estimate the pressure transients in the equalizing reservoir of the brake system. Then, the residual is generated between the estimated pressure by the UKF and the measured pressure by the sensor. Afterwards, the Sequential Probability Ratio Test is used to evaluate the residual so that the incipient and gradual sensor faults can be diagnosed. An experimental prototype platform for diagnosis of the equalizing reservoir pressure control system is constructed to validate the proposed method.
Highlights
The electro-pneumatic brake system has shown the extensive applications in passenger trains, metros, and heavy haul trains because of its fast response time and high reliability [1]
This paper proposes an adaptive unscented Kalman filter- (UKF-)based scheme to detect bias faults and drift faults of the equalizing reservoir pressure sensor
We construct an experimental platform for the equalizing reservoir pressure system and verify the effectiveness and feasibility of the proposed sensor fault diagnosis strategy
Summary
The electro-pneumatic brake system has shown the extensive applications in passenger trains, metros, and heavy haul trains because of its fast response time and high reliability [1]. This paper proposes an adaptive UKF-based scheme to detect bias faults and drift faults of the equalizing reservoir pressure sensor. For the locomotive electro-pneumatic brake system, different from existing UKF-based fault diagnosis methods, the proposed scheme can detect incipient and gradual sensor faults. By combining the Sage-Husa mechanism and Sequential Probability Ratio Test, the proposed scheme can detect the incipient and gradual sensor faults of the locomotive electro-pneumatic brake system. (i) The mechanism of the electro-pneumatic brake system is analysed adequately, and the accurate analytical pressure model is established (ii) The adaptive UKF is applied to estimate the system output pressure, improving the robustness of the fault diagnosis approach under the uncertainty and noise (iii) The Sequential Probability Ratio Test is introduced to evaluate the residual to minimize the occurrence of misinformation or false detection in fault diagnosis.
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