A novel compound fault-tolerant method based on online sequential extreme learning machine with cycle reservoir for turbofan engine direct thrust control

Abstract

Sensors are the primary information source of the aeroengine control system, their measurement accuracy is closely related to whether the engine can operate safely and efficiently. Aiming at the direct thrust control system of aeroengines, this paper proposes an intelligent prediction algorithm combining feedforward and recurrent networks and a fault-tolerant control strategy combining analytical redundancy and controller switching. First, the cycle reservoir with regular jumps is introduced into the online sequential extreme learning machine. The CR-OSELM is developed, which maps the inputs from the low-dimensional space to the high-dimensional space. Then the CR-OSELM is adopted to build the sensor measurement prediction module, and identify and reconstruct the sensor bias and drift fault. Furthermore, in allusion to the rotor speed sensor and temperature/pressure sensor faults, analytic redundancy and controller switching strategies are designed respectively, realizing the fault-tolerant control of thrust feedback. The main contribution of this paper is to come up with the CR-OSELM algorithm with the ability to save historical input information, which overcomes the limitations of traditional feedforward neural networks in identifying time series. And the compound fault-tolerant control scheme is put forward based on the fault characteristics and their impact on the direct thrust control system, which can deal with different types of sensor faults. Simulations are performed on some benchmark datasets and a turbofan engine model to investigate the time series prediction accuracy, sensor fault diagnosis, and thrust fault-tolerant control effect. The results show that the system can detect various faults rapidly, realize the analytic redundancy with high accuracy, reduce the influence of sensor faults and restore the engine performance.