Fault detection and isolation of actuator failures in jet engines using adaptive dynamic programming☆
Introduction
The development of the jet engine as an aircraft power plant has been so rapid that it is difficult to appreciate that prior to the 1950s, very few people had heard of this method of aircraft propulsion. The jet engine although appearing so different from the piston engine-propeller combination, applies the same basic principles to effect propulsion, both propel their aircraft solely by thrusting a large weight of air backwards. Although today jet propulsion is popularly linked with the gas turbine engine, which will be mainly discussed in this paper, there are other types of jet propelled engines, such as the ram jet, the pulse jet, the rocket, the turbo/ram jet, and the turbo-rocket. The mechanical arrangement of the gas turbine engine is simple, for it consists of only two main rotating parts, a compressor and a turbine, and one or a number of combustion chambers [1].
Many researchers have been working in the field of fault diagnosis for jet engines [2], [3], and several methods and techniques have been raised. The problem of fault detection and isolation (FDI) can be solved mainly in two different ways, model-based [4], [5] and data-based respectively. Data-based fault detection method is studied and applied increasingly, for mass data is stored easily, as the application of computers and sensors in control systems [6], [7]. Generally speaking, fault-tolerant control allows systems to work smoothly in a certain range, but abrupt changes induced by actuator failures, which will be mainly discussed in this paper, could disable the fault-tolerant controller, such as loss in effectiveness (LIE), bias and stuck (lock-in-place).
In recent years, data-based fault detection and fault-tolerant control [8], [9] becomes one of the most popular research fields among all others. Meskin has proposed a novel real-time fault detection and isolation (FDI) scheme for aircraft jet engines based on the concept of multiple model [10], [11], which linearize the nonlinear dynamics to a set of linear models [12] and Abbasfard concerned the FDI problem of nonlinear systems by using a symbolic-based linear multiple model approach [13]. An intelligent optimal control scheme for unknown nonlinear discrete-time systems was proposed by Liu [14], [15], using an iterative adaptive dynamic programming (ADP) algorithm [16], [17] belongs to machine learning, for the corresponding Hamilton–Jacobi–Bellman(HJB) equation is too complex to solve directly. Yet there’s hardly no research of fault detection and isolation in nonlinear systems using the method of ADP.
In this paper, a new kind of data-based fault detection method is proposed, which combines adaptive dynamic programming and multiple-modeling. To start with, a stable working state is established by using ADP and history data. Once the system runs out of the stable state, it’s known for sure that fault occurs. Furthermore, a whole stable working space of the system can be built with the method of multi-modeling and data of all working modes, as long as system states remain in the working space, system is working normally, exclude the disturbance of modes switching, and improve the accuracy of the fault detection algorithm. Eventually, the fault can be isolated, if the direction and magnitude of the deviate vector are solved, which can be achieved by approximation and iteration using the method of ADP.
The rest of the paper is organized as follows. In Section 2, modeling of the jet engine and a brief theoretical background of the techniques used in this paper the will be discussed. In Section 3, the algorithm of fault detection and isolation of the aero-engine with ADP is proposed. A simulation example is given to prove the method this paper proposed in Section 4. Conclusions and future works are presented in Section 5.
Section snippets
Jet engine modeling and theoretical background
In this section, the modeling of jet engines and theory and techniques used in this paper will be discussed [18], [19]. To be specific, a typical turbofan engine was chosen in this paper, which represents a variety of advanced engines. The basic components of a turbofan engine are duct fan, intakes, compressor, combustion chamber, turbine and nozzle. The figure follows is the structure of a turbofan engine (Fig. 1).
To build the stable working modes of the system, an Integral Reinforcement
Fault detection and isolation algorithm based on integral reinforcement learning
In this section, an IRL-based fault detection and isolation algorithm for the th mode of the system is proposed. Base on the critic and actor NNs built in last section, a further fault NN is proposed to approximate the faulty model of the th environment of the system, and even isolate the fault by calculate the fault. Fig. 2 follows shows the process of the fault detection algorithm.
Simulation result
In this section, simulation results of different modes of the system are presented. The engine model is presented in chapter 2, and is chosen as represents the normal working state, and and represent two accelerant states respectively. In Critic-Actor NNs, chose , and is partial derivative of . The initial condition of and initial weights of Critic-Actor NNs are chosen randomly in
Conclusions
In this paper, a new kind of data-based fault detection method is discussed, which combines adaptive dynamic programming and multiple-modeling. The fault detection algorithm for the nonlinear system is designed for each operating modes, to approximate the system model and estimate the faults. The Critic-Actor NNs convergence was guaranteed without knowing the drift dynamics and the input dynamics in the presence of unknown nonlinearities and approximation errors. A simulation example of
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2022, Applied Mathematics and ComputationCitation Excerpt :The past several decades have witnessed a surge of research interest on the problem of fault detection (FD) owing to demands for safety and reliability of modern industrial systems [1], such as the power network system [2], the sensor network system [3,4], the multi-agent system [5,6], the three-tank system [7] and the aircraft jet engine [8].
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This work was supported in part by the funds of National Science of Foundation China (Grant nos. 61873306, U1908213, 6162100, 61420106016), in part by the National Key Research and Development Program of China (2020YFE021100), in part by the Fundamental Research Funds for the Central Universities (Nos. N2004018), and the research fund of the State Key Laboratory of Synthetical Automation for Process Industries (Grant nos. SAPI2019-3, 2018ZCX19).