control surface fault diagnosis is essential for timely detection of manoeuvring and stability risks for an unmanned aircraft. Timely detection is crucial since control surface related faults impact stability of fligh...
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ISBN:
(纸本)9781479928569
control surface fault diagnosis is essential for timely detection of manoeuvring and stability risks for an unmanned aircraft. Timely detection is crucial since control surface related faults impact stability of flight and safety. Reliable diagnosis require well fitting dynamical models but with the high cost of detailed modelling and wind tunnel testing, it would be highly desirable if good diagnosis could be obtained with very generic models that are adapted to individual conditions of aircraft and of its operation. This paper presents an approach where a basic generic model is applied and necessary parameters in residual generators are identified on the fly. Initial estimates of parameters are known from off-line analysis of previous flights. The paper analyses how such self-tuning residual generators are combined with change detection to obtain timely fault diagnosis. The paper investigates the parameter convergence and detection properties for the suggested combination of identification and change detection techniques and shows design aspects and trade-offs to be made to make this scheme an effective and robust system for diagnosis or even prognosis. Results are verified using a number of test flights with different members of a population of UAVs that have inherent model uncertainty from one member to another and from one flight to another. Events with actual faults on control surfaces demonstrates the efficacy of the approach.
As many systems depend on electronics, concern for fault tolerance is growing rapidly. For example, a car with its steering controlled by electronics and no mechanical linkage from steering wheel to front tires (steer...
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Stabiliser faults in multi-machine power systems are examined in this paper where fault-masking and system reconfiguration of the nonlinear system are obtained using a virtual actuator approach. Phasor Measurement Uni...
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Inverted pendulum systems have an important place in practice for control problems due to their nonlinear structures. It is a very difficult task to control the orientation and speed of objects and robots which are fr...
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The three tank benchmark system is considered in this paper in connection with combined feedback control and fault detection and identification (FDI). The combined design problem is formulated as an H ∞ design proble...
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The three tank benchmark system is considered in this paper in connection with combined feedback control and fault detection and identification (FDI). The combined design problem is formulated as an H ∞ design problem by using a standard system setup.
Discrete time robust linear quadratic Gaussian controller with loop transfer recovery (LQG/LTR) considering the augmented system with the addition of backward Euler integrator at controller output is applied to a prac...
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ISBN:
(纸本)9781509007561
Discrete time robust linear quadratic Gaussian controller with loop transfer recovery (LQG/LTR) considering the augmented system with the addition of backward Euler integrator at controller output is applied to a practical quadruple tank system. The plant developed in this work is a benchmark of a multivariable two-input-two-output (TITO) system with coupled channels, frequently used in academic works, mainly due to its versatility for generating minimum phase or non-minimum phase systems and to the coupled channels characteristic. For the LQG/LTR controller, the singular values of the open loop transfer function are matched in the frequency range from zero up to the Nyquist rate using the same approach considered for continuous time system design. Simulation and practical experiments are performed in order to validate the controller design considering set-point tracking and disturbance rejection. It is possible to observe that the resulting controller performs very well tracking step and sinusoidal inputs and rejecting sinusoidal disturbances. Practical and simulated results are very similar to each other.
The principal lateral motion of an aircraft is described by a minimum of fourth order coupled state variable model. It is interesting to investigate how a decoupling controller can be designed. The Youla-parametrizati...
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ISBN:
(纸本)9780889868632
The principal lateral motion of an aircraft is described by a minimum of fourth order coupled state variable model. It is interesting to investigate how a decoupling controller can be designed. The Youla-parametrization is a simple method to design controllers. The KB-parametrization is a successful extension of this method for two-degree-of freedom (2DOF) systems. The paper extends this methodology for multivariable case and applies to the decoupling lateral control.
In time delay control systems, the observed information is related to a past instant and using this delayed signal may cause unsatisfactory results in control system applications. This paper deals with how time delay ...
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ISBN:
(纸本)0889866104
In time delay control systems, the observed information is related to a past instant and using this delayed signal may cause unsatisfactory results in control system applications. This paper deals with how time delay information can be used in reorganizing the rule base so as to improve system performance. Basically, it proposes a new scheme of appropriate shifting of the rule base to compensate the information lag caused by time delay in the system. The parameters affecting the shifting scheme are discussed in detail and the shifting scheme is tabulated with respect to system model parameters. Applications of the new methodology in different systems are simulated and the effectiveness of the scheme is fully illustrated.
In order to overcome the effect of the assumption between emissivity and wavelength on the measurement of true temperature and spectral emissivity for most engineering materials, a neural network based method is propo...
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In order to overcome the effect of the assumption between emissivity and wavelength on the measurement of true temperature and spectral emissivity for most engineering materials, a neural network based method is proposed for data processing while a blackbody furnace and three optical filters with known spectral transmittance curves were used to make up a true target. The experimental results show that the calculated temperatures are in good agreement with the temperature of the blackbody furnace, and the calculated spectral emissivity curves are in good agreement with the spectral transmittance curves of the filters. The method proposed has been proved to be an effective method for solving the problem of true temperature and emissivity measurement, and it can overcome the effect of the assumption between emissivity and wavelength on the measurement of true temperature and spectral emissivity for most engineering materials.
Development of model-based fault diagnosis methods is a challenge when industrial systems are large and exhibit complex process behavior. Latent projection (LP), a statistical method that extract features of data via ...
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Development of model-based fault diagnosis methods is a challenge when industrial systems are large and exhibit complex process behavior. Latent projection (LP), a statistical method that extract features of data via dimensionality reduction, is an alternative approach to diagnosis as it can be formulated to not rely on process knowledge. However, LP methods may perform poorly at identifying abnormal process variables due a “fault smearing” effect -variables unaffected by a fault are unintentionally characterized as being abnormal. The effect occurs because data compression permits faulty and non-faulty variables to interact. This paper presents an autoencoder (AE), a nonlinear LP method based on neural networks, as a monitoring method of a simulated nonlinear triple tank process (TTP). Simulated process data was used to train the AE to generate a monitoring statistic representing the condition of the TTP. Sparsity was introduced in the AE to reduce variable interactivity. The AE’s ability to detect a fault was demonstrated. The individual contributions of process variables to the AE’s monitoring statistic were analyzed to reveal the process variables that were no longer consistent with normal operating conditions. The key result in this study was that sparsity reduced fault smearing onto unaffected variables and increased the contributions of actual faulty variables.
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