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The purpose of this paper is to solve difficult estimation problem on aircraft sudden fault by proposing a new pre-estimating method according to the energy evolution degree of the sensitive parameters to estimate the sudden fault. The sudden fault affects seriously the flight safety of aircraft.

It is based on the dissipative structure theory, and the evolution process of energy parameters is utilized. First, the evolution key points of sudden fault are determined by the time-varying entropy of sensitive parameters and the frequency band energy distribution. Then, we can obtain the evolution degree of sample while the evolution key points import the logistic regression (LR) model, and one can establish the pre-estimation model by means of relevance vector machine (RVM). While the evolution feature vector imports the RVM pre-estimation model, one can pre-estimate the sudden fault of aircraft.

The simulation results showed that this method can not only track the evolution process of aircraft sudden fault but also estimate its evolution degree, and it has a higher pre-estimating accuracy.

It provides a new way to forecast the sudden fault and increase the security of aircraft.

This paper proposes a pre-estimating method on aircraft sudden fault. It is based on the dissipative structure theory and the energy-sensitive parameters of the sudden faults are used. This method can enhance the security of aircraft and increase the protective ability of sudden fault on aircraft.

This paper aims to demonstrate the effectiveness of a fault-tolerant flight control method by using simple adaptive control (SAC) with PID controller.

Numerical simulations and flight tests are executed for pitch angle and roll angle control of research aircraft MuPAL-α under the following fault cases: sudden reduction in aileron effectiveness, sudden reduction in elevator effectiveness and loss of longitudinal static stability.

The simulations and flight tests reveal the effectiveness of the proposed SAC with PID controller as a fault-tolerant flight controller.

This research includes implications for the development of vehicles’ robustness.

This study proposes novel SAC-based flight controller and actually demonstrates the effectiveness by flight test.

The purpose of this paper is to propose an adaptive neural-sliding mode-based observer for the estimation and reconstruction of unknown faults and disturbances for time-varying nonlinear systems such as aircraft, to ensure preciseness in the diagnosis of fault magnitude as well as the shape without enhancement of system complexity and cost. Fault-tolerant control (FTC) strategy based on adaptive neural-sliding mode is also proposed in the existence of faults for ensuring the stability of the faulty system.

In this paper, three strategies are presented: adaptive radial basis functions neural network (ARBFNN), conventional radial basis functions neural network (CRBFNN) and integral-chain differentiator. For the purpose of enhancement of fault diagnosis and isolation, a new sliding mode-based concept is introduced for the weight updating parameters of radial basis functions neural network (RBFNN).The main objective of updating the weight parameters adaptively is to enhance the effectiveness of fault diagnosis and isolation without increasing the computational complexities of the system. Results depict the effectiveness of the proposed ARBFNN approach in fault detection (FD) and approximation compared to CRBFNN, integral-chain differentiator and schemes existing in literature. In the second step, the FTC strategy is presented separately for each observer in the presence of unknown faults and failures for ensuring the stability of the system, which is validated on Boeing 747 100/200 aircraft.

The proposed adaptive neural-sliding mode approach is investigated, which depicts more effectiveness in numerous situations such as faults, disturbances and uncertainties compared to algorithms used in literature. In this paper, both the fault approximation and isolation and the fault tolerance approaches are studied.

For the enhancement of safety level as well as for avoiding any kind of damage, timely FD and fault tolerance have always had a significant role; therefore, the algorithms proposed in this research ensure the tolerance of faults and failures, which plays a vital role in practical life for avoiding any kind of damage.

In this study, a new neural-sliding mode concept is adopted for the adaptive faults approximation and reconstruction, and then the FTC algorithms are studied for each observer separately, whereas in previous studies, only the fault detection and isolation (FDI) or the fault tolerance problems were studied. Results demonstrate the effectiveness of the proposed strategy compared to the approaches given in the literature.

Steer-by-wire (SBW) system mainly relies on sensors, controllers and motors to replace the traditionally mechanical transmission mechanism to realize steering functions. However, the sensors in the SBW system are particularly vulnerable to external influences, which can cause systemic faults, leading to poor steering performance and even system instability. Therefore, this paper aims to adopt a fault-tolerant control method to solve the safety problem of the SBW system caused by sensors failure.

This paper proposes an active fault-tolerant control framework to deal with sensors failure in the SBW system by hierarchically introducing fault observer, fault estimator, fault reconstructor. Firstly, the fault observer is used to obtain the observation output of the SBW system and then obtain the residual between the observation output and the SBW system output. And then judge whether the SBW system fails according to the residual. Secondly, dependent on the residual obtained by the fault observer, a fault estimator is designed using bounded real lemma and regional pole configuration to estimate the amplitude and time-varying characteristics of the faulty sensor. Eventually, a fault reconstructor is designed based on the estimation value of sensors fault obtained by the fault estimator and SBW system output to tolerate the faulty sensor.

The numerical analysis shows that the fault observer can be rapidly activated to detect the fault while the sensors fault occurs. Moreover, the estimation accuracy of the fault estimator can reach to 98%, and the fault reconstructor can make the faulty SBW system to retain the steering characteristics, comparing to those of the fault-free SBW system. In addition, it was verified for the feasibility and effectiveness of the proposed control framework.

As the SBW fault diagnosis and fault-tolerant control in this paper only carry out numerical simulation research on sensors faults in matrix and laboratory/Simulink, the subsequent hardware in the loop test is needed for further verification.

Aiming at the SBW system with parameter perturbation and sensors failure, this paper proposes an active fault-tolerant control framework, which integrates fault observer, fault estimator and fault reconstructor so that the steering performance of SBW system with sensors faults is basically consistent with that of the fault-free SBW system.

This paper aims to improve the fault detection adaptive threshold of aircraft flap control system to make the system fault diagnosis more accurate.

According to the complex mechanical–electrical–hydraulic structure and the multiple fault modes of the aircraft flap control system, the advanced fault diagnosis method based on the bond graph (BG) model is presented, and based on the system diagnostic BG model, the parameter uncertainty intervals are estimated and a new adaptive threshold is constructed by linear fraction transformation.

To construct a more reasonable and accurate adaptive threshold range to more accurately detect system failures, some typical failure modes’ diagnosis process are selected and completed for verification; the simulation results show that the proposed method is effective and feasible for complex systems’ fault diagnosis.

This study can provide a theoretical guidance and technical support for fault diagnosis of complex systems, which avoid misdiagnosis and missed diagnosis.

This study enables more accurate fault detection and diagnosis of complex systems when considering factors such as parameter uncertainty.

Monitoring of a process leading to the detection of faults and determination of the root causes are essential for the production of consistent good quality end products with improved yield. The history of process monitoring fault detection (PMFD) strategies can be traced back to 1930s. Thereafter various tools, techniques and approaches were developed along with their application in diversified fields. The purpose of this paper is to make a review to categorize, describe and compare the various PMFD strategies.

Taxonomy was developed to categorize PMFD strategies. The basis for the categorization was the type of techniques being employed for devising the PMFD strategies. Further, PMFD strategies were discussed in detail along with emphasis on the areas of applications. Comparative evaluations of the PMFD strategies based on some commonly identified issues were also carried out. A general framework common to all the PMFD has been presented. And lastly a discussion into future scope of research was carried out.

The techniques employed for PMFD are primarily of three types, namely data driven techniques such as statistical model based and artificial intelligent based techniques, priori knowledge based techniques, and hybrid models, with a huge dominance of the first type. The factors that should be considered in developing a PMFD strategy are ease in development, diagnostic ability, fault detection speed, robustness to noise, generalization capability, and handling of nonlinearity. The review reveals that there is no single strategy that can address all aspects related to process monitoring and fault detection efficiently and there is a need to mesh the different techniques from various PMFD strategies to devise a more efficient PMFD strategy.

The review documents the existing strategies for PMFD with an emphasis on finding out the nature of the strategies, data requirements, model building steps, applicability and scope for amalgamation. The review helps future researchers and practitioners to choose appropriate techniques for PMFD studies for a given situation. Further, future researchers will get a comprehensive but precise report on PMFD strategies available in the literature to date.

The review starts with identifying key indicators of PMFD for review and taxonomy was proposed. An analysis was conducted to identify the pattern of published articles on PMFD followed by evolution of PMFD strategies. Finally, a general framework is given for PMFD strategies for future researchers and practitioners.

Power converters are an integral part of the energy conversion process in solar photovoltaic (PV) systems which is used to match the solar PV generation with the load requirements. The increased penetration of renewable invokes intermittency in the generated power affecting the reliability and continuous energy supply of such converters. DC-DC converters deployed in solar PV systems impose stringent restrictions on supplied power, continuous operation and fault prediction scenarios by continuously observing state variables to ensure continuous operation of the converter.

A converter deployed for a mission-critical application has to ensure continuous regulated output for which the converter has to ensure fault-free operation. The fault diagnostic algorithm relies on the measurement of a state variable to assess the type of fault. In the same line, a predictive controller depends on the measurement of a state variable to predict the control variable of a converter system to regulate the converter output around a fixed or a variable reference. Consequently, both the fault diagnosis and the predictive control algorithms depend on the measurement of a state variable. Once measured, the available data can be used for both algorithms interchangeably.

The objective of this work is to integrate the fault diagnostic and the predictive control algorithms while sharing the measurement requirements of both these control algorithms. The integrated algorithms thus proposed could be applied to any converter with a single inductor in its energy buffer stage.

laboratory prototype is created to verify the feasibility of the integrated predictive control and fault diagnosis algorithm. As the proposed method combine the fault detection algorithm along with predictive control, a load step variation and manual fault creation methods are used to verify the feasibility of the converter as with the simulation analysis. The value for the capacitors and inductors were chosen based on the charge-second and volt-second balance equations obtained from the steady-state analysis of boost converter.

In the event of a DC short-circuit fault in a flexible DC power grid, the high peak value of the fault current puts forward more stringent requirements on the DC circuit breaker. The existing fault current cutoff mainly focuses on changing the topology structure. To suppress the development of fault current and reduce the investment cost of the DC grid, this paper aims to propose a dual-loop active current-limiting control based on energy difference.

Firstly, the equivalent circuit at fault is established, and the parameters related to the fault current are analyzed. Then, the relationship between the output voltage change of the bridge arm and the difference between the AC and DC energy is deduced. Finally, the experimental results are discussed on the real-time simulation platform Opal-RT.

The proposed current-limiting measures can greatly reduce the fault current, reduce the breaking current of the circuit breaker and increase the capacitor voltage during the fault period, which is beneficial to the stability of the AC system. It is verified that the proposed method is also applicable to a certain high-resistance fault.

This paper applies the method of AC fault to DC fault and deduces the relationship between energy difference and voltage variation corresponding to different step lengths based on digital simulation. In addition, two variables are used as control structure parameters to reduce the probability of system misoperation.

This paper aims to propose an integrated protection scheme for converters of a low-power, low-cost photovoltaic system. Power electronic converters use a variety of methods to limit overload and fault current. The use of insulated and non-insulated sensors along with additional circuits to detect and limit fault current can cause current to be limited or completely cut off before damage to semiconductor devices. In addition, fuses that have slower performance are used as backup for any type of protection.

First, all the candidate points for protection are investigated. In this paper, after examining the performance of glass fuses as linear resistors, they are used as a current feedback element. A simple, isolated and reliable circuit for fault detection at various points of the system has been proposed that can be implemented and operated in single shot or auto-reclose operating mode.

The experimental results of this circuit on a dc/dc converter and an H-bridge inverter show that it can cut off all instantaneous short circuit errors in less than 50 µs and prevent damage to the semiconductor switch.

In low-cost and low-power converters, it is usually not cost-effective to use complex and expensive devices. For this reason, these converters are more vulnerable to faults. On the other hand, in complex systems such as photovoltaics, several converters are used simultaneously in different parts, and the occurrence of a fault in each of them causes the whole system to fail.

The purpose of this paper is to develop a new software reliability growth model considering different fault distribution function before and after the change point.

In this paper, the authors have developed a framework to incorporate change-point in developing a hybrid software reliability growth model by considering different distribution functions before and after the change point.

Numerical illustration suggests that the proposed model gives better results in comparison to the existing models.

The existing literature on change point-based software reliability growth model assumes that the fault correction trend before and after the change is governed by the same distribution. This seems impractical as after the change in the testing environment, the trend of fault detection or correction may not follow the same trend; hence, the assumption of same distribution function may fail to predict the potential number of faults. The modelling framework assumes different distributions before and after change point in developing a software reliability growth model.