Adaptive strategy for fault detection, isolation and reconstruction of aircraft actuators and sensors

Taimoor, Muhammad; Li, Aijun

doi:10.3233/jifs-191627

Cited by 11 publications

(7 citation statements)

References 23 publications

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“…Network architectures applied for fault diagnosis can be separated as follows: (a) static (i.e., feed-forward) network in which the inputs for each layer only rely on the outputs of the previous layer and (b) dynamic network in which the inputs to a specific layer depend on the outputs of the previous layer and the previous iterations of the network itself [97]. Most ANN approaches proposed to date have been based on static networks, including the multi-layer perceptron (MPL) [52,[104][105][106], radial basis function network (RBF) [97,107,108], and general regression neural network (GRNN) [25]. Several dynamic networks (e.g., recurrent neural network) have been developed for fault diagnosis.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Glowacz [105] used the nearest neighbor classifier, backpropagation neural network (BPNN) and modified classifier based on words coding to identify the real state of a three-phase induction motor by acoustic signals. Taimoor et al [106] exploited the extended Kalman filter to update the weight parameters of MLP neural network for improving the fault diagnosis capabilities, which is applied to detect an aircraft actuators and sensors fault. Compared with MLP, RBF trains quicker than MPL.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

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Technology development and commercial applications of industrial fault diagnosis system: a review

Liu

Cichoń

Królczyk

et al. 2021

Int J Adv Manuf Technol

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Machinery will fail due to complex and tough working conditions. It is necessary to apply reliable monitoring technology to ensure their safe operation. Condition-based maintenance (CBM) has attracted significant interest from the research community in recent years. This paper provides a review on CBM of industrial machineries. Firstly, the development of fault diagnosis systems is introduced systematically. Then, the main types of data in the field of the fault diagnosis are summarized. After that, the commonly used techniques for the signal processing, fault diagnosis, and remaining useful life (RUL) prediction are discussed, and the advantages and disadvantages of these existing techniques are explored for some specific applications. Typical fault diagnosis products developed by corporations and universities are surveyed. Lastly, discussions on current developing situation and possible future trends are in the CBM performed.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Artificial Neural Networkmentioning

confidence: 99%

Technology development and commercial applications of industrial fault diagnosis system: a review

Liu

Cichoń

Królczyk

et al. 2021

Int J Adv Manuf Technol

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“…For which H ∞ technique is used for the purpose of stability as well as the consistency, theory of bi-index is used for the designing of FTC system. Many other algorithms and methodologies such as Kalman Filter SMO (Zhang et al , 2016; Djeghali et al , 2016), NNs (Chen et al , 2016; Taimoor and Aijun, 2019; Allen et al , 2016; Baghernezhad & horasani, 2016; Giorgi De et al , 2019; Fentaye et al , 2018; Yildirim and Kurt, 2019; Jia, and Duan, 2017; Amin et al , 2019; Amin et al , 2016; Taimoor and Aijun, 2020; Taimoor et al , 2020) and fuzzy logic (Ballesteros-Moncada et al , 2015) are implemented for the estimation of nonlinear parameters. In the above-mentioned techniques, NN techniques are better for faults identification because of the properties such as nonlinear function estimation property and learning abilities.…”

Section: Introductionmentioning

confidence: 99%

Adaptive rapid neural observer-based sensors fault diagnosis and reconstruction of quadrotor unmanned aerial vehicle

Taimoor

Maqsood³

et al. 2021

AEAT

Self Cite

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Purpose The objective of this research is to investigate various neural network (NN) observer techniques for sensors fault identification and diagnosis of nonlinear system in consideration of numerous faults, failures, uncertainties and disturbances. For the importunity of increasing the faults diagnosis and reconstruction preciseness, a new technique is used for modifying the weight parameters of NNs without enhancement of computational complexities. Design/methodology/approach Various techniques such as adaptive radial basis functions (ARBF), conventional radial basis functions, adaptive multi-layer perceptron, conventional multi-layer perceptron and extended state observer are presented. For increasing the fault detection preciseness, a new technique is used for updating the weight parameters of radial basis functions and multi-layer perceptron (MLP) without enhancement of computational complexities. Lyapunov stability theory and sliding-mode surface concepts are used for the weight-updating parameters. Based on the combination of these two concepts, the weight parameters of NNs are updated adaptively. The key purpose of utilization of adaptive weight is to enhance the detection of faults with high accuracy. Because of the online adaptation, the ARBF can detect various kinds of faults and failures such as simultaneous, incipient, intermittent and abrupt faults effectively. Results depict that the suggested algorithm (ARBF) demonstrates more confrontation to unknown disturbances, faults and system dynamics compared with other investigated techniques and techniques used in the literature. The proposed algorithms are investigated by the utilization of quadrotor unmanned aerial vehicle dynamics, which authenticate the efficiency of the suggested algorithm. Findings The proposed Lyapunov function theory and sliding-mode surface-based strategy are studied, which shows more efficiency to unknown faults, failures, uncertainties and disturbances compared with conventional approaches as well as techniques used in the literature. Practical implications For improvement of the system safety and for avoiding failure and damage, the rapid fault detection and isolation has a great significance; the proposed approaches in this research work guarantee the detection and reconstruction of unknown faults, which has a great significance for practical life. Originality/value In this research, two strategies such Lyapunov function theory and sliding-mode surface concept are used in combination for tuning the weight parameters of NNs adaptively. The main purpose of these strategies is the fault diagnosis and reconstruction with high accuracy in terms of shape as well as the magnitude of unknown faults. Results depict that the proposed strategy is more effective compared with techniques used in the literature.

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“…Artificial neural network has been widely used in aircraft modeling, control and fault diagnosis because of its adaptive and nonlinear characteristics (Jategaonkar, 2015; Taimoor and Aijun, 2020a, 2020b; Taimoor et al , 2021). The state variables and control inputs of the aircraft are mapped to the aerodynamic coefficients of the aircraft, but the mathematical model of the aerodynamic coefficients is not established (Jategaonkar, 2015).…”

Section: Introductionmentioning

confidence: 99%

Aerodynamic coefficients modeling using Levenberg–Marquardt algorithm and network

Wang

et al. 2021

AEAT

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Purpose The purpose of this paper is to propose a new aerodynamic parameter estimation methodology based on neural network and output error method, while the output error method is improved based on particle swarm algorithm. Design/methodology/approach Firstly, the algorithm approximates the dynamic characteristics of aircraft based on feedforward neural network. Neural network is trained by extreme learning machine, and the trained network can predict the aircraft response at (k + 1)th instant given the measured flight data at kth instant. Secondly, particle swarm optimization is used to enhance the convergence of Levenberg–Marquardt (LM) algorithm, and the improved LM method is used to substitute for the Gauss Newton algorithm in output error method. Finally, the trained neural network is combined with the improved output error method to estimate aerodynamic derivatives. Findings Neither depending on the initial guess of the parameters to be estimated nor requiring numerical integration of the aircraft motion equation, the proposed algorithm can be used for unstable aircraft and is successfully applied to extract aerodynamic derivatives from both simulated and real flight data. Research limitations/implications The proposed method requires iterative calculation and can only identify parameters offline. Practical implications The proposed method is successfully applied to estimate aircraft aerodynamic parameters and can also be used as a new algorithm for other optimization problems. Originality/value In this study, the output error method is improved to reduce the dependence on the initial value of parameters and expand its application scope. It is applied in aircraft aerodynamic parameter identification together with neural network.

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Adaptive strategy for fault detection, isolation and reconstruction of aircraft actuators and sensors

Cited by 11 publications

References 23 publications

Technology development and commercial applications of industrial fault diagnosis system: a review

Technology development and commercial applications of industrial fault diagnosis system: a review

Adaptive rapid neural observer-based sensors fault diagnosis and reconstruction of quadrotor unmanned aerial vehicle

Aerodynamic coefficients modeling using Levenberg–Marquardt algorithm and network

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