Adaptive fuzzy wavelet network for robust fault detection and diagnosis in non‐linear systems

Shahriari-kahkeshi, Maryam; Sheikholeslam, Farid

doi:10.1049/iet-cta.2013.0960

Cited by 16 publications

(10 citation statements)

References 25 publications

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“…Besides, it provided a new integrated solution to concurrently provide component fault detection and isolation. In [ 15 ], the safe operation of hydraulic generator units (HGUs) was the most important feature of the proposed fault diagnosis. In the algorithm, the macroscopic Euler number (ME), fuzzy convex-concave feature (FCC) and boundary-layer feature (BL) were proposed for three different aspects: boundary, structure and region.…”

Section: Related Workmentioning

confidence: 99%

AF-DHNN: Fuzzy Clustering and Inference-Based Node Fault Diagnosis Method for Fire Detection

Jin

Zhang

Wang

2015

Sensors

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Wireless Sensor Networks (WSNs) have been utilized for node fault diagnosis in the fire detection field since the 1990s. However, the traditional methods have some problems, including complicated system structures, intensive computation needs, unsteady data detection and local minimum values. In this paper, a new diagnosis mechanism for WSN nodes is proposed, which is based on fuzzy theory and an Adaptive Fuzzy Discrete Hopfield Neural Network (AF-DHNN). First, the original status of each sensor over time is obtained with two features. One is the root mean square of the filtered signal (FRMS), the other is the normalized summation of the positive amplitudes of the difference spectrum between the measured signal and the healthy one (NSDS). Secondly, distributed fuzzy inference is introduced. The evident abnormal nodes’ status is pre-alarmed to save time. Thirdly, according to the dimensions of the diagnostic data, an adaptive diagnostic status system is established with a Fuzzy C-Means Algorithm (FCMA) and Sorting and Classification Algorithm to reducing the complexity of the fault determination. Fourthly, a Discrete Hopfield Neural Network (DHNN) with iterations is improved with the optimization of the sensors’ detected status information and standard diagnostic levels, with which the associative memory is achieved, and the search efficiency is improved. The experimental results show that the AF-DHNN method can diagnose abnormal WSN node faults promptly and effectively, which improves the WSN reliability.

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Section: Related Workmentioning

confidence: 99%

AF-DHNN: Fuzzy Clustering and Inference-Based Node Fault Diagnosis Method for Fire Detection

Jin

Zhang

Wang

2015

Sensors

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“…Moreover, the wavelet transform has been widely used for analyzing the complicated time-varying signals due to its varied window function for the time domain [15], [16]. Recently, the wavelet functions have been proposed to integrate into FNN to construct the wavelet fuzzy neural network (WFNN) for enhancing the adaptive and learning ability in complex engineering issues [17], [18]. Furthermore, owing to the specific structure of a recurrent network with the internal feedback loop to capture system dynamics, the recurrent fuzzy neural network (RFNN) has better dynamic ability than the feed-forward form [19]- [21].…”

Section: Introductionmentioning

confidence: 99%

Improved LVRT Performance of PV Power Plant Using Recurrent Wavelet Fuzzy Neural Network Control for Weak Grid Conditions

et al. 2020

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An intelligent control method using recurrent wavelet fuzzy neural network (RWFNN) is proposed to improve the low-voltage ride through (LVRT) performance of a two-stage photovoltaic (PV) power plant under grid faults for the weak grid conditions. The PV power plant comprises an interleaved DC/DC converter and a three-level neutral-point clamped (NPC) smart inverter, in which the output active and reactive powers of the inverter can be predetermined in accordance with grid codes of the utilities. Moreover, for the purpose of improving the control performance of the PV power plant to handle the grid faults for the weak grid conditions, a new RWFNN with online training is proposed to replace the traditional proportional-integral (PI) controller for the active and reactive powers control of the smart inverter. Furthermore, the proposed controllers are implemented by two floating-point digital signal processors (DSPs). From the simulation and experimental results, excellent control performance for the tracking of active and reactive powers under grid faults for the weak grid conditions can be achieved by using the proposed intelligent control method. INDEX TERMS PV power plant, interleaved DC/DC converter, short-circuit ratio, weak grid, wavelet fuzzy neural network, three-level neutral-point clamped inverter.

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“…In some studies, the gradient technique 30,31 is applied to tune and update the parameters of FWNN structure such as the dilation and translation of wavelet functions and weights of networks which culminating in achieving good proficiency and higher accuracy than WNNs. Recently, an adaptive FWNN structure 32‐36 has been proposed and its parameters updated based on adaptive learning algorithms stemming from Lyapunov theory. As a result, the number of iterations for training FWNN structure becomes smaller and better precision in function approximation is attained rather than NNs 37‐40 .…”

Section: Introductionmentioning

confidence: 99%

Observer‐based controller design for uncertain disturbed Takagi‐Sugeno fuzzy systems: A fuzzy wavelet neural network approach

Ebrahimi

Asemani

Safavi

2020

Adaptive Control & Signal

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SummaryIn this article, we develop a novel method to design a controller for nonlinear systems represented by Takagi‐Sugeno (T‐S) fuzzy model in the presence of unknown dynamics, uncertainties in parameters of nonlinear system and external disturbances. The control law is constituted two segments. The first segment derives from parallel distributed compensation (PDC) procedure, in which each control rule is drawn from the respective rule of T‐S fuzzy model. The second segment stems from fuzzy wavelet neural network (FWNN) estimator which is evoked by the hypothesis of multiresolution analysis (MRA) of wavelet transforms and fuzzy notions, so as to approximate the uncertainties and external disturbances in T‐S fuzzy model accurately. In this regard, the Lyapunov stability theorem is applied to acquire the adaptive learning laws for training and tuning online FWNN parameters such as the dilations, translations, and the weights of networks. Moreover, the asymptotic stability of the closed loop system is guaranteed based on the Lyapunov stability theorem. Furthermore, a development of the proposed method to observer‐based controller design for uncertain nonlinear systems descripted by T‐S fuzzy model is provided. The efficiency and robustness of the proposed method are illustrated by simulation outcomes. It is noteworthy that the proposed controller remarkably handles the uncertainties and external disturbances in T‐S fuzzy model without employing traditional conservative lemma and without considering bounds on uncertainties.

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Adaptive fuzzy wavelet network for robust fault detection and diagnosis in non‐linear systems

Cited by 16 publications

References 25 publications

AF-DHNN: Fuzzy Clustering and Inference-Based Node Fault Diagnosis Method for Fire Detection

AF-DHNN: Fuzzy Clustering and Inference-Based Node Fault Diagnosis Method for Fire Detection

Improved LVRT Performance of PV Power Plant Using Recurrent Wavelet Fuzzy Neural Network Control for Weak Grid Conditions

Observer‐based controller design for uncertain disturbed Takagi‐Sugeno fuzzy systems: A fuzzy wavelet neural network approach

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