Fuzzy-based metaheuristic algorithm for optimization of fuzzy controller: fault-tolerant control application

Patel, Himanshukumar R.

doi:10.1108/ijicc-09-2021-0204

Cited by 24 publications

(9 citation statements)

References 42 publications

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“…Han et al proposed a model that integrates convolutional neural network and gated recurrent unit (CNN-GRU) to classify faults, and the experimental results show that the model can classify faults visually with higher accuracy (Han et al, 2022). In addition to these, there are research papers (Levent et al, 2019;Ma et al, 2019;Patel, 2022;Latha and Rooban, 2023). Although their methods are effective in diagnosing mechanical failures, they do not take into account the real-time requirements of real factories.…”

Section: Limitation Of Prior Workmentioning

confidence: 99%

Research on the mechanical fault diagnosis method based on sound signal and IEMD-DDCNN

Wen

Sun

et al. 2023

IJICC

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PurposeThe purpose of this paper is to provide a shorter time cost, high-accuracy fault diagnosis method for water pumps. Water pumps are widely used in industrial equipment and their fault diagnosis is gaining increasing attention. Considering the time-consuming empirical mode decomposition (EMD) method and the more efficient classification provided by the convolutional neural network (CNN) method, a novel classification method based on incomplete empirical mode decomposition (IEMD) and dual-input dual-channel convolutional neural network (DDCNN) composite data is proposed and applied to the fault diagnosis of water pumps.Design/methodology/approachThis paper proposes a data preprocessing method using IEMD combined with mel-frequency cepstrum coefficient (MFCC) and a neural network model of DDCNN. First, the sound signal is decomposed by IEMD to get numerous intrinsic mode functions (IMFs) and a residual (RES). Several IMFs and one RES are then extracted by MFCC features. Ultimately, the obtained features are split into two channels (IMFs one channel; RES one channel) and input into DDCNN.FindingsThe Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection (MIMII dataset) is used to verify the practicability of the method. Experimental results show that decomposition into an IMF is optimal when taking into account the real-time and accuracy of the diagnosis. Compared with EMD, 51.52% of data preprocessing time, 67.25% of network training time and 63.7% of test time are saved and also improve accuracy.Research limitations/implicationsThis method can achieve higher accuracy in fault diagnosis with a shorter time cost. Therefore, the fault diagnosis of equipment based on the sound signal in the factory has certain feasibility and research importance.Originality/valueThis method provides a feasible method for mechanical fault diagnosis based on sound signals in industrial applications.

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Section: Limitation Of Prior Workmentioning

confidence: 99%

Research on the mechanical fault diagnosis method based on sound signal and IEMD-DDCNN

Wen

Sun

et al. 2023

IJICC

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“…ANFIS is defined as a soft computing technique that combines FLC and ANN algorithms. The FLC-type with the harmonic search metaheuristic method can provide the best approach for generating optimal vector values in optimizing the fuzzy type-1 and interval type-2 controller membership function in non-linear problems (Patel, 2022). For the system under uncertainty conditions, the fuzzy type-1 and type-2 intervals are implemented to improve various algorithms in control fields (Patel and Shah, 2022a-f).…”

Section: Introductionmentioning

confidence: 99%

Improvement of Rooftop Solar Panels Efficiency using Maximum Power Point Tracking Based on an Adaptive Neural Network Fuzzy Inference System

Nrartha¹,

Ginarsa²,

Muljono³

et al. 2023

American Journal of Engineering and Applied Sciences

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Rooftop solar panels are a strategy for achieving Indonesia's renewable energy goals, but their non-linear characteristics make them difficult to control, especially in the face of extreme weather changes. An effective controller is needed to optimize the power output of solar panels. This study proposes a Maximum Power Point Tracking (MPPT) controller based on an Adaptive Neural network Fuzzy Inference System (ANFIS) to address this control problem. The capacity of the rooftop solar panels is 3,430-Watt peak (Wp) and they are connected to a 220-Volt (V) grid system. The system is designed, simulated, and analyzed using the Simulink model. The proposed ANFIS MPPT control for rooftop solar panels is compared to Perturb and Observe (P&O) MPPT and no MPPT systems. The simulation results show that in rapid changes in irradiation and extreme temperature, the efficiency of MPPT based on ANFIS is better than P&O MPPT and no MPPT by 0.4523 and 0.1115%, respectively.

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“…Fault tolerant control is an active research area in recent times, attributable to gives acceptable control performance under the exogenous uncertainty like actuator, system component and sensor faults or process disturbances. And so this work is done for optimization of fuzzy fault tolerant controller (Patel andShah, 2018, 2019a, b).…”

Section: Introductionmentioning

confidence: 99%

“…There are some papers on fuzzy controller optimization that use other metaheuristics. In Patel (2021), for example, the cuckoo search algorithm (CSA) and FPA are used to evolve the architecture of a type-1 and type-1 fuzzy controller in real-world control problems under perturbations. In Patel and Shah (2022b), proposed dynamic parameter adaption using shadowed fuzzy sets for two metaheuristic algorithms CSA and FPA, respectively for nonlinear control system.…”

Section: Introductionmentioning

confidence: 99%

“…The FPA algorithm is used for optimization engineering and non-engineering problem (Priyadarshi et al , 2018; Templos-Santos and Aguilar-Mejia, 2019; Patel et al , 2020a; Zhao et al , 2019; Sun et al , 2018; Jain et al , 2018), however, the use of the metaheuristic algorithm in fault-tolerant control (FTC) applications is novel and proposed in the article, in addition, the metaheuristic FPA is used to design adaptive parameterization of type-1 fuzzy controller membership functions (MFs) for process control benchmark control problem with uncertainties. In recent (Priyadarshi et al , 2018; Templos-Santos and Aguilar-Mejia, 2019; Patel et al , 2020a; Lagunes et al ., 2018a, b; Bernal et al , 2019; Patel and Shah, 2022a, b; Patel, 2021), various metaheuristic algorithms are used to optimize fuzzy controllers for different engineering applications but due to advantages of the FPA (i.e. fewer parameters for tuning, random flight based algorithm, etc.)…”

Section: Introductionmentioning

confidence: 99%

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Type-2 fuzzy logic applications designed for active parameter adaptation in metaheuristic algorithm for fuzzy fault-tolerant controller

Patel

Shah

2022

IJICC

Self Cite

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PurposeIn recent times, fuzzy logic is gaining more and more attention, and this is because of the capability of understanding the functioning of the system as per human knowledge-based system. The main contribution of the work is dynamically adapting the important parameters throughout the execution of the flower pollination algorithm (FPA) using concepts of fuzzy logic. By adapting the main parameters of the metaheuristics, the performance and accuracy of the metaheuristic have been improving in a varied range of applications.Design/methodology/approachThe fuzzy logic-based parameter adaptation in the FPA is proposed. In addition, type-2 fuzzy logic is used to design fuzzy inference system for dynamic parameter adaptation in metaheuristics, which can help in eliminating uncertainty and hence offers an attractive improvement in dynamic parameter adaption in metaheuristic method, and, in reality, the effectiveness of the interval type-2 fuzzy inference system (IT2 FIS) has shown to provide improved results as matched to type-1 fuzzy inference system (T1 FIS) in some latest work.FindingsOne case study is considered for testing the proposed approach in a fault tolerant control problem without faults and with partial loss of effectiveness of main actuator fault with abrupt and incipient nature. For comparison between the type-1 fuzzy FPA and interval type-2 fuzzy FPA is presented using statistical analysis which validates the advantages of the interval type-2 fuzzy FPA. The statistical Z-test is presented for comparison of efficiency between two fuzzy variants of the FPA optimization method.Originality/valueThe main contribution of the work is a dynamical adaptation of the important parameters throughout the execution of the flower pollination optimization algorithm using concepts of type-2 fuzzy logic. By adapting the main parameters of the metaheuristics, the performance and accuracy of the metaheuristic have been improving in a varied range of applications.

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Fuzzy-based metaheuristic algorithm for optimization of fuzzy controller: fault-tolerant control application

Cited by 24 publications

References 42 publications

Research on the mechanical fault diagnosis method based on sound signal and IEMD-DDCNN

Research on the mechanical fault diagnosis method based on sound signal and IEMD-DDCNN

Improvement of Rooftop Solar Panels Efficiency using Maximum Power Point Tracking Based on an Adaptive Neural Network Fuzzy Inference System

Type-2 fuzzy logic applications designed for active parameter adaptation in metaheuristic algorithm for fuzzy fault-tolerant controller

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