Proposal and experimental case study on building ventilating fan fault diagnosis based on cuckoo search algorithm optimized extreme learning machine

Xu, Yingjie; Chen, Ning; Shen, Xi; Xu, Lu; Pan, Zhongyu; Pan, Fan

doi:10.1016/j.seta.2020.100975

Cited by 15 publications

(11 citation statements)

References 35 publications

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“…With the development of scientific and computational techniques (Zhang et al, 2012), machine learning models, particularly artificial neural networks (ANNs) (Liu et al, 2020), have been widely used in many fields, such as time series prediction (Liu et al, 2021;, poverty assessment (Guan et al, 2019;Yin & Zhang, 2020), and fault diagnosis (Chen et al, 2021;Xu et al, 2021). They have also been proved to be an effective alternative to the conventional model (Lin et al, 2020), due to their obvious advantages in dealing with complex nonlinear relationships among air pollutant concentrations (Yang et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid fine particulate matter (PM_2.5) forecasting and its further application system: Case studies in China

Wang

Yang

et al. 2021

Journal of Forecasting

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Section: Introductionmentioning

confidence: 99%

A novel hybrid fine particulate matter (PM_2.5) forecasting and its further application system: Case studies in China

Wang

Yang

et al. 2021

Journal of Forecasting

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“…( 6) We calculate the fitness value of the individual after updating the position and determine whether the termination condition is satisfied or the maximum number of iterations is reached; if it is satisfied, the optimal weight and bias are output, if not, we iterate through the loop until the termination condition is met or the maximum number of iterations is reached. (7) e ISSA-ELM model is constructed using the obtained optimal weights and biases. (8) We evaluate the performance of the ISSA-ELM model.…”

Section: Issa-elm Modelingmentioning

confidence: 99%

“…In [ 6 ], a new fault diagnosis model for rotating machinery based on residual network (Reset) and ELM is established. In [ 7 ], the cuckoo search algorithm (CSA) is introduced to optimize the ELM to achieve the goal of fault diagnosis for fans. In [ 8 ], finite element method (FEM) simulation and ELM are combined to detect gear faults.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Balancing Machine Based on ISSA-ELM

Liu

Wang

et al. 2022

Computational Intelligence and Neuroscience

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Balancing machine is a general equipment for dynamic balance verification of rotating parts, whether it breaks down or does not determine the accuracy of dynamic balance verification. In order to solve the problem of insufficient fault diagnosis accuracy of balancing machine, a fault diagnosis method of balancing machine based on the Improved Sparrow Search Algorithm (ISSA) optimized Extreme Learning Machine (ELM) was proposed. Firstly, iterative chaos mapping and Fuch chaos mapping were introduced to initialize the population and increase the population diversity. Secondly, the adaptive dynamic factor and Levy flight strategy were also introduced to update the individual positions and improve the model convergence speed. Finally, the fault feature vector was input to the ISSA-ELM model with the fault type as the output. The experiment showed that the fault diagnosis accuracy of ISSA-ELM is as high as 99.17%, which is 1.67%, 2.50%, 7.50%, and 17.50% higher than that of SSA-ELM, HHO-ELM, PSO-ELM, and ELM, respectively, further improving the prediction accuracy of the operation state of the balancing machine.

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“…Various mathematical programming techniques such as the dynamic programming, linear/nonlinear programming, semidefinite programming, and benders decomposition [21][22][23][24] have found numerous applications to solve several complicated problems. Most of these techniques provide poor results against stochastic and nonconvex optimization problems with a strong tendency to trap in the local optima [25][26][27]. Moreover, their application is limited to the offline implementation, in contrast to the online optimizers which are more suitable for mobile applications [28,29].…”

Section: Introductionmentioning

confidence: 99%

Efficient control and multi‐criteria energy scheduling of renewable‐based utility grid via pareto‐metaheuristic optimizers

Ramadan

Iqbal

Becherif

et al. 2022

IET Renewable Power Gen

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Being a renewable influenced utility grid, the wind integrated hydrothermal system (WHTS) is considered as a future-proof and contemporary energy system to reduce the dependency on depleting fossil fuels. However, due to intermittent nature of wind energy and complex operational constraints, offering the day-ahead operation schedule of WHTS with joint awareness of fuel economy and emissions is classified as a complicated multiobjective optimization problem. The major challenge is to maximize the utilization of clean hydro power to accommodate uncertain wind energy together with reduced dependency on fossils based thermal power. In this context, the paper devises an efficient control and multi-criteria energy scheduling method, essentially comprised of adaptive repair and volume restriction to maximize the utilization of available hydro power. The paretometaheuristic optimizer based on non-dominated sorting is exploited for the day-ahead scheduling of WHTS based utility grid. The most appropriate solution from pareto-optimal archive is selected using fuzzy-ranking index. The expected reductions in fuel cost and emissions are reflected using a significant probability scheme. The effectiveness of the proposed approach based on pareto-metaheuristic is appraised by various case studies. The simulation results provide a suitable day-ahead generation schedule of WHTS based grid, considering both economics and environmental aspects.

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Proposal and experimental case study on building ventilating fan fault diagnosis based on cuckoo search algorithm optimized extreme learning machine

Cited by 15 publications

References 35 publications

A novel hybrid fine particulate matter (PM_2.5) forecasting and its further application system: Case studies in China

A novel hybrid fine particulate matter (PM_2.5) forecasting and its further application system: Case studies in China

Fault Diagnosis of Balancing Machine Based on ISSA-ELM

Efficient control and multi‐criteria energy scheduling of renewable‐based utility grid via pareto‐metaheuristic optimizers

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Proposal and experimental case study on building ventilating fan fault diagnosis based on cuckoo search algorithm optimized extreme learning machine

Cited by 15 publications

References 35 publications

A novel hybrid fine particulate matter (PM2.5) forecasting and its further application system: Case studies in China

A novel hybrid fine particulate matter (PM2.5) forecasting and its further application system: Case studies in China

Fault Diagnosis of Balancing Machine Based on ISSA-ELM

Efficient control and multi‐criteria energy scheduling of renewable‐based utility grid via pareto‐metaheuristic optimizers

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Product

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A novel hybrid fine particulate matter (PM_2.5) forecasting and its further application system: Case studies in China

A novel hybrid fine particulate matter (PM_2.5) forecasting and its further application system: Case studies in China