Development of Fault Diagnosis Algorithm using Correlation Analysis and ELM

Lim, Jung‐Hyurk; Ji, Pyeong-Shik

doi:10.5370/kieep.2016.65.3.204

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…Some studies have also diagnosed failures using machine-learning methods [9,10]. Extreme learning machine and support vector machine (SVM) classification were used as failure diagnosis methods.…”

Section: Introductionmentioning

confidence: 99%

Failure Diagnosis Method of Photovoltaic Generator Using Support Vector Machine

Cho

Kim

et al. 2020

J. Electr. Eng. Technol.

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The capacity of photovoltaic (PV) generators can increase owing to the 4030 policy of the Government of South Korea.. In addition, there has been significant interest in developing a technology for the maintenance of PV generators owing to an increase in the number of outdated PV generators. This paper describes a failure diagnosis method that uses operational data for power generation and solar radiation of PV generators. The measured data stored since four years in an operational 50-kW PV generator that was installed in 2014, were analyzed. The proposed failure diagnosis logic uses support vector machine classification as a failure diagnosis method that can classify normal and failure data. The failure data were processed to be used as the fault diagnosis logic for solar power generators. A new 50-kW PV generator, which contained no fault data, was used for a case study in this paper. Fault data were generated and the operation data of the PV generators were diagnosed by applying the proposed method. In addition, the accuracy was calculated and the results were analyzed.

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Section: Introductionmentioning

confidence: 99%

Failure Diagnosis Method of Photovoltaic Generator Using Support Vector Machine

Cho

Kim

et al. 2020

J. Electr. Eng. Technol.

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“…Therefore, ELM is widely used in the field of fault diagnosis. Lim and Ji [ 5 ] apply ELM to fault diagnosis of PV systems. In [ 6 ], a new fault diagnosis model for rotating machinery based on residual network (Reset) and ELM is established.…”

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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