Misalignment Fault Prediction of Wind Turbines Based on Improved Artificial Fish Swarm Algorithm

Hua, Zhe; Xiao, Yancai; Cao, Jiadong

doi:10.3390/e23060692

Cited by 16 publications

(5 citation statements)

References 31 publications

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“…At present, rolling bearing fault prediction models can be broadly divided into two categories, traditional fault prediction models and neural network-based fault prediction models. Traditional fault prediction models can establish useful mathematical models based on fault mechanisms, structural characteristics and state spaces to achieve fault prediction, such as least squares Support Vector Machine [1], high-order fuzzy time series [2], and Grey-Markov chain [3], etc. Although the above traditional fault prediction methods have achieved some results, it is difficult to establish mathematical expressions for rolling bearing fault prediction under complex operating conditions due to its problems of over-reliance on a priori knowledge, inefficient data processing, and poor data mining ability.…”

Section: Introductionmentioning

confidence: 99%

Fault prediction model for rolling bearings based on double adaptive sliding time windows

Zhang,

Guo,

Zhang

et al. 2024

Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023)

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To address the problems of traditional and neural network-based methods in rolling bearing fault prediction, we proposed a fault prediction model for rolling bearings based on double adaptive sliding time windows. Firstly, the rolling bearing vibration signal is mapped into fault features that can characterise its degradation state by setting up a state estimation non-linear operator that can remove correlations. Secondly, a loss function is used as a criterion to set up an adaptive update mechanism for the model parameters, as well as a sliding time window capable of adaptively selecting the data length. Finally, the validity of the proposed failure prediction model is verified by simulating the occurrence of failures under the combined sudden and gradual failures in practice using the whole life cycle data of rolling bearings released by Xi'an Jiaotong University.

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Section: Introductionmentioning

confidence: 99%

Fault prediction model for rolling bearings based on double adaptive sliding time windows

Zhang,

Guo,

Zhang

et al. 2024

Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023)

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“…The current real-time monitoring technology is mainly to monitor the objects near the transmission line in real time, but at present, it can only locate the moving objects, not detect them. In a word, the on-line monitoring by means of line patrol inspection has low monitoring efficiency and poor real-time performance [3]. At present, both sensors and image surveillance lack the ability of automatic identification of objects, resulting in high error detection.…”

Section: Introductionmentioning

confidence: 99%

Research on fault early warning of power transmission line crossing and collision based on multi-source data

Chang

Zheng

Shang

et al. 2023

Eighth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2022)

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The existing transmission line fault early warning can’t automatically identify the abnormal area, resulting in poor early warning effect for the line crossing and line touching fault. Based on this, an early-warning method of power transmission line crossing and collision fault based on multi-source data is proposed. Combined with the feature extraction method of multi-source data fusion, based on the analysis of the characteristics of the stock index of power grid transmission line crossing and touching, the leakage current acquisition model is constructed. Based on the model, the abnormal data of power grid are collected, and the characteristics of power grid transmission line crossing and touching are mined and identified. Then the texture features are obtained by using the local binary pattern operator to simplify the early warning steps of power grid transmission line crossing and touching. The experimental results show that the multi-source data-based power grid transmission line crossing and collision fault early warning is more practical than the traditional methods. In the process of practical application, the effect of abnormal data prediction and collection is significantly better. It can carry out fault early warning more quickly and accurately, and fully meet the research requirements.

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“…The swarm intelligence optimization algorithms are a kind of random optimization algorithm constructed by simulating natural laws or the inherent behavior and living habits of creatures such as foraging and hunting. The swarm intelligence algorithms such as Particle Swarm Optimization(PSO) [1][2], Grey Wolf Optimization (GWO) [3], Ant Colony Optimization (ACO) [4][5][6], Artificial Fish Swarm Algorithm (AFSA) [7][8][9] etc. have been applied in the fields of engineering science, computing science and so on.…”

Section: Introductionmentioning

confidence: 99%

An Improved Chicken Swarm Optimization Algorithm Based on Adaptive Mutation Learning Strategy

Zhou¹,

Zhou²,

Gao³

2022

Journal of Compurters

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<p>To solve the problem that the Chicken swarm optimization (CSO) has low solution accuracy and tends to fall into the local optimum on later stages of iteration, an adaptive mutation learning Chicken swarm optimization (AMLCSO) is proposed in this paper. Firstly, to solve the problem of uneven initial distribution and improve the algorithm’s stability, a good-point set is introduced. Secondly, according to the difference between the current individual position and the optimal individual position, the nonlinear adaptive adjustment of weight is realized and the position update step is dynamically adjusted. This strategy improves the algorithm’s convergence. Thirdly, the learning update strategies of Gaussian mutation and normal distribution are introduced to improve the probability of selection and solving accuracy and avoid falling into the local optimum. Finally, the AMLCSO is compared with other standard algorithms and improved Chicken swarm optimization algorithms on twenty benchmark test functions. The experimental results show the AMLCSO has faster convergence and higher solution accuracy.</p> <p> </p>

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Misalignment Fault Prediction of Wind Turbines Based on Improved Artificial Fish Swarm Algorithm

Cited by 16 publications

References 31 publications

Fault prediction model for rolling bearings based on double adaptive sliding time windows

Fault prediction model for rolling bearings based on double adaptive sliding time windows

Research on fault early warning of power transmission line crossing and collision based on multi-source data

An Improved Chicken Swarm Optimization Algorithm Based on Adaptive Mutation Learning Strategy

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