基于粒子群算法的空气增压压缩机(ABC)电机故障预测维护

N. Rosli, Nurul Rawaida ain Burhani, R. Ibrahim
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引用次数: 5

摘要

在工业4.0时代,预测性维护变得至关重要,因为它将对工业经济产生重大影响。因此,准确的预测性维护对有效处理大型工厂故障的要求越来越高。本文利用人工神经网络(ANN)建立了空气增压压缩机(ABC)电机故障的预测维修模型。然而,网络的最优权值是影响人工神经网络准确性的参数之一。为此,提出了粒子群算法(PSO)来训练神经网络的权值和偏置。将本文的结果与基于均方误差(MSE)和均方根误差(RMSE)的传统人工神经网络进行了比较。
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Predictive Maintenance of Air Booster Compressor (ABC) Motor Failure using Artificial Neural Network trained by Particle Swarm Optimization
Predictive maintenance becomes crucial nowadays in industry 4.0 since it will have a high impact on the industrial economy. Therefore, accurate predictive maintenance growing high demand for handling the failure of big plants effectively. In this paper, the model of predictive maintenance for Air Booster Compressor (ABC) Motor failure is using Artificial Neural Network (ANN) is presented. However, the optimal weights of the network are one of the parameters that lead to the accuracy of ANN. Therefore, Particle Swarm Optimization (PSO) is proposed to train the weights and bias of ANN. The result presented in this paper is compared with conventional ANN based on Mean Square Error (MSE) and Root Mean Square Error (RMSE)
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