A novel performance trend prediction approach using ENBLS with GWO

Abstract

Bearing is the core component of rotating machinery, which directly affects its reliability and operational efficiency. The effective evaluation of its operation state is the key to ensure the safe operation of the equipment. In this paper, a novel prediction method of bearing performance trend based on elastic net broad learning system (ENBLS) and the grey wolf optimization (GWO) algorithm is proposed. The proposed method combines the advantages of ENBLS and GWO algorithm to achieve better prediction results. In order to solve the problems that traditional regression prediction algorithms may lead to unsatisfactory prediction results and long training time, we propose a performance trend prediction method based on ENBLS. To further improve the prediction accuracy, we utilize GWO algorithm to optimize various parameters present in the model to improve the performance of the model. The bearing data of whole life cycle from the 2012 IEEE PHM challenge are selected to verify the effectiveness of the proposed method. The results show that the proposed method has high prediction accuracy and stability.

Research on the health status evaluation method of rolling bearing based on EMD‐GA‐BP

“…BP neural network (Back Propagation), proposed by a team of scientists headed by Rumelhart and McCelland in 1986, is a multi-layer feedforward network trained by the error back propagation algorithm, and is one of the most widely used neural network models. 6 The weights between the input layer, the hidden layer, and the output layer in the network, as well as the initial values of the thresholds of the hidden layer and the output layer, have obvious effects on the training effect and classification accuracy of the network. The genetic algorithm and other optimization theories can optimize the parameters of the multi-parameter model.…”

“…Wu et al 5 proposed an evolutionary algorithm, which can effectively solve complex optimization problems, and on the basis of the algorithm, proposed a difference vector mutation strategy to enhance the search ability and descent ability. Zhao et al 6 proposed a new method for predicting bearing degradation based on grey wolf optimization algorithm and elastic network extensive learning system to obtain better prediction results. The results show that the proposed method has high prediction accuracy and stability.…”

Intelligent prediction of fatigue life of natural rubber considering strain ratio effect

“…In our own experiment, a large number of alternative values are tested, and some classical values are selected from literatures. [28][29][30][31][32][33][34][35][36] These parameter values are experimentally modified until the most reasonable parameter values are determined. The parameters settings of different optimization algorithms are listed in Table 1.…”

“…With the advancement and popularization of machine learning (ML) algorithms, artificial intelligence methods are recently used in several studies to develop models for forecasting performances of materials and components including fatigue characteristics, [21][22][23][24] battery state of charge, 25 fault diagnosis, 26 karst tunnel water inrush, 27 airport taxiway planning and gate allocation, 28,29 and bearing performance. 30 Among them, the support vector regression (SVR) algorithm, which is based on support vector machines (SVM), has encouraging learning performance in solving the regression problem with small samples. [25][26][27] The present study focuses on the intelligent prediction method for rubber fatigue life.…”

Safety Helmet Detection Based on YOLOv5 Driven by Super-Resolution Reconstruction

“…Intelligence algorithms are a method to enhance automation. They are widely used in image processing, prediction, robotics and so on [ 6 , 7 , 8 , 9 , 10 , 11 ]. Currently, evolutionary algorithms and deep learning are two important intelligent systems [ 12 , 13 , 14 ].…”