A New Support Vector Regression Model for Equipment Health Diagnosis with Small Sample Data Missing and Its Application

Abstract

Actually, it is difficult to obtain a large number of sample data due to equipment failure, and small sample data may also be missing. This paper proposes a novel small sample data missing filling method based on support vector regression (SVR) and genetic algorithm (GA) to improve equipment health diagnosis effect. First, the genetic algorithm is used to optimize support vector regression, and a new method GA-SVR can be proposed. The GA-SVR model is trained by using other data of the variable to which the missing data belongs, and the single-variable prediction method can be obtained. The correlation analysis is used to reconstruct the training set, and the GA-SVR is trained by using the data of the variables related to the missing data to obtain the multivariate prediction method. Then, the dynamic weight is presented to combine the single-variable prediction method with the multiple-variable prediction method based on certain principles, and the missing data are filled with the combined prediction methods. The filled data are used as input of GA-SVM to diagnose equipment failure. Finally, a case study is given to verify the applicability and effectiveness of the proposed method.

Permeability Prediction Using Machine Learning Methods for the CO2 Injectivity of the Precipice Sandstone in Surat Basin, Australia

“…Despite its low training performances, SVR shows good generalizability, maintaining similar performances in validation and testing. As discussed, the penalty factor, the insensitive loss function, and the radial basis function are the major parameters controlling the performance of SVR [24]. Thus, higher training performance may be achieved without jeopardizing the generalizability by improving the search space for these parameters [24].…”

“…Support Vector Regressor (SVR): SVR is the regression version of the support vector machine (SVM) often used in classification problems. It approximates a given data to a continuous function through a non-linear transformation that maps the data to a high-dimensional space [24]. It solves a convex optimization problem that minimizes an ε-insensitive loss function [24].…”

“…It approximates a given data to a continuous function through a non-linear transformation that maps the data to a high-dimensional space [24]. It solves a convex optimization problem that minimizes an ε-insensitive loss function [24]. The model performance and complexity are controlled by hyper-parameters such as the kernel function used and its associated parameters, and the regularization factor (C) and the most commonly used are radial basis function (RBF) or gamma function, sigmoid, linear, or non-linear such as polynomial [19].…”

A Novel Framework for Anomaly Detection for Satellite Momentum Wheel Based on Optimized SVM and Huffman-Multi-Scale Entropy

“…In the field of fault diagnosis, research on the SVM method mainly focuses on two aspects of obtaining more accurate recognition accuracy, i.e., by optimizing the hyperparameters of the model and constructing a new kernel function. For specific recognition tasks, to optimize the hyperparameters of the model to obtain better recognition performance, many optimization methods are applied [24][25][26]. Liu et al proposed a novel small sample data missing filling method based on support vector regression (SVR) and genetic algorithm (GA) to improve the equipment health diagnosis effect [25].…”

“…For specific recognition tasks, to optimize the hyperparameters of the model to obtain better recognition performance, many optimization methods are applied [24][25][26]. Liu et al proposed a novel small sample data missing filling method based on support vector regression (SVR) and genetic algorithm (GA) to improve the equipment health diagnosis effect [25]. Particle swarm optimization (PSO) is a hyperparameter optimization algorithm which is used by Cuong-Le et al for damage identifications [26].…”