Remaining Useful Life Estimation of Rolling Bearing Based on SOA-SVM Algorithm

Abstract

Rolling bearings are an important part of rotating machinery, and are of great significance for fault diagnosis and life monitoring of rolling bearings. Analyzing fault signals, extracting effective degradation information and establishing corresponding models are the premise of residual life prediction of rolling bearings. In this paper, first, the time-domain features were extracted to form the eigenvector of the vibration signal, and then the index representing the bearing degradation was found. It was found that the time-domain index could effectively describe the degradation information of the bearing, and the multi-dimensional time-domain characteristic information could effectively describe the attenuation trend of the vibration signal of the rolling bearing. On this basis, appropriate feature vectors were selected to describe the degradation characteristics of bearings. Aiming at the problems of large amounts of data, large amounts of information redundancy and unclear performance index of multi-dimensional feature vectors, the dimensionality of multi-dimensional feature vectors was reduced with principal component analysis, thus, simplifying the multi-dimensional feature vectors and reducing the information redundancy. Finally, in view of the support vector machine (SVM)’s needs to determine kernel function parameters and penalty factors, the squirrel optimization algorithm (SOA) was used to adaptively select parameters and establish the state-life evaluation model of rolling bearings. In addition, mean absolute error (MAE) and root mean squared error (RMSE) were used to comprehensively evaluate SOA. The results showed that the SOA reduced the errors by 5.1% and 13.6%, respectively, compared with a genetic algorithm (GA). Compared with particle swarm optimization (PSO), the error of SOA was reduced by 7.6% and 15.9%, respectively. It showed that SOA-SVM effectively improved the adaptability and regression performance of SVM, thus, significantly improving the prediction accuracy.

Fine-grained transfer learning based on deep feature decomposition for rotating equipment fault diagnosis

“…Model-based diagnosis algorithms can be divided into deterministic fault diagnosis methods [8], stochastic fault diagnosis methods [9], fault diagnosis for discrete-events and hybrid systems [10], and fault knowledge of health system symptoms, including the wavelet transform [16], empirical mode decomposition [17], and Hilbert-Huang transform [18]. Machine learning methods analyze faults by manually extracting fault features and then using machine learning algorithms such as support vector machine algorithm [19], K-nearest neighbor algorithm [20], and Markov model [21]. Deep learning algorithms, on the other hand, make fault diagnosis algorithms more intelligent and less dependent on humans by automatically extracting deep features of the data and by their powerful fitting capabilities [22][23][24][25].…”

Digital Twin-Driven Remaining Useful Life Prediction for Rolling Element Bearing

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A Novel Multi–Scale One–Dimensional Convolutional Neural Network for Intelligent Fault Diagnosis of Centrifugal Pumps

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