Health Prognosis for Equipment Based on ACO-K-Means and MCS-SVM under Small Sample Noise Unbalanced Data

Abstract

For the problem of manufacturing system residual life prognosis with insufficient small sample data and unbalanced distribution, this paper proposes a model for equipment health status analysis and life prognosis based on improved ant colony optimization K-Means (ACO-K-Means) and multi-classification Self-Adding SVM (MCS-SVM). First, based on the fuzzy data set, the data is classified for the first time according to the traditional SVM, and the initial classification results are obtained. Second, the improved K-Means algorithm based on the ant colony algorithm is used to cluster the data set after the initial classification, to obtain more health status labels in different states.The noise scale coefficient is established, and the data set distribution is optimized by introducing the unbalanced scale standard and the adaptive addition rule, to enrich the sample capacity of the scarce label under the influence of noise. On this basis, the SVMset is introduced according to the number of clusters to achieve multi-classification of the data set. Finally, by using the state data of the hydraulic pump of Caterpillar, the simulation results show that the two improved algorithms can accurately analyze the health state and lifetime prognosis of equipment under small noise samples and unbalanced data.

Keywords

• health prognosis, machine learning algorithm Noise data, state recognition, unbalanced data