Multivariable least squares support vector machine with time integral operator for the prediction of bearing performance degradation

The prediction of performance degradation is significant for the health monitoring of rolling bearing, which helps to greatly reduce the loss caused by potential faults in the entire life cycle of rotating machinery. As a new method of machine learning based on statistical learning theory, least squares support vector machine is developed and has achieved good results. However, it lacks the description of the time-sum effect and delay characteristics, which cannot fully describe the performance degradation process. To overcome the problem, a new time shift least squares support vector machine with integral operator is proposed. What is more, multivariable prediction model is introduced to describe the process from multiple perspectives. In this model, different features are extracted to construct sample pairs through a moving window. Then these features are decomposed in time domain using a set of orthogonal basis functions to simplify computation. Furthermore, the model adaptability is also improved through an iterative updating strategy. Bearing fault experiments show that the proposed model outperforms the general method.