Application of incremental support vector regression based on optimal training subset and improved particle swarm optimization algorithm in real-time sensor fault diagnosis

Attracted by the advantages of support vector regression and incremental learning approach, it is proposed in this work that an incremental support vector regression (ISVR) model optimized by particle swarm optimization (PSO) algorithm, and some improvements are made to be more suitable for sensor faults on-line diagnosis. To reducethe training time of ISVR model, an optimal training subset (OTS) method is adopted to reduce the size of training data set of the model. Then, in order to solve the problem of slow convergence of standard PSO algorithm, an incremental PSO (IPSO) algorithm is proposed to accelerate the model convergence through adjusting the inertial weight of each particle, which is gained by comparing the current position of each particle and the optimal position of the last incremental training. Based on the above improvements, a hybrid model, IPSO-OTS-ISVR model is presented finally. Experimental results based on actual operational data of a gas turbine shows that, under the premise of ensuring accuracy, the proposed IPSO-OTS-ISVR has much better performance in model response time and convergence performance over the comparison models. The experimental results based on an UCI data set indicate that the proposed hybrid model can also be extended to solve other prediction problems.

Automated summary

An incremental support vector regression model optimized by particle swarm optimization algorithm is proposed for sensor faults online diagnosis. Experimental results show that the model has superior performance in model response time and convergence performance.