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.

受到支持向量回归和增量学习方法的优点的吸引，本文提出了一种通过粒子群优化（PSO）算法优化的增量支持向量回归（ISVR）模型，并进行了一些改进以更适合传感器故障在线诊断。为了减少ISVR模型的训练时间，采用最优训练子集（OTS）方法来减少模型训练数据集的大小。然后，为解决标准PSO算法收敛速度慢的问题，提出了一种增量PSO算法，通过调整每个粒子的惯性权重来加快模型收敛速度。以及最近一次增量训练的最佳位置。基于以上改进，最后提出了一种混合模型，即IPSO-OTS-ISVR模型。基于燃气轮机实际运行数据的实验结果表明，在保证精度的前提下，提出的IPSO-OTS-ISVR在模型响应时间和收敛性能方面均优于比较模型。基于UCI数据集的实验结果表明，提出的混合模型也可以扩展以解决其他预测问题。