关键词: 增量稀疏核极限学习机, 样本稀疏, 瞬时信息测量, 稀疏核函数字典, 减样学习, 在线诊断

Abstract: In order to realize online fault diagnosis of diesel engine, a fast online diagnosis method based on incremental sparse kernel extreme learning machine (ISKELM) was proposed. Aiming at the problem of sample sparseness and model expansion in kernel online learning process, a sparse kernel function matrix construction strategy based on instantaneous information measurement was proposed. In the strategy, sample forward sparseness and backward deletion are implemented according to the principle of minimizing dictionary redundancy and maximizing the self information of dictionary elements. Then the dictionary is expanded and pruned online under the best order. So a diagnosis model with limited order and sparse structure is established. In order to solve the updating problem of the model kernel weight matrix, a method consisting of sample addition learning algorithm and improved sample reduction learning algorithm was put forward. It reduces the computational complexity and improves the online updating speed of the diagnosis model. UCI standard data and diesel engine fault data classification experiment results indicate that compared with several existing online diagnosis methods, ISKELM not only has high classification accuracy, but also greatly improves the online modeling speed, and it realizes diesel engine fault online diagnosis more quickly and accurately.

Key words: incremental sparse kernel extreme learning machine (ISKELM), sample sparseness, instantaneous information measurement, sparse kernel function dictionary, sample reduction learning algorithm, online diagnosis