关键词: 移不变, 稀疏分类, 特征标记, 拉格朗日对偶, 实时性, 汽油发动机, 在线故障识别

Abstract: According to the low on-line efficiency of the shift invariant sparse coding, a fast shift invariant sparse coding algorithm which has high on-line efficiency is proposed in this paper. Then the on-line faults recognition can be realized combining the sparse representation classification. The algorithm transforms the shift invariant optimization problem from the time domain to the frequency domain firstly. The sparse coefficient and classification dictionary are solved quickly using the feature sign and Lagrange dual method, which can reduce the computational time complexity without the loss of classification accuracy. And the real-time performance for fault recognition will be promoted. The experimental results of engine show that, while the engine works in the idle and 1500~2000?r/min conditions, the novelty algorithm gets the average recognition accuracy of five kinds of common faults are respectively 92.35% and 91.44%, which are approximately equal to other algorithms. But the average online fault recognition speed are 13.91?ms and 14.5?ms respectively, the dictionary training time are 1.43?s and 1.47?s, which are obviously faster than those of other recognition algorithms.

Key words: shift invariant, sparse representation classification, feature sign, Lagrange dual method, real-time performance, gasoline engine, on-line fault recognition