Abstract:

In order to deal with the problem that locality preserving projections (LPP) does not take into account the global structure and dynamic characteristic of process data, a new fault detection method based on dynamic sparse locality preserving projections (DSLPP) is proposed. In the study, the original data matrix is firstly extended to a time-delay augmented matrix. Then, a sparse coefficient matrix which can represent global sparse reconstructive relationship of data is gotten by solving an optimal problem of sparse representation (SR). The sparse coefficient matrix combines with the objective function of LPP to form a new objective function for dimensionality reduction. The new dimensionality reduction algorithm can not only preserve the local neighbor structure of the original data space, but also have better effect in preserving the global sparse reconstructive relationship. At last, DSLPP-based T² and Q statistics are constructed respectively in the feature space and residual space for fault detection.The simulation results of Tennessee Eastman process demonstrate that the proposed method detects faults more quickly and achieves lower fault missing alarm rate than the LPP method.

Key words: fault detection, locality preserving projections, sparse representation, feature extraction, process monitoring

摘要：

针对保局投影(locality preserving projections,LPP)没有考虑过程数据的全局信息和动态性的问题,提出一种新的基于动态稀疏保局投影(dynamic sparse locality preserving projections,DSLPP)的故障检测方法。该方法首先将原始数据矩阵扩展为考虑时序相关的增广矩阵,然后通过求解最优稀疏表示(sparse representation,SR)问题,得到能够表示数据全局稀疏重构关系的稀疏系数矩阵,并将其与LPP算法结合,构建综合考虑数据局部和全局关系的目标函数进行数据降维,最后分别在特征空间和残差空间构造T²统计量和Q统计量进行故障检测。TEP的仿真结果表明,与LPP方法相比,新方法能更迅速检测故障发生并降低过程监控漏报率。

关键词: 故障检测, 保局投影, 稀疏表示, 特征提取, 过程监控