基于自适应稀疏表示和保局投影的工业故障检测

Industrial Fault Detection Based on Adaptive Sparse Representation and Locality Preserving Projections

针对工业过程中检测和储存的数据维度不断增大,传统的检测方法中存在处理速度慢、故障特征提取不明显等问题,提出了一种基于自适应稀疏表示和保局投影(Adaptive Sparse Representation and Locality Preserving Projections,ASRLPP)的故障检测方法。首先利用稀疏字典学习算法构造残差空间对数据进行特征提取,使数据的全局特征更加明显;然后在残差空间中利用保局投影(Locality Preserving Projections,LPP)算法进行降维操作,对数据进行过滤降维,保留局部特征;最后利用T~2统计量计算控制限进行监控。在检测过程中,引入自适应更新规则,将检测到的正常工况数据用于更新初始训练集,选取更加合理的训练集,动态地调整控制限,使其与所处理的故障数据特征相适应,提高故障检测效率和准确率。通过一个数值例子以及TE(Tennessee-Eastman)过程仿真验证了ASRLPP算法的有效性。

With the increasing of detection and storage data dimension in industrial process,the traditional detection methods are encountering difficulties such as slow processing speed and unconspicuous feature extraction.Therefore,it is quite necessary and important to research the fault detection technology on high-dimensional data.To this end,this paper proposes a fault detection method based on adaptive sparse representation and locality preserving projects(ASRLPP).Firstly,the sparse dictionary learning algorithm is used to construct the residual space for feature extraction,which can make the global feature of the data more obvious.Then,the locality preserving projections(LPP)algorithm is used to reduce the dimension of the data in the residual space.LPP can effectively preserve the local features of data.Finally,T~2 statistics are used to calculate the control limit for monitoring.In the process of monitoring,the adaptive updating rules are introduced to update the initial training data,which can improve the efficiency and accuracy of fault detection by dynamically updating the control limits.Additionally,it is shown via the numerical example test and tennessee-eastman(TE)process simulation that the proposed ASRLPP algorithm is superior to the LPP and sparse residual distance(SRD)algorithms and has better fault detection ability in industrial process.