Data-driven based Fault Diagnosis using Principal Component Analysis

Modern industrial systems are growing day by day and unlikely their complexity is also increasing. On the other hand, the design and operations have become a key focus of the researchers in order to improve the production system. To cope up with these chellenges, the data-driven technique like principal component analysis (PCA) is famous to assist the working systems. A data in bulk quanitity from the sensor measurements are often available in such industrial systems. Considering the modern industrial systems and their economic benifits, the fault diagnostic techniqes have been deeply studied. For example, the techniques that consider the process data as the key element. In this paper, the faults have been detected with the data-driven approach using PCA. In particular, the faults have been detected by using T^2 and Q statistics. In this process, PCA projects large data into smaller dimensions. Additionally it also preserves all the important information of process. In order to understand the impact of the technique, Tennessee Eastman chemical plant is considerd for the performance evaluation.

[1]  Donghua Zhou,et al.  Total projection to latent structures for process monitoring , 2009 .

[2]  Nola D. Tracy,et al.  Multivariate Control Charts for Individual Observations , 1992 .

[3]  E. Martin,et al.  Probability density estimation via an infinite Gaussian mixture model: application to statistical process monitoring , 2006 .

[4]  Paul M. Frank,et al.  Analytical and Qualitative Model-based Fault Diagnosis - A Survey and Some New Results , 1996, Eur. J. Control.

[5]  Frank Klawonn,et al.  Incremental quantile estimation , 2010, Evol. Syst..

[6]  Ping Zhang,et al.  A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process , 2012 .

[7]  Steven X. Ding,et al.  Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems , 2014 .

[8]  Donghua Zhou,et al.  Quality Relevant Data-Driven Modeling and Monitoring of Multivariate Dynamic Processes: The Dynamic T-PLS Approach , 2011, IEEE Transactions on Neural Networks.

[9]  Steven X. Ding,et al.  Improved canonical correlation analysis-based fault detection methods for industrial processes , 2016 .

[10]  Okyay Kaynak,et al.  Improved PLS Focused on Key-Performance-Indicator-Related Fault Diagnosis , 2015, IEEE Transactions on Industrial Electronics.

[11]  Leo H. Chiang,et al.  Exploring process data with the use of robust outlier detection algorithms , 2003 .

[12]  K. A. Kosanovich,et al.  Monitoring Process Performance in Real-Time , 1992, 1992 American Control Conference.

[13]  Steven X. Ding,et al.  Canonical correlation analysis-based fault detection methods with application to alumina evaporation process , 2016 .

[14]  S. D. Jong SIMPLS: an alternative approach to partial least squares regression , 1993 .

[15]  Barry M. Wise,et al.  The process chemometrics approach to process monitoring and fault detection , 1995 .

[16]  B. K. Panigrahi,et al.  Review of control and fault diagnosis methods applied to coal mills , 2015 .