Fault detection method based on relative-transformation partial least squares

作者：Shi Huaitao^*; Liu Jianchang; Zhang Yu; Li Long

来源：Chinese Journal of Scientific Instrument, 2012, 33(4): 816-822.

• Data simulation
• Data space
• Detection methods
• Eigenvalues
• Force senior fault
• Force sensor
• Latent variable
• Mahalanobis distances
• On-line detection
• On-line performance
• Partial least square (PLS)
• Partial least-squares method
• Process faults
• Relative transformation
• Rolling system
• Sample data
• Simulation experiments
• Tennessee Eastman
• Theoretic analysis

Aiming at the problems that the eigenvalues are approximately equal and the representative latent variables can not be extracted effectively after dimensionless standardization processing in partial least squares method (PLS), a fault detection method based on relative-transformation PLS (RTPLS) is proposed. The method introduces the relative transformation scheme based on Mahalanobis distance. Firstly, the RTPLS approach transfers the original data space into relative space through computing the Mahalanobis distance between sample data. Secondly, PLS approach is used to decompose the data in the relative space, extract the representative latent variables, build the fault detection model and implement on-line detection of the sample data. In the end, the proposed method was applied to detect Tennessee Eastman (TE) process fault and the force sensor fault of a rolling system in simulation experiments; the effectiveness and applicability of the proposed method are proved with the actual data simulations. Both theoretic analysis and simulation experiment demonstrate that RTPLS based fault detection method can directly remove the effect of dimension, effectively extract the latent variables with more variation degree and representativeness, and then improve the accuracy and on-line performance of fault detection.

• 出版日期2012
• 单位东北大学