Data density-based fault detection and diagnosis with nonlinearities between variables and multimodal data distributions

作者：Kaneko Hiromasa; Funatsu Kimito^*

来源：Chemometrics and Intelligent Laboratory Systems, 2015, 147: 58-65.

DOI：10.1016/j.chemolab.2015.07.016

• Process control
• Fault detection
• Fault diagnosis
• Data density
• One-class support vector machine

Multivariate statistical process control (MSPC) is an important means of monitoring multiple process variables and their interrelationships while controlling chemical and industrial plants efficiently and stably. To consider nonlinearities between process variables and multimodal data distributions, the data density can be used as an index for fault detection. Data domains with a low data density are considered abnormal states. However, after fault detection, faulty process variables cannot be diagnosed with an MSPC model based on the data density. Therefore, we have developed a new index to diagnose the process variables that contribute to process faults using a data density-based MSPC model. The proposed index uses the partial derivative of an MSPC model with respect to each process variable. We demonstrate the effectiveness of the proposed method using numerical simulation data, Tennessee Eastman process data, and real plant data analyses.

• 出版日期2015-10-15