Statistical process control

Physics

Process control

Universality (dynamical systems)

Feature (linguistics)

Operating system

Algorithm

Data mining

Philosophy

Artificial intelligence

False alarm

Computer science

Linguistics

Quantum mechanics

Process (computing)

Constant false alarm rate

IEEE Transactions on Instrumentation and Measurement

Multivariate statistical process monitoring has been widely used in industry. However, traditional algorithms often ignore the dynamic characteristics of actual industry process. This study proposes a novel algorithm called multistep dynamic slow feature analysis (MS-DSFA), which has completed the full-condition monitoring of a dynamic system and divided dynamic structures more precisely. This algorithm achieves an optimal detection rate according to multiple control limits. To enrich the experiments, we select a numerical example, Tennessee Eastman process, and XJTU-SY bearing data sets to verify the universality of the algorithm. According to the overall score for optimal detection rates and false alarm rates, MS-DSFA stands out in the comparison of existing algorithms.

Multistep Dynamic Slow Feature Analysis for Industrial Process Monitoring