Feature Extraction from Time-Series Data for Process Monitoring

Takeshi Fujiwara, Hirokazu Nishitani, Takeshi Fujiwara

Research output: Contribution to journalArticlepeer-review


A plant operator monitors time-series data of process variables to judge the state of process, diagnose abnormal states, and to identify failure origins. In this study, a new feature extraction method which extracts simultaneously both local features such as spikes and step changes, and the trend which characterizes global changes is provided from the viewpoint of process monitoring. In this method, the continuous function interpolated from the time-series data is represented by a series of inflection points first. Each time interval between two inflection points is called an episode. Then an approximation function of the time-series data is made iteratively by way of merging these episodes. This feature extraction method is also useful for compaction of a large number of process data.

Original languageEnglish
Pages (from-to)1103-1110
Number of pages8
Issue number5
Publication statusPublished - 1996
Externally publishedYes


  • Data Analysis
  • Feature Extraction
  • Process Monitoring
  • Process System
  • Process Trend

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)


Dive into the research topics of 'Feature Extraction from Time-Series Data for Process Monitoring'. Together they form a unique fingerprint.

Cite this