Fault diagnosis in intelligent greenhouse control with decomposed neural models

Peter Eredics, T. Dobrowiecki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The novel approach to the intelligent greenhouse control is based on predictive thermal models and actuator action plans to increase control performance. As operating the actuators essentially changes the thermal dynamics, the overall thermal model is decomposed into sub-models based on the state of the actuators. For the intelligent control it is essential to know the actuator configuration to select the most appropriate sub-model, but unfortunately information available about actuator states is not reliable enough to be directly applicable. This paper proposes a method to detect the difference between the believed and the actual state of the greenhouse and to identify the most likely model for the actual state. The method can be used to support the intelligent control in case of actuator malfunction or other accidents and is also suitable to provide possible reasons of the malfunctions to support the greenhouse personnel to take the necessary repair actions. Copyright

Original languageEnglish
Title of host publication13th IMEKO TC10 Workshop on Technical Diagnostics 2014: Advanced Measurement Tools in Technical Diagnostics for Systems' Reliability and Safety
PublisherIMEKO-International Measurement Federation Secretariat
Pages45-49
Number of pages5
ISBN (Print)9781632669841
Publication statusPublished - 2014
Event13th IMEKO TC10 Workshop on Technical Diagnostics 2014: Advanced Measurement Tools in Technical Diagnostics for Systems' Reliability and Safety - Warsaw, Poland
Duration: Jun 26 2014Jun 27 2014

Other

Other13th IMEKO TC10 Workshop on Technical Diagnostics 2014: Advanced Measurement Tools in Technical Diagnostics for Systems' Reliability and Safety
CountryPoland
CityWarsaw
Period6/26/146/27/14

Fingerprint

Greenhouses
Failure analysis
Actuators
Intelligent control
Accidents
Repair
Personnel
Hot Temperature

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality

Cite this

Eredics, P., & Dobrowiecki, T. (2014). Fault diagnosis in intelligent greenhouse control with decomposed neural models. In 13th IMEKO TC10 Workshop on Technical Diagnostics 2014: Advanced Measurement Tools in Technical Diagnostics for Systems' Reliability and Safety (pp. 45-49). IMEKO-International Measurement Federation Secretariat.

Fault diagnosis in intelligent greenhouse control with decomposed neural models. / Eredics, Peter; Dobrowiecki, T.

13th IMEKO TC10 Workshop on Technical Diagnostics 2014: Advanced Measurement Tools in Technical Diagnostics for Systems' Reliability and Safety. IMEKO-International Measurement Federation Secretariat, 2014. p. 45-49.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Eredics, P & Dobrowiecki, T 2014, Fault diagnosis in intelligent greenhouse control with decomposed neural models. in 13th IMEKO TC10 Workshop on Technical Diagnostics 2014: Advanced Measurement Tools in Technical Diagnostics for Systems' Reliability and Safety. IMEKO-International Measurement Federation Secretariat, pp. 45-49, 13th IMEKO TC10 Workshop on Technical Diagnostics 2014: Advanced Measurement Tools in Technical Diagnostics for Systems' Reliability and Safety, Warsaw, Poland, 6/26/14.
Eredics P, Dobrowiecki T. Fault diagnosis in intelligent greenhouse control with decomposed neural models. In 13th IMEKO TC10 Workshop on Technical Diagnostics 2014: Advanced Measurement Tools in Technical Diagnostics for Systems' Reliability and Safety. IMEKO-International Measurement Federation Secretariat. 2014. p. 45-49
Eredics, Peter ; Dobrowiecki, T. / Fault diagnosis in intelligent greenhouse control with decomposed neural models. 13th IMEKO TC10 Workshop on Technical Diagnostics 2014: Advanced Measurement Tools in Technical Diagnostics for Systems' Reliability and Safety. IMEKO-International Measurement Federation Secretariat, 2014. pp. 45-49
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