Fault detection and identification in a deep trough hydroponic system using adaptive neuro-fuzzy analysis |
Posted on:2009-04-27 | Degree:Ph.D | Type:Dissertation |
University:Cornell University | Candidate:Setiawan, Albert | Full Text:PDF |
GTID:1448390005450041 | Subject:Engineering |
Abstract/Summary: | |
An early fault detection and identification system (FDI) can be an important part in any plant production system. A FDI can be used to avoid costly repairs and long disruptions in production. A hydroponic plant production system is a complex biological system that contains plants and microorganisms in its processes that are hard to model mathematically. A soft computing method called a neuro-fuzzy system is chosen to implement the FDI. A neuro-fuzzy system is a hybrid combination of a neural network and a fuzzy logic system that combines the best from both methods: knowledge based structure from fuzzy logic and a proven learning capability from a neural network. An adaptive neuro-fuzzy inference system (ANFIS) is developed to detect and identify actuator and sensor faults in the hydroponic plant production system. A separate system for exploring the ANFIS capability in detecting biological faults is also investigated. The novelty of the neuro-fuzzy FDI in this research used a single output to simultaneously detect and identify various faults in the system. |
Keywords/Search Tags: | System, Fault detection and identification, Neuro-fuzzy, Detect and identify |
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