Paper Info
Title
Adaptive Mamdani fuzzy model for condition-based maintenance
Abstract
Proper maintenance of equipment to prevent failures has become increasingly important. For manufacturing companies, it enables uninterrupted production to support lean manufacturing. For commercial carriers, it ensures the safety of passengers and crew members. Maintenance technology has progressed from time-based to condition-based. The idea of condition-based maintenance (CBM) is to monitor equipment using various sensors to enable real-time diagnosis of impending failures and prognosis of equipment health. The success of CBM hinges on the ability to develop accurate diagnosis/prognosis models. These models must be cognitive friendly for them to gain user acceptance, especially in safety critical applications. This paper presents a neuro-fuzzy modeling approach for CBM. The emphasis is on model comprehensibility so it can effectively serve as a decision-aid for domain experts. The comprehensibility of a neuro-fuzzy system usually deteriorates once rules are tuned. To solve this problem, Kullback-Leibler mean information is used to evaluate and refine tuned rules so they remain easily interpretable. The effectiveness of this modeling approach is demonstrated via a couple of real-world applications.
Year
DOI
Venue
2007
10.1016/j.fss.2007.07.004
Fuzzy Sets and Systems
Keywords
Field
DocType
neuro-fuzzy modeling approach,lean manufacturing,neural network,equipment health,accurate diagnosis,diagnosis,condition-based maintenance,cbm hinge,proper maintenance,modeling approach,adaptive mamdani fuzzy model,model comprehensibility,maintenance technology,fuzzy systems,real time,kullback leibler,neuro fuzzy,fuzzy system
Fuzzy model,Condition-based maintenance,Crew,Information processing,Fuzzy set,Lean manufacturing,Artificial intelligence,Fuzzy control system,Artificial neural network,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
158
24
Fuzzy Sets and Systems
Citations 
PageRank 
References 
18
0.96
7
Authors
2
Name
Order
Citations
PageRank
Ranganath Kothamasu1181.30
Samuel H. Huang219319.64