An intelligent service matching method for mechanical equipment condition monitoring using the fibre Bragg grating sensor network

ABSTRACT

Due to the advantages of being able to function under harsh environmental conditions and serving as a distributed condition information source in a networked monitoring system, the fibre Bragg grating (FBG) sensor network has attracted considerable attention for equipment online condition monitoring. To provide an overall conditional view of the mechanical equipment operation, a networked service-oriented condition monitoring framework based on FBG sensing is proposed, together with an intelligent matching method for supporting monitoring service management. In the novel framework, three classes of progressive service matching approaches, including service-chain knowledge database service matching, multi-objective constrained service matching and workflow-driven human-interactive service matching, are developed and integrated with an enhanced particle swarm optimisation (PSO) algorithm as well as a workflow-driven mechanism. Moreover, the manufacturing domain ontology, FBG sensor network structure and monitoring object are considered to facilitate the automatic matching of condition monitoring services to overcome the limitations of traditional service processing methods. The experimental results demonstrate that FBG monitoring services can be selected intelligently, and the developed condition monitoring system can be re-built rapidly as new equipment joins the framework. The effectiveness of the service matching method is also verified by implementing a prototype system together with its performance analysis.

KEYWORDS:

• Mechanical equipment condition monitoring
• networked service-oriented model
• FBG sensor network
• intelligent service matching
• ontology knowledge database
• enhanced PSO
• workflow driven
• manufacturing web service

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was supported by the National Natural Science Foundation of China [grant number 51305319], the Specialised Research Fund for the Doctoral Program of Higher Education of China [grant number 20120143110017] and the Key Project of Natural Science Foundation of Hubei Province of China [grant number 2013CFA044].