Title :
Multi-Agent Remote Predictive Diagnosis of Dangerous Good Transports
Author :
Arpaia, Pasquale ; Lucariello, Giuseppe ; Zanesco, Antonio
Author_Institution :
Dept. of Eng., Sannio Univ., Benevento
Abstract :
A real-time remote monitoring system for motor-vehicle fleets of dangerous good transport is proposed for the dynamic fleet management, the predictive diagnosis and the failure prognosis of vehicle wear, operating danger, and fraud on goods. Predictive fault diagnosis and failure prognosis are performed by a hierarchical multi-agent architecture based on 2 levels: (i) fault detection and isolation, by wavelet transforms (signal segmentation), Bayesian logic (feature extraction), and cultural algorithms (fault isolation); and (ii) diagnosis-prognosis, by hybrid structures fuzzy/neuro-fuzzy
Keywords :
condition monitoring; fault diagnosis; feature extraction; fuzzy logic; fuzzy neural nets; multi-agent systems; road vehicles; traffic engineering computing; wavelet transforms; Bayesian logic; cultural algorithms; dangerous good transports; diagnostic reasoning; dynamic fleet management; failure prognosis; fault detection; fault isolation; feature extraction; fuzzy logic; fuzzy neural networks; fuzzy structure; motor-vehicle fleets; multiagent system; neuro-fuzzy structure; predictive fault diagnosis; remote monitoring system; remote predictive diagnosis; road transportation; signal segmentation; vehicle wear; wavelet transforms; Bayesian methods; Fault detection; Fault diagnosis; Feature extraction; Fuzzy logic; Real time systems; Remote monitoring; Remotely operated vehicles; Vehicle dynamics; Wavelet transforms; Diagnostic reasoning; Digital system fault diagnosis; Fault diagnosis; Fuzzy logic; Fuzzy neural networks; Road transportation;
Conference_Titel :
Instrumentation and Measurement Technology Conference, 2005. IMTC 2005. Proceedings of the IEEE
Conference_Location :
Ottawa, Ont.
Print_ISBN :
0-7803-8879-8
DOI :
10.1109/IMTC.2005.1604456
