An Industrial Multi Agent System for real-time distributed collaborative prognostics. (October 2019)

• Record Type: Journal Article
• Authors: Salvador Palau, Adrià; Dhada, Maharshi Harshadbhai; Bakliwal, Kshitij; Parlikad, Ajith Kumar
• Is Part Of: Engineering applications of artificial intelligence. Volume 85(2019)
• Page Start: 590
• Page End: 606
• Publication Date: 2019-10
• Subjects: Multi agent systems -- Distributed systems -- Recurrent neural networks -- Prognostics -- Networks -- Asset management; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2019.07.013
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Despite increasing interest, real-time prognostics (failure prediction) is still not widespread in industry due to the difficulties of existing systems to adapt to the dynamic and heterogeneous properties of real asset fleets. In order to address this, we present an Industrial Multi Agent System for real-time distributed collaborative prognostics. Our system fulfils all six core properties of Advanced Multi Agent Systems: Distribution, Flexibility, Adaptability, Scalability, Leanness, and Resilience. Experimental examples of each are provided for the case of prognostics using the C-MAPPS engine degradation data set, and data from a fleet of industrial gas turbines. Prognostics are performed using the Weibull Time To Event-Recurrent Neural Network algorithm. Collaboration is achieved by sharing information between agents in the system. We conclude that distributed collaborative prognostics is especially pertinent for systems with presence of sensor faults, limited computing capabilities or significant fleet heterogeneity.