Multi-agent system architectures for collaborative prognostics

This paper provides a methodology to assess the optimal Multi-Agent architecture for collaborative prognostics in modern fleets of assets. The use of Multi- Agent Systems has been shown to improve the ability to predict equipment failures by enabling machines with communication and collaborative learning capabilities. Di fferent architectures have been postulated for industrial Multi-Agent Systems in general. A rigorous analysis of the implications of their implementation for collaborative prognostics is essential to guide industrial deployment. In this paper, we investigate the cost and reliability implications of using di fferent Multi-Agent Systems architectures for collaborative failure prediction and maintenance optimization in large fleets of industrial assets. Results show that purely distributed architectures are optimal for high-value assets, while hierarchical architectures optimize communication costs for low-value assets. This enables asset managers to design and implement Multi-Agent systems for predictive maintenance that signi ficantly decrease the whole-life cost of their assets.The project that has generated these results has been supported by a la Caixa Fellowship (ID 100010434), with code LCF/BQ/EU17/11590049. This research was partly supported by Siemens Industrial Turbomachinery UK. This research was also partly supported by the Next Generation Converged Digital Infrastructure project (EP/R004935/1) funded by the Engineering and Physical Sciences Research Council and BT. The server used to perform the experiments in this paper was funded by the Centre for Digital Built Britain

An Industrial Multi Agent System for real-time distributed collaborative prognostics

Despite increasing interest, real-time prognostics (failure prediction) is still not widespread in industry due to the di fficulties 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 fufil ls 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

An Industrial Multi Agent System for real-time distributed collaborative prognostics

Despite increasing interest, real-time prognostics (failure prediction) is still not widespread in industry due to the di fficulties 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 fufil ls 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

Recurrent Neural Networks for real-time distributed collaborative prognostics

We present the first steps towards real-time distributed collaborative prognostics enabled by an implementation of the Weibull Time To Event - Recurrent Neural Network (WTTE-RNN) algorithm. In our system, assets determine their time to failure (TTF) in real-time according to an asset-specific model that is obtained in collaboration with other similar assets in the asset fleet. The presented approach builds on the emergent field of similarity analysis in asset management, and extends it to distributed collaborative prognostics. We show how through collaboration between assets and distributed prognostics, competitive time to failure estimates can be obtained

A Multi Agent System architecture to implement Collaborative Learning for social industrial assets

The `Industrial Internet of Things' aims to connect industrial assets with one another and subsequently bene t from the data that is generated, and shared, among these assets. In recent years, the extensive instrumentation of machines and the advancements in Information Communication Technologies are re-shaping the role of assets in our industrial systems. An emerging paradigm here is the concept of `social assets': assets that collaborate with each other in order to improve system performance. Cyber-Physical Systems (CPS) are formed by embedding the assets with computing capabilities and linking them with their cyber models. These are known as the `Digital Twins' of the assets, and form the backbone of social assets. Collaboration among assets, by allowing them to share and analyse data from other assets can make embedded computing algorithms more accurate, robust and reliable. This paper proposes a Multi Agent System (MAS) architecture for collaborative learning, and presents the fi ndings of an implementation of this architecture for a prognostics problem. Collaboration among assets is performed by calculating inter-asset similarity during operating condition to identify `friends' and sharing operational data within these clusters of friends. The architecture described in this paper also presents a generic model for the Digital Twins of assets. Prognostics is demonstrated for the C-MAPSS turbofan engine degradation simulated data-set (Saxena and Goebel (2008))