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

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.

Metrological Challenges in Collaborative Sensing: Applicability of Digital Calibration Certificates

“…Yong et al have proposed an architecture that can be used to take the various sources of uncertainty, including the sensors’ measurement uncertainty, into account in the predictions. Salvador Palau et al have presented an advanced multi-agent system for real-time distributed collaborative prognostics [ 36 ].…”

Secure and communications‐efficient collaborative prognosis

“…As a result, any given asset's data repository is enriched with failure trajectories originating from other assets. Prediction models are then trained using the enriched dataset [9]. Other such similarity‐based prognoses have also been proposed by researchers [12–14].…”

“…However, an industrial system of assets is often characterised by widespread heterogeneity, due to assets operating in varied conditions, model types, and the presence of multiple failure modes. It has been shown that in such settings, it is beneficial to have separate prediction models catering to subsets of asset populations, identified based on some sense of homogeneity [7, 9].…”

“…It has also been shown that the failure predictions are most accurate if the model learns from a single asset only [7]. Recently popularised distributed computing architectures for industrial systems enable every asset in the fleet to have its own corresponding prediction model [8, 9, 11, 16]. However, the individualised models would require assets to fail a certain number of times so that necessary training data is available [17, 24].…”

“…The collaborative prognosis technique aims at reducing these asset failures by enabling assets to identify other similar assets in the fleet and learn from their failures as well [7]. This is made possible by identifying clusters of similar assets and exchanging failure data within assets comprising these clusters [7, 9]. Collaborative prognosis is most suitable for assets with high reliability, such as flight engines, where individual assets would not experience enough failures to generate sufficient training data [10].…”