Predicting Remaining Useful Life with Similarity-Based Priors

Prognostics is the area of research that is concerned with predicting the remaining useful life of machines and machine parts. The remaining useful life is the time during which a machine or part can be used, before it must be replaced or repaired. To create accurate predictions, predictive techniques must take external data into account on the operating conditions of the part and events that occurred during its lifetime. However, such data is often not available. Similarity-based techniques can help in such cases. They are based on the hypothesis that if a curve developed similarly to other curves up to a point, it will probably continue to do so. This paper presents a novel technique for similarity-based remaining useful life prediction. In particular, it combines Bayesian updating with priors that are based on similarity estimation. The paper shows that this technique outperforms other techniques on long-term predictions by a large margin, although other techniques still perform better on short-term predictions.</p

Collaborative prognostics in Social Asset Networks

With the spread of Internet of Things (IoT) technologies, assets have acquired communication, processing and sensing capabilities. In response, the fi eld of Asset Management has moved from fleet-wide failure models to individualised asset prognostics. Individualised models are seldom truly distributed, and often fail to capitalise the processing power of the asset fleet. This leads to hardly scalable machine learning centralised models that often must nd a compromise between accuracy and computational power. In order to overcome this, we present a novel theoretical approach to collaborative prognostics within the Social Internet of Things. We introduce the concept of Social Asset Networks, de ned as networks of cooperating assets with sensing, communicating and computing capabilities. In the proposed approach, the information obtained from the medium by means of sensors is synthesised into a Health Indicator, which determines the state of the asset. The Health Indicator of each asset evolves according to an equation determined by a triplet of parameters. Assets are given the form of the equation but they ignore their parametric values. To obtain these values, assets use the equation in order to perform a non-linear least squares t of their Health Indicator data. Using these estimated parameters, they are interconnected to a subset of collaborating assets by means of a similarity metric. We show how by simply interchanging their estimates, networked assets are able to precisely determine their Health Indicator dynamics and reduce maintenance costs. This is done in real time, with no centralised library, and without the need for extensive historical data. We compare Social Asset Networks with the typical self-learning and fleet-wide approaches, and show that Social Asset Networks have a faster convergence and lower cost. This study serves as a conceptual proof for the potential of collaborative prognostics for solving maintenance problems, and can be used to justify the implementation of such a system in a real industrial fleet.EU H202

Towards smart electrolytic plasma technologies: An overview of methodological approaches to process modelling

This paper reviews the present understanding of electrolytic plasma processes (EPPs) and approaches to their modelling. Based on the EPP type, characteristics and classification, it presents a generalised phenomenological model as the most appropriate one from the process diagnostics and control point of view. The model describes the system ‘power supply–electrolyser–electrode surface’ as a system with lumped parameters characterising integral properties of the surface layer and integral parameters of the EPP. The complexity of EPPs does not allow the drawing of a set of differential equations describing the treatment, although a model can be formalised for a particular process as a black box regression. Evaluation of dynamic properties reveals the multiscale nature of electrolytic plasma processes, which can be described by three time constants separated by 2–3 orders of magnitude (minutes, seconds and milliseconds), corresponding to different groups of characteristics in the model. Further developments based on the phenomenological approach and providing deeper insights into EPPs are proposed using frequency response methodology and electromagnetic field modelling. Examples demonstrating the efficiency of the proposed approach are supplied for EPP modelling with static and dynamic neural networks, frequency response evaluations and electromagnetic field calculations