On the design of Neutral Scanning Helium Atom Microscopes (SHeM) : Optimal configurations and evaluation of experimental findings

Scanning Helium Microscopes (SHeMs) are novel microscopy tools using neutral helium atoms as the imaging probe. Helium atoms have several advantages compared to other probes such as electrons or helium ions. Helium atoms are neutral and inert and when compared to electrons their higher mass leads to a smaller de-Broglie wavelength for a given energy. Furthermore, helium atoms are strictly surface sensitive, scattering off the electron density distribution off the surface. These combined properties allow for non-destructive mapping of the surface of virtually any vacuum-compatible solid sample. Helium ions have a similar mass but they interact more strongly with the sample because they are not inert and require much higher energies to achieve electrostatic focusing. Charge neutrality makes helium a great imaging corpuscle, but also means that designing SHeMs is very difficult. Neutral helium atoms are very hard to manipulate, as electromagnetic fields cannot be used to focus and redirect the beam - instead, one needs to use diffraction optics and apertures. They are also hard to detect because helium has the highest ionisation potential of all atoms - hindering the task of ionisation based detectors. Therefore, to have a functioning microscope, one needs to form a highly intense atom beam. This thesis presents the work done over the last years to optimise the intensity of SHeMs, and more generally their atom-optics configuration. Amongst the papers included here are the first ones to show that SHeM optics have well-defined intensity maxima that give optimal designs. These papers show that existing designs were suboptimal and that the intensity could be increased several orders of magnitude. This thesis also features the first paper to present a design for a 3D imaging SHeM. A true nano-scale stereo microscope based on Heliometric stereo, a technique adapted from light. Besides these theoretical papers, two papers are included that focus on understanding the helium beam using experimental data. These papers are important as they provide the experimental foundations for the theoretical models used. Amongst other findings, the papers explore the importance of the Knudsen number at the skimmer, the validity of different intensity models, and the top-hat profile of the beam. The research presented here happened in parallel to a two order of magnitude improvement in detector efficiency. I believe that now we are in the position to build high-resolution SHeMs that have the potential to become an important tool for science and industry.. . .Doktorgradsavhandlin

Exploiting traffic data to improve asset management and citizen quality of life

The main goal of this project was to demonstrate how large data sources such as Google Maps can be used to inform transportation-related asset management decisions. Specifically, we investigated how the interdependence between infrastructures and assets can be studied using transportation data and heat maps. This involves linking the effect of disruptions in lower-order assets to travel accessibility to private and public infrastructure. In order to demonstrate the viability of our approach, we conducted 5 case studies, 3 public and 2 private. On the public side, we collaborated with two county councils in the United Kingdom, specifically Cambridgeshire and Hertfordshire, and offered solutions to existing infrastructure-related problems proposed by them. For Cambridgeshire, we analysed the accessibility to Cambridge University’s new research centers and the criticality of roads leading to Addenbrooke’s Hospital in Cambridge. Similarly for Hertfordshire, the accessibility to different critical assets in the county were examined with the aim of supporting planning decisions. In addition, to highlight how our approach can bring benefits to private citizens, we solved two examples of commuting-related problems posed by students at the Institute for Manufacturing (IfM). We conclude that heat maps generated using the Google Maps API are powerful and efficient tools for use in infrastructure asset management. Our approach appears to be more cost-efficient and offers a higher quality of visualisation and presentation than other available tools. Furthermore, there exists the potential for a commercial spin-off: our approach can be employed in local, regional and national administrations to inform infrastructure-related decision-making, and can be used by commercial parties to improve employees’ commutes, parking, et ceteraCentre for Digital Built Britai

Federated Learning for Collaborative Prognosis

Modern industrial assets generate prodigious condition monitoring data. Various prognosis techniques can use this data to predict the asset’s remaining useful life. But the data in most asset fleets is distributed across multiple assets, bound by the privacy policies of the operators, and often legally protected. Such peculiar characteristics make data-driven prognosis an interesting problem. In this paper, we propose Federated Learning as a solution to the above mentioned challenges. Federated Learning enables the manufacturer to utilise condition monitoring data without moving it away from the corresponding assets. Concretely, we demonstrate Federated Averaging algorithm to train feed-forward, and recurrent neural networks for predicting failures in a simulated turbofan fleet. We also analyse the dependence of prediction quality on the various learning parameters.1. Siemens Industrial Turbomachinery U

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))

On the design of Neutral Scanning Helium Atom Microscopes (SHeM) : Optimal configurations and evaluation of experimental findings

Scanning Helium Microscopes (SHeMs) are novel microscopy tools using neutral helium atoms as the imaging probe. Helium atoms have several advantages compared to other probes such as electrons or helium ions. Helium atoms are neutral and inert and when compared to electrons their higher mass leads to a smaller de-Broglie wavelength for a given energy. Furthermore, helium atoms are strictly surface sensitive, scattering off the electron density distribution off the surface. These combined properties allow for non-destructive mapping of the surface of virtually any vacuum-compatible solid sample. Helium ions have a similar mass but they interact more strongly with the sample because they are not inert and require much higher energies to achieve electrostatic focusing. Charge neutrality makes helium a great imaging corpuscle, but also means that designing SHeMs is very difficult. Neutral helium atoms are very hard to manipulate, as electromagnetic fields cannot be used to focus and redirect the beam - instead, one needs to use diffraction optics and apertures. They are also hard to detect because helium has the highest ionisation potential of all atoms - hindering the task of ionisation based detectors. Therefore, to have a functioning microscope, one needs to form a highly intense atom beam. This thesis presents the work done over the last years to optimise the intensity of SHeMs, and more generally their atom-optics configuration. Amongst the papers included here are the first ones to show that SHeM optics have well-defined intensity maxima that give optimal designs. These papers show that existing designs were suboptimal and that the intensity could be increased several orders of magnitude. This thesis also features the first paper to present a design for a 3D imaging SHeM. A true nano-scale stereo microscope based on Heliometric stereo, a technique adapted from light. Besides these theoretical papers, two papers are included that focus on understanding the helium beam using experimental data. These papers are important as they provide the experimental foundations for the theoretical models used. Amongst other findings, the papers explore the importance of the Knudsen number at the skimmer, the validity of different intensity models, and the top-hat profile of the beam. The research presented here happened in parallel to a two order of magnitude improvement in detector efficiency. I believe that now we are in the position to build high-resolution SHeMs that have the potential to become an important tool for science and industry.. .

Velocity distributions in microskimmer supersonic expansion helium beams: High precision measurements and modeling

Supersonic molecular beams are used in many applications ranging from spectroscopy and matter wave optics to surface science. The experimental setup typically includes a conically shaped, collimating aperture, the skimmer. It has been reported that microskimmers with diameters below 10 μm produce beams with significantly broader velocity distributions (smaller speed ratios) than larger skimmers. Various explanations for this phenomenon have been proposed, but up till now, only a limited amount of data has been available. Here we present a systematic study of the velocity distribution in microskimmer supersonic expansion helium beams. We compare a 4 μm diameter skimmer with a 390 μm diameter skimmer for room temperature and cooled beams in the pressure range 11-181 bars. Our measurements show that for properly aligned skimmers, the only difference is that the most probable velocity for a given pressure and temperature is slightly lower for a microskimmed beam. We ascribed this to the comparatively narrow and long geometry of the microskimmers which can lead to local pressure variations along the skimmer channel. We compare our measurements to a model for the supersonic expansion and obtain good agreement between the experiments and simulations

Research data supporting "True to size surface mapping with neutral helium atoms"

Simulated SHeM images and the associated 3D reconstructions and reconstruction errors that are presented in the associated publication. The contents of this data pack are split into: 1. A series of ray tracing simulation results that are provided in full, base simulation data of all the reconstructions presented in the paper are present. Those used for non-normal incidence with rotation are found with the 3D reconstructions rather than in the ray tracing results folder. 2. A selection of heliometric reconstructions. All the reconstructions that are directly presented in the paper are included. 3. Data on overall reconstruction accuracy that is used to produce the plots in the paper. All data is provided either in plain text or matlab `.mat` format. The provided `.stl` and `.png` files are for convinience, and replicate data stored in the `.mat` files