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A.J. Robinson

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Master thesis (2022) - A.J. Robinson, X. Jiang, R.R. Negenborn, N. Maat
Industrial electric motors are at the heart of almost every industry. They are a 47 billion USD market in 2020 and consume 70% of all industrial electricity. They are generally paired with a gearbox and load, which is referred to as an electric gearmotor system. Being so essential means that it is important to avoid failures, 98% of 300 researched companies by ITIC reported a cost of 100.000 USD every hour of downtime. To detect failures a technology called fault detection and diagnosis (FDD) is used, which is a method to foresee failures or faults in a system that deteriorates over time through evaluating the state of the system. It is an extensively academically research subject, however, has hardly been adopted in industrial settings where electric gearmotor systems are applied. Thus, a FDD model was developed to provide insight, knowledge, and a practical example into the necessities of an industrial FDD model. This was achieved through conducting an analysis on the state-of-the-art of FDD in industrial settings and conducting a literature review on FDD. Based on an analysis of the conclusions a hybrid diagnostics model was developed. For the fault detection a model-based solution was used, it compared the predicted torque to the measured torque of a motor to create a health indication value. If this value crosses a pre-determined threshold an alarm would go off. For fault identification a decision tree machine learning algorithm is used to identify: blockage, bearing, gear or random failure in a system. To verify and validate the hybrid diagnostics model it was applied to a client of SEW Eurodrive where data was available of a known system. The system had a fault detection accuracy as high as 94% and could classify failures with an accuracy of 93%. ...
Review (2018) - Philip D. Prewett, Cornelis W. Hagen, More authors..., Claudia Lenk, Steve Lenk, Marcus Kaestner, Tzvetan Ivanov, Ahmad Ahmad, Alex P.G. Robinson, Sangeetha Hari, Marijke Scotuzzi
Following a brief historical summary of the way in which electron beam lithography developed out of the scanning electron microscope, three state-of-the-art charged-particle beam nanopatterning technologies are considered. All three have been the subject of a recently completed European Union Project entitled "Single Nanometre Manufacturing: Beyond CMOS". Scanning helium ion beam lithography has the advantages of virtually zero proximity effect, nanoscale patterning capability and high sensitivity in combination with a novel fullerene resist based on the sub-nanometre C60 molecule. The shot noise-limited minimum linewidth achieved to date is 6 nm. The second technology, focused electron induced processing (FEBIP), uses a nozzle-dispensed precursor gas either to etch or to deposit patterns on the nanometre scale without the need for resist. The process has potential for high throughput enhancement using multiple electron beams and a system employing up to 196 beams is under development based on a commercial SEM platform. Among its potential applications is the manufacture of templates for nanoimprint lithography, NIL. This is also a target application for the third and final charged particle technology, viz. field emission electron scanning probe lithography, FE-eSPL. This has been developed out of scanning tunneling microscopy using lower-energy electrons (tens of electronvolts rather than the tens of kiloelectronvolts of the other techniques). It has the considerable advantage of being employed without the need for a vacuum system, in ambient air and is capable of sub-10 nm patterning using either developable resists or a self-developing mode applicable for many polymeric resists, which is preferred. Like FEBIP it is potentially capable of massive parallelization for applications requiring high throughput. ...