There are different approaches to take when it comes to maintenance of certaine quipment in environments such as production factories, manufacturing facilities and research laboratories. No type of equipment that falls in any of these categories are perfect and lack the requirement of preservation in some form. One of these approaches to take is called Predictive Maintenance has the core functionality of predicting the need for maintenance ahead of time instead of having to rely on traditional methods such as scheduled maintenance or even run-to-failure. This prediction is often done using some form of machine learning algorithm that is able to, based on the analysis of past data, develop a learned behaviour in order to see patterns and thereby predict imminent errors in the equipment in question. In this paper, a deep learning neural network using LSTM for predicting future potential errors in an electron accelerator facility is applied, developed and tested. This is shown in the form of a proof of concept, directly tied to the specific accelerator in the facility of MAX IV, just outside Lund, Sweden. The process includes developing software for data processing, deep learning neural networks and result review. The best result from the developed model that has been trained and tested on recorded unseen data from the facility and has an error detection rate of 98.17%. The proof of concept is demonstrated, which concept is that it is possible to carry out multivariate time series forecasting using predictive maintenance in MAX IV electron accelerator with tangible accuracy.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:mau-52974 |
| Date | January 2022 |
| Creators | Heinze, Henrik, Persson, Olof |
| Publisher | Malmö universitet, Institutionen för datavetenskap och medieteknik (DVMT) |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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