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Machine Learning Based Fault Prediction for Real-time Scheduling on Shop Floor
KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Nowadays, scheduling on a shop floor is only focused on the availabil-ity of resources, where the potential faults are not able to be predicted.

A big data analytics based fault prediction was proposed to be ap-plied in scheduling, which require a real-time decision making. To select a proper machine learning algorithm for real-time scheduling, this paper first proposes a data generation method in terms of pattern complexity and scale. Three levels of depth, an index of data complex-ity, and three levels of data attributes, an index of data scale, are used to obtain the data sets. Based on those data sets, ten commonly used machine learning algorithms are trained, in which the parameters are adjusted to achieve a high accuracy. The testing results including three indexes including training time, testing time and prediction accuracy, are used to evaluate the algorithms.

The results of the tests shows that when working with data of sim-ple structure and small scale, typical machine learning methods like Naive Bayes classifier and SVM is good enough with fast training an high accuracy. When dealing with complex data on large scale, deep learning methods like CNN and DBN outperform all other methods.

Abstract [sv]

Nu för tiden, schemaläggning på en affärsplan är endast inriktad på tillgången på resurser, där de potentiella felen inte kan förutses.

En stor dataanalysbaserad felprediktion föreslogs att tillämpas vid schemaläggning, vilket kräver beslutsfattande i realtid. För att välja en riktig maskininlärningsalgoritm för realtidsplanering, föreslår det-ta papper först en datagenereringsmetod när det gäller mönsterkom-plexitet och skala. Baserat på dessa datasatser utbildas tio allmänt an-vända maskininlärningsalgoritmer, där parametrarna justeras för att uppnå hög noggrannhet. Testresultaten inklusive tre index inklusive träningstid, testtid och prediktionsnoggrannhet används för att utvär-dera algoritmerna.

Resultaten av testen visar att typiska maskininlärningsmetoder som Naive Bayes-klassificerare och SVM är bra nog med snabb träning med hög noggrannhet när de arbetar med data med enkel struktur och liten skala. När man hanterar komplexa data i stor skala, överträffar djupa inlärningsmetoder som CNN och DBN alla andra metoder.

Place, publisher, year, edition, pages
2018. , p. 54
National Category
Mechanical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-245221OAI: oai:DiVA.org:kth-245221DiVA, id: diva2:1294436
Supervisors
Examiners
Available from: 2019-03-07 Created: 2019-03-07 Last updated: 2019-03-07Bibliographically approved

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