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Industrial Data Analysis and Prediction Modeling
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- Author / Creator
- Araya, Ruben
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Multiple studies have reported results of activities focused on development of different algorithms for prognosis of failures of apparatuses and machines. Failure prediction allows to schedule maintenance activities, increase productivity, and decrease inventory of spare parts. Construction of prediction models requires multiple procedures and thorough analysis of available data. For that, processes of Data Mining and Machine Learning can be applied. Data Mining provides necessary tools for storing, refining data, and analyzing data. Machine learning, on the other hand, includes a variety of approaches and techniques for finding patterns in data and building data models.
This study addresses data-driven analysis of two industrial problems. In the case of the first one, we analyze a degree of “weariness” of suspensions in haul trucks. Here, we determine usage of a suspension via generating and integrating features representing struts’ pressures. The second problem concerns prediction of outages in power systems. Feature selection is performed, and different prediction models are built. Additionally, we look at graph-based data representation and visualization of data with Neo4j database. -
- Subjects / Keywords
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- Graduation date
- Fall 2018
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- Type of Item
- Thesis
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- Degree
- Master of Science
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- License
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