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Improving availability of industrial products through data stream mining
Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik, Produkt- och produktionsutveckling.
2011 (Engelska)Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

Products of high quality are of great interest for industrial companies. The quality of a product can be considered in terms of production cost, operating cost, safety and product availability, for example. Product availability is a function of maintainability and reliability. Monitoring prevents unplanned stops, thus increasing product availability by decreasing needed maintenance. Through monitoring, failures can be detected and/or avoided. Detecting failures eliminates extra costs such as costs associated with machinery damage and dissatisfied customers, and time is saved since stops can be scheduled, instead of having unplanned stops. Product monitoring can be done through searching the data generated from sensors installed on products.Nowadays, the data can be collected at high rates as part of a data stream. Therefore, data stream management systems (DSMS) and data stream mining (DSM) are being used to control, manage and search the data stream. This work investigated how the availability of industrial products can be increased through the use of DSM and DSMS technologies.A review of the data stream mining algorithms and their applications in monitoring was conducted. Based on the review, a new data stream classification method, i.e. Grid-based classifier was proposed, tested and validated. Also, a fault detection system based on DSM and DSMS technologies was proposed. The proposed fault detection system was tested using data collected from Hägglunds Drives AB (HDAB) hydraulic motors. Thereafter, a data stream predictor was integrated into the proposed fault detection system to detect failures earlier, thus gaining more time for response actions. The modified fault detection system was tested and showed good performance. The results showed that the proposed fault detection system, which is based on DSM and DSMS technologies, achieved good performance (with classification accuracy around 95%) in detecting failures on time. Detecting failures on time prevents unplanned stops and may improve the maintainability of the industrial systems and, thus, their availability.

Ort, förlag, år, upplaga, sidor
Luleå: Luleå tekniska universitet, 2011. , s. 92
Serie
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
Nationell ämneskategori
Annan maskinteknik
Forskningsämne
Datorstödd maskinkonstruktion
Identifikatorer
URN: urn:nbn:se:ltu:diva-25914Lokalt ID: baf68965-89d6-4482-a3e5-67e6a65aaf0aISBN: 978-91-7439-332-3 (tryckt)OAI: oai:DiVA.org:ltu-25914DiVA, id: diva2:999072
Anmärkning
Godkänd; 2011; 20111025 (ahmalz); LICENTIATSEMINARIUM Ämnesområde: Datorstödd maskinkonstruktion/Computer Aided Design Examinator: Professor Lennart Karlsson, Institutionen för teknikvetenskap och matematik, Luleå tekniska universitet Diskutant: Doctor Erik Zeitler, Department of Information Technology, Uppsala University Tid: Måndag den 19 december 2011 kl 09.00 Plats: E231, Luleå tekniska universitetTillgänglig från: 2016-09-30 Skapad: 2016-09-30 Senast uppdaterad: 2017-11-24Bibliografiskt granskad

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