Mining data streams to increase ‎industrial product availability

• Main Author: Alzghoul, Ahmad
• Format: Doctoral Thesis
• Language: English
• Published: Luleå tekniska universitet, Produkt- och produktionsutveckling 2013
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-17609; http://nbn-resolving.de/urn:isbn:978-91-7439-654-6; http://nbn-resolving.de/urn:isbn:978-91-7439-655-3 (PDF)

Improving product quality is always of industrial interest. Product availability, a function of product maintainability and reliability, is an example of a measurement that can be used to evaluate product quality. Product availability and cost are two units which are especially important to manage in the context of the manufacturing industry, especially where industry is interested in selling or buying offers with increased service content. Industry in general uses different strategies for increasing equipment availability; these include: corrective (immediate or delayed) and preventive strategies. Preventive strategies may be further subdivided into scheduled and predictive (condition-based) maintenance strategies. In turn, predictive maintenance may also be subdivided into scheduled inspection and continuously monitored. The predictive approach can be achieved by early fault detection. Fault detection and diagnosis methods can be classified into three categories: data-driven, analytically based, and knowledge-based methods. In this thesis, the focus is mainly on fault detection and on data-driven models.Furthermore, industry is generating an ever-increasing amount of data, which may eventually become impractical to store and search, and when the data rate is increasing, eventually impossible to store. The ever-increasing amount of data has prompted both industry and researchers to find systems and tools which can control the data on the fly, as close to real-time as possible, without the need to store the data itself. Approaches and tools such as Data Stream Mining (DSM) and Data Stream Management Systems (DSMS) become important. For the work reported in this thesis, DSMS and DSM have been used to control, manage and search data streams, with the purpose of supporting increased availability of industrial products.Bosch Rexroth Mellansel AB (formerly Hägglunds Drives AB) has been the industrial partner company during the course of the work reported in this thesis. Related data collection concerning the functionality of the BRMAB hydraulic system has been performed in collaboration with other researchers in Computer Aided Design at Luleå University of Technology.The research reported in this thesis started with a review of data stream mining algorithms and their applications in monitoring. Based on the review, a data stream classification method, i.e. Grid-based classifier, was proposed, tested and validated (Paper A). Also, a fault detection system based on DSM and DSMS was proposed and tested, as reported in Paper A. Thereafter, a data stream predictor was integrated into the proposed fault detection system to detect failures earlier, thus demonstrating how data stream prediction can be used to gain more time for proactive response actions by industry (Paper B). Further development included an automatic update method which allows the proposed fault detection system to be able to overcome the problem of concept drift (Paper E). The proposed and modified fault detection systems were tested and verified using data collected in collaboration with Bosch Rexroth Mellansel AB (BRMAB). The requirements for the proposed fault detection system and how it can be used in product development and design of the support system were also discussed (Paper C). In addition, the performance of a knowledge-based method and a data- driven method for detecting failures in high-volume data streams from industrial equipment have been compared (Paper D). It was found that both methods were able to detect all faults without any false alert. Finally, the possible implications of using cloud services for supporting industrial availability are discussed in Paper F. Further discussions regarding the research process and the relations between the appended papers can be found in Chapter 2, Figure 4 and in Chapter 5, Figure 21.The results showed that the proposed and modified fault detection systems achieved good performance in detecting and predicting failures on time (see Paper A and Paper B). In Paper C, it is shown how data stream management systems may be used to increase product availability awareness. Also, both the data-driven method and the knowledgebased method were suitable for searching data streams (see Paper D). Paper E shows how the challenge of concept drift, i.e. the situation in which the statistical properties of a data stream change over time, was turned to an advantage, since the authors were able to develop a method to automatically update the safe operation limits of the one-class data-driven models.In general, detecting faults and failures on time prevents unplanned stops and may improve both maintainability and reliability of industrial systems and, thus, their availability (since availability is a function of maintainability and reliability). By the results, this thesis demonstrates how DSM and DSMS technologies can be used to increase product availability and thereby increase product quality in terms of availability. === <p>Godkänd; 2013; 20130423 (ahmalz); Tillkännagivande disputation 2013-05-24 Nedanstående person kommer att disputera för avläggande av teknologie doktorsexamen. Namn: Ahmad Alzghoul Ämne: Datorstödd maskinkonstruktion/Computer Aided Design Avhandling: Mining Data Streams to Increase Industrial Product Availability Opponent: Professor Patrik Eklund, Institutionen för datavetenskap, Umeå universitet Ordförande: Professor Lennart Karlsson, Institutionen för teknikvetenskap och matematik, Luleå tekniska universitet Tid: Måndag den 17 juni 2013, kl 09.00 Plats: E231, Luleå tekniska universitet</p>