Remote data collection for condition monitoring in machine building companies
Kallio, Topias (2021)
Kallio, Topias
2021
Konetekniikan DI-ohjelma - Master's Programme in Mechanical Engineering
Tekniikan ja luonnontieteiden tiedekunta - Faculty of Engineering and Natural Sciences
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Hyväksymispäivämäärä
2021-05-24
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202104273859
https://urn.fi/URN:NBN:fi:tuni-202104273859
Tiivistelmä
The amount of generated data in manufacturing industry is booming. This is a result of the advances in information and communication technologies, broadly referred as Industrial Internet of Things (IIoT). At the same time, small- and medium-sized enterprises (SMEs) are finding ways to remotely collect and use machine related data to enhance their business and enable servitization possibilities. One promising application for the remotely collected data is condition monitoring of machines, which results better maintenance decisions and higher availability. Even though current technologies make remote data collection possible and allow SMEs to gain value from data, the lack of standardized solutions make the implementation complex and challenging.
This thesis is focused study how remote data collection for condition monitoring could be implemented and developed. To support that intention, another objective is to identify reasons for pursuing them as well as challenges that SMEs may have. Study is limited to machine build-ing SMEs with interest in remote data collection and condition monitoring.
Mentioned issues were researched by conducting a qualitative, multiple-case study that consisted of both empirical research and theoretical part with literature review. To form an over-view of the topic and support empirical research, literature review was conducted first. After that, empirical research was done with four machine building SMEs and one large automation company as case companies. Qualitative data was collected from these companies through meetings and semi-structured interviews.
Whereas previous literature emphasized preparatory activities for determining collected data and technologies such as specific sensors, study showed that many machine building SMEs approach collection with more of an experimentation mindset. Due to the limited resources, these companies aim to maximize the use of existing technologies and data sources, such as process related sensors and machine automation system. Except for cloud services that make remote access and data management possible, amount of additional technologies such as external IIoT collection devices is kept minimal to save limited resources and ensure cost efficiency. This is seen to be possible in the case companies, as they have existing data sources in their machines. One of the studied companies already does remote data collection for condition monitoring, but it should be noticed that amount of data sources in their machines is big.
Research also showed many reasons why machine building SMEs are willing to collect data remotely and monitor condition of their machines. For example, with remote diagnostics with data, companies could achieve more efficient maintenance and raise the availability of their machines. At the same time, limited resources could be saved as the need for maintenance re-lated traveling reduces. However, achieving the benefits require a lot of high-quality data. That in turn requires careful consideration about relevant data, its properties and technologies that enable its collection. That was found to be challenging in SMEs. In addition, many other challenges to be considered were recognized. IIoT is seen to be complex and selection of proper solutions difficult, because there is lot of offering with marketing involved. Therefore, machine building SMEs should think carefully about their objectives and based on them, limit remote data collection and condition monitoring to the most potential subjects.
This thesis is focused study how remote data collection for condition monitoring could be implemented and developed. To support that intention, another objective is to identify reasons for pursuing them as well as challenges that SMEs may have. Study is limited to machine build-ing SMEs with interest in remote data collection and condition monitoring.
Mentioned issues were researched by conducting a qualitative, multiple-case study that consisted of both empirical research and theoretical part with literature review. To form an over-view of the topic and support empirical research, literature review was conducted first. After that, empirical research was done with four machine building SMEs and one large automation company as case companies. Qualitative data was collected from these companies through meetings and semi-structured interviews.
Whereas previous literature emphasized preparatory activities for determining collected data and technologies such as specific sensors, study showed that many machine building SMEs approach collection with more of an experimentation mindset. Due to the limited resources, these companies aim to maximize the use of existing technologies and data sources, such as process related sensors and machine automation system. Except for cloud services that make remote access and data management possible, amount of additional technologies such as external IIoT collection devices is kept minimal to save limited resources and ensure cost efficiency. This is seen to be possible in the case companies, as they have existing data sources in their machines. One of the studied companies already does remote data collection for condition monitoring, but it should be noticed that amount of data sources in their machines is big.
Research also showed many reasons why machine building SMEs are willing to collect data remotely and monitor condition of their machines. For example, with remote diagnostics with data, companies could achieve more efficient maintenance and raise the availability of their machines. At the same time, limited resources could be saved as the need for maintenance re-lated traveling reduces. However, achieving the benefits require a lot of high-quality data. That in turn requires careful consideration about relevant data, its properties and technologies that enable its collection. That was found to be challenging in SMEs. In addition, many other challenges to be considered were recognized. IIoT is seen to be complex and selection of proper solutions difficult, because there is lot of offering with marketing involved. Therefore, machine building SMEs should think carefully about their objectives and based on them, limit remote data collection and condition monitoring to the most potential subjects.