With the advent of Industry 4.0 and the Internet of Things, collecting data on Cyber-Physical systems has become the norm practice in large scale industries. By collectingrelevant data, it is possible to monitor the health status of whole systems or specificcomponents within them. Such practices allow for historical maintenance strategies suchas reactive maintenance or preventive maintenance to be phased out.In this thesis two separate algorithms are presented, both designed to identify contaminationlevels in the hydraulic pressure filters of forklifts. Furthermore, in contrast torelevant literature for similar applications only sensory data from the hydraulic pump’smotor current and hydraulic fluid pressure at the load was used. More specifically, theproposed algorithms are based on trends observed in the relationship between the measurementsand how it changes over time. The algorithms were evaluated on data fromfour forklifts used in Toyota’s factory. The forklifts had been collecting data while usedin production for over a year.The results indicate strong evidence that both algorithms can be used to detect degradationin the hydraulic system. This is especially true for one forklift where it was knownthat the damage at the time of replacement was substantial. However, it cannot be trulyestablished without further testing whether the algorithms detect degradation in the filteror pump.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-186470 |
| Date | January 2022 |
| Creators | Sehlstedt, Robert, Sellén, Erik |
| Publisher | Linköpings universitet, Institutionen för systemteknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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