TY - THES

T1 - Condition monitoring of large-scale slew bearings in bucket-wheel boom-type reclaimers

AU - Rothschedl, Christopher Josef

N1 - embargoed until 23-06-2021

PY - 2016

Y1 - 2016

N2 - This thesis examines solutions for condition monitoring of slew bearings, which are main components of bucket-wheel boom-type reclaimers. A detailed overview of the function of this type of reclaimer is given and the characteristics of the slew bearings are described. A sample design calculation of a slew bearing is performed to illustrate influencing factors. Extensive studies on failure modes and their probable causes are discussed. Established as well as potential ways of monitoring the condition of slew bearings are outlined. These methods of monitoring are based solely on observing the effects of wear and damage on slew bearings. The concept of data mining is introduced to assess the causes of excessive wear and damage of slew bearings. Historical operational sensor data of reclaimers is analysed using physical models. These models correspond to inverse problems that are solved by using Linear Differential Operators and their inverses. The findings of these analyses are presented in this thesis. Finally, a framework for data mining is suggested, which can be used to describe mechanisms of collecting, storing, analysing, and evaluating sensor data.

AB - This thesis examines solutions for condition monitoring of slew bearings, which are main components of bucket-wheel boom-type reclaimers. A detailed overview of the function of this type of reclaimer is given and the characteristics of the slew bearings are described. A sample design calculation of a slew bearing is performed to illustrate influencing factors. Extensive studies on failure modes and their probable causes are discussed. Established as well as potential ways of monitoring the condition of slew bearings are outlined. These methods of monitoring are based solely on observing the effects of wear and damage on slew bearings. The concept of data mining is introduced to assess the causes of excessive wear and damage of slew bearings. Historical operational sensor data of reclaimers is analysed using physical models. These models correspond to inverse problems that are solved by using Linear Differential Operators and their inverses. The findings of these analyses are presented in this thesis. Finally, a framework for data mining is suggested, which can be used to describe mechanisms of collecting, storing, analysing, and evaluating sensor data.

KW - Data Mining

KW - Cyber-Physical Systems

KW - Condition Monitoring

KW - Zustandsüberwachung

KW - Zustandsorientierte Wartung

KW - Schwenklager

KW - Großwälzlager

KW - Rückladegerät

KW - Betriebsdaten

KW - Linear Differential Operators

KW - Datenanalyse

KW - Lexical Analysis

KW - data mining

KW - cyber-physical systems

KW - condition monitoring

KW - predictive maintenance

KW - slew bearing

KW - reclaimer

KW - operational data

KW - linear differential operators

KW - data analytics

KW - lexical analysis

M3 - Diploma Thesis

ER -