Data-driven Models for Fault Classification and Prediction of Industrial Robots

Data-driven Models for Fault Classification and Prediction of Industrial Robots. Nentwich, C., Junker, S., & Reinhart, G. Procedia CIRP, 93:1055–1060, January, 2020.

Paper doi abstract bibtex

Economic data acquisition and storage have been key enablers to pave the way for data-driven predictions of machine downtimes. Regarding industrial robots, such predictions can maximize the robot’s availability and effective life span. This paper focuses on the comparison of different data-driven models for robot fault prediction and classification by applying them to a data set derived from a robot test bed and illuminates the data transformation process from raw sensor data to domain knowledge motivated robot health indicators.

@article{nentwich_data-driven_2020,
	series = {53rd {CIRP} {Conference} on {Manufacturing} {Systems} 2020},
	title = {Data-driven {Models} for {Fault} {Classification} and {Prediction} of {Industrial} {Robots}},
	volume = {93},
	issn = {2212-8271},
	url = {http://www.sciencedirect.com/science/article/pii/S2212827120307642},
	doi = {10.1016/j.procir.2020.04.126},
	abstract = {Economic data acquisition and storage have been key enablers to pave the way for data-driven predictions of machine downtimes. Regarding industrial robots, such predictions can maximize the robot’s availability and effective life span. This paper focuses on the comparison of different data-driven models for robot fault prediction and classification by applying them to a data set derived from a robot test bed and illuminates the data transformation process from raw sensor data to domain knowledge motivated robot health indicators.},
	language = {en},
	urldate = {2020-09-28},
	journal = {Procedia CIRP},
	author = {Nentwich, Corbinian and Junker, Sebastian and Reinhart, Gunther},
	month = jan,
	year = {2020},
	keywords = {Type your keywords here, separated by semicolons},
	pages = {1055--1060},
}

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