Abstract
Digital twins are meant to revolutionize the manufacturing industry by enabling advanced condition monitoring and predictive maintenance processes. However, disruptions within the manufacturing process, such as sensor malfunctions or connectivity issues are inevitable and will cripple these advanced analysis methods if not properly addressed. Therefore, efficient data management and analysis practices are key to advancing this technology. This work examines the impact of missing data imputation on model parameter estimation, a crucial task in developing models for digital twins. We theoretically derive the Cramer-Rao Lower Bound (CRLB) for a DC signal with an unknown scalar parameter in the presence of missing data, analyzing the impact of data imputation techniques. Moreover, we experimentally evaluate the performance of two imputation methods on parameter estimation in a Mass-Spring-Damper model, finding up to a 78% decrease in estimation errors when data is missing completely at random.
Recommended Citation
S. Thompson et al., "Enhancing Measurement Accuracy in Industrial Applications: The Impact of Sensor Data Imputation on Model Parameter Estimation," Conference Record IEEE Instrumentation and Measurement Technology Conference, Institute of Electrical and Electronics Engineers, Jan 2025.
The definitive version is available at https://doi.org/10.1109/I2MTC62753.2025.11078961
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
data management; data processing; sensor data imputation
International Standard Serial Number (ISSN)
1091-5281
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2025
Comments
Boeing, Grant None