This article describes a method for compensating thermal effects on tiltmeter (TM) readings using operating temperature measurements. This practice is necessary when the contribution of thermal effects is comparable with the measurand inclination, which is typical of monitoring applications. In such contexts, several temperature-related phenomena take place concurrently and a customized compensation model is necessary. A tailored moving least square (MLS) approach is presented. This formulation is able to separate the contribution of the unknown measurand signal from the contribution of influence quantities, considered as exogenous inputs. Moreover, this method returns the model coefficients over time. However, the uncertainty in the temperature measurements, used as regressors, may induce bias in coefficient estimates. Therefore, a strategy for a posteriori evaluation and correction of the coefficients is proposed and validated with the Monte Carlo method (MCM). The effectiveness of this method is illustrated on a real case, consisting of the inclination monitoring of a wind-turbine tower.
Compensation of Thermal Effects on Tiltmeter Measurements With Moving Least Squares / Battista, G.; Pavoni, S.; Vanali, M.. - In: IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT. - ISSN 0018-9456. - 73:(2024), pp. 7501914.1-7501914.14. [10.1109/TIM.2024.3369139]
Compensation of Thermal Effects on Tiltmeter Measurements With Moving Least Squares
Battista G.
;Pavoni S.;Vanali M.
2024-01-01
Abstract
This article describes a method for compensating thermal effects on tiltmeter (TM) readings using operating temperature measurements. This practice is necessary when the contribution of thermal effects is comparable with the measurand inclination, which is typical of monitoring applications. In such contexts, several temperature-related phenomena take place concurrently and a customized compensation model is necessary. A tailored moving least square (MLS) approach is presented. This formulation is able to separate the contribution of the unknown measurand signal from the contribution of influence quantities, considered as exogenous inputs. Moreover, this method returns the model coefficients over time. However, the uncertainty in the temperature measurements, used as regressors, may induce bias in coefficient estimates. Therefore, a strategy for a posteriori evaluation and correction of the coefficients is proposed and validated with the Monte Carlo method (MCM). The effectiveness of this method is illustrated on a real case, consisting of the inclination monitoring of a wind-turbine tower.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.