Stochastic filtering approach for condition-based maintenance considering sensor degradation

This paper proposes a condition-based maintenance (CBM) policy for a deteriorating system whose state is monitored by a degraded sensor. In the literature of CBM, it is commonly assumed that inspection of system state is perfect or subject to measurement error. The health condition of the sensor, which is dedicated to inspect the system state, is completely ignored during system operation. However, due to the varying operation environment and aging effect, the sensor itself will suffer a degradation process and its performance deteriorates with time. In the presence of sensor degradation, the Kalman filter is employed in this paper to progressively estimate the system and the sensor state. Since the estimation of system state is subject to uncertainty, maintenance solely based on the estimated state will lead to a suboptimal solution. Instead, predictive reliability is used as a criterion for maintenance decision-making, which is able to incorporate the effect of estimation uncertainty. Preventive replacement is implemented when the estimated system reliability at inspection hits a specific threshold, which is obtained by minimizing the long-run maintenance cost rate. An example of wastewater treatment plant is used to illustrate the effectiveness of the proposed maintenance policy. It can be concluded through our research that: 1) disregarding the sensor degradation while it exists will significantly increase the maintenance cost and 2) the negative impact of sensor degradation can be diminished via proper inspection and filtering methods

Model-based quality assessment of tower-based field spectroscopy measurements

Recent and upcoming satellite missions providing high-quality spectrometric measurements are used for vegetation monitoring and studies of ecosystem functioning which are becoming increasingly important in the context of climate change. The calibration and validation of these measurements are crucial but remain a challenge. The need for in-situ references is high and is expected to increase with the trend toward mini-satellites without onboard calibration systems. In-situ measurements however need to be validated themselves before being used as a reference for air- or space-borne sensors. Crossvalidation of measurements with additional independent measurements is established but costly. Three approaches using two Radiative Transfer Models (RTM) namely the library for Radiative transfer (libRadtran) and the Soil Canopy Observation of Photosynthesis and Energy Fluxes Model (SCOPE) were built to validate in-situ irradiance and radiance measurements based on simulations. The performance of the approaches was assessed from summer to late autumn and over a single clear-sky day resulting in an average Root Mean Square Relative Error (RMSRE) of below 10% for irradiance simulations and 10%-38% RMSRE for radiance simulations compared to in-situ measurements. The higher RMSRE of radiance simulations originates in misspecifications of the reflectance spectrum which is either assumed constant (approach 1) or modelled (approach 2 & 3) based on vegetation parameters. The vegetation parameters however are themselves subject to large uncertainty. Shadowing on the vegetation canopy can additionally lead to ill-posed vegetation parameter selection. The experiments show the potential of coupled RTM-based quality assessment of high-frequency field measurements but also indicate the need for more accurate vegetation canopy parameter estimates and a more sophisticated optimization process to avoid the effects of ill-posedness