Calibration Approach to Quantify Nonlinearity of MEMS Pore Pressure Sensors Using Optimal Interpolation

Abstract MEMS-based instruments have become more attractive in recent years for many industries, particularly geotechnical monitoring owing to their small size and low capital cost. However, overcoming nonlinearity errors is a major concern to ensure accuracy, precision, and repeatability of measurement. Nonlinearity error in measuring instruments can be solved using polynomial function of different degree based on severity of error. In this study, Lagrange polynomial fitting method is applied for nonlinearity calibration of a newly developed MEMS pore pressure sensor by means of optimum calibration points. A procedure for optimum selection of the calibration points to get the best calibration characteristics of a pore pressure sensor is investigated. For this work, the calibration characteristics are evaluated by Lagrange interpolation using special set of Chebyshev nodes, D, A and R-optimum points. The D-A-R optimum points are constructed by Imperialist Competitive Algorithm (ICA). The value of the optimal approach is also compared with a uniform approach using equidistant points through actual readings. The results show the increased accuracy and precision of measurement using optimum approach. This increased accuracy allows the application of MEMs to sense smaller changes in pore pressure readings providing unique opportunity for passive estimation of subsurface properties.