Use Of MEMS Accelerometers Research Articles

MEMS accelerometers for mechanical vibrations analysis: a comprehensive review with applications

In this paper, the use of MEMS accelerometers for measuring mechanical vibrations is presented. Also a wide review of the literature is performed by presenting the uses of the MEMS accelerometers in a great number of applications. These sensors are known for their low prices, low power consumption and low sizes, which enhance their use in applications such as energy harvesters, monitoring processes and for educational purposes. In order to propose these sensors for measuring vibrations, a complete evaluation of the MEMS accelerometers was performed by measuring amplitudes and frequencies of oscillations and comparing their dynamic characteristics with other accelerometers with higher precision. Moreover, two experiments were conducted: In the first one, the measurements of the amplitude given by a MEMS and a standard accelerometer while being excited with sinusoidal waves with different frequencies using a vibration exciter were taken and compared. For the second experiment, three MEMS sensors and a piezoelectric accelerometer were used to measure the accelerations of a 3-DOF shear-building excited by an unbalanced DC motor. The signals obtained were compared in the time and frequency domains; for the last case, the wavelet transform, the wavelet coherence and the power spectrum density were used.

Acceleration Measurement Drift Rejection in Motion Control Systems by Augmented-State Kinematic Kalman Filter

This paper deals with the use of MEMS accelerometers to improve the performances of positioning control systems equipped with low-resolution positioning sensors. A kinematic Kalman filter (KKF) is used to combine the position and acceleration measurements and get a smooth estimate of the kinematic variables, even in the presence of a coarse position quantization. Compared to similar schemes existing in literature, the state of the proposed KKF is augmented, to include the accelerometer output bias/drift among the variables estimated by the filter. In this way, the intrinsic robustness of the KKF scheme is further improved, by making the estimation process of the kinematic variables practically insensitive to the variation of the sensor bias/drift. The proposed KKF is used to provide a smooth and robust estimate of the kinematic variables to a positioning control system consisting of a two degrees-of-freedom (DOF) proportional–derivative (PD) position control combined with an acceleration-based disturbance observer (ADOB). Compared to a solution based on a conventional KKF, not accounting for the accelerometer output disturbance, the proposed solution exhibits better positioning performances, and insensitivity to the accelerometer output bias/drift. This feature is validated through several experimental tests on a positioning system based on a linear motor.