A Low-Power Low-Noise Monolithic Accelerometer with Automatic Sensor Offset Calibration

Yeh, Chih Yuan; Huang, Jung Tang; Tseng, Sheng-Hsiang; Wu, Po-Chang; Tsai, Hann-Huei; Juang, Ying‐Zong

doi:10.1016/j.mejo.2020.104910

Cited by 4 publications

(1 citation statement)

References 15 publications

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“…The standard accelerometer and the three-axis accelerometer are installed using the back-to-back method and the acceleration, which is measured by the standard accelerometer, is applied as the input of the three-axis accelerometer (Deka et al, 2021;Forootan et al, 2021;Zhang et al, 2021). This method has the advantages of having a simple principle, convenient operation and low-cost fabrication, which have made the method increasingly acceptable as an important solution to calibrate three-axis accelerometers (Yu et al, 2020;Yeh et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Sensitivity calibration of a three-axis accelerometer under different temperature conditions using the hybrid GA–PSO–BPNN algorithm

Kong

2021

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Purpose Three-axis accelerometers play a vital role in monitoring the vibrations in aircraft machinery, especially in variable flight temperature environments. The sensitivity of a three-axis accelerometer under different temperature conditions needs to be calibrated before the flight test. Hence, the authors investigated the efficiency and sensitivity calibration of three-axis accelerometers under different conditions. This paper aims to propose the novel calibration algorithm for the three-axis accelerometers or the similar accelerometers. Design/methodology/approach The authors propose a hybrid genetic algorithm–particle swarm optimisation–back-propagation neural network (GA–PSO–BPNN) algorithm. This method has high global search ability, fast convergence speed and strong non-linear fitting capability; it follows the rules of natural selection and survival of the fittest. The authors describe the experimental setup for the calibration of the three-axis accelerometer using a three-comprehensive electrodynamic vibration test box, which provides different temperatures. Furthermore, to evaluate the performance of the hybrid GA–PSO–BPNN algorithm for sensitivity calibration, the authors performed a detailed comparative experimental analysis of the BPNN, GA–BPNN, PSO–BPNN and GA–PSO–BPNN algorithms under different temperatures (−55, 0 , 25 and 70 °C). Findings It has been showed that the prediction error of three-axis accelerometer under the hybrid GA–PSO–BPNN algorithm is the least (approximately ±0.1), which proved that the proposed GA–PSO–BPNN algorithm performed well on the sensitivity calibration of the three-axis accelerometer under different temperatures conditions. Originality/value The designed GA–PSO–BPNN algorithm with high global search ability, fast convergence speed and strong non-linear fitting capability has been proposed to decrease the sensitivity calibration error of three-axis accelerometer, and the hybrid algorithm could reach the global optimal solution rapidly and accurately.

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Section: Introductionmentioning

confidence: 99%