Comparative assessment of modified deconvolution and neuro-fuzzy technique for force prediction using an accelerometer balance system

Deka, Sushmita; Pallekonda, Ramesh Babu; Rahang, Maneswar

doi:10.1016/j.measurement.2020.108770

Cited by 3 publications

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the calibration methods of the three-axis accelerometer, currently, there is no statistically unified standard; the most popular calibration method is a back-to-back comparison calibration method. 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

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Kong

2021

View full text Add to dashboard Cite

show abstract

Dynamic Calibration of a Stress-Wave Force Balance Using Hybrid Soft Computing Approach

Nayak

Sahoo

2022

Nonlinear Dynamics and Applications

View full text Add to dashboard Cite

Transfer learning: A new aerodynamic force identification network based on adaptive EMD and soft thresholding in hypersonic wind tunnel

Sun

Gao

et al. 2023

Chinese Journal of Aeronautics

View full text Add to dashboard Cite

Comparative assessment of modified deconvolution and neuro-fuzzy technique for force prediction using an accelerometer balance system

Cited by 3 publications

References 22 publications

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

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

Dynamic Calibration of a Stress-Wave Force Balance Using Hybrid Soft Computing Approach

Transfer learning: A new aerodynamic force identification network based on adaptive EMD and soft thresholding in hypersonic wind tunnel

Contact Info

Product

Resources

About