Estimation of local failure in tensegrity using Interacting Particle-Ensemble Kalman Filter

Aswal, Neha; Sen, Subhamoy; Mevel, Laurent

doi:10.1016/j.ymssp.2021.107824

Cited by 12 publications

(16 citation statements)

References 63 publications

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“…m Φ T B, Z is the modal damping matrix. From equation (5), we can obtain the transfer function of the ith mode by applying the Laplace Transform where b mqi is the ith row of B m , C mqi is the ith columns of C mq , ζ i is modal damping of the ith mode.…”

Section: Task and Modal Space Second-order Structural Modelmentioning

confidence: 99%

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A modal self-excitation method of tensegrity robots

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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A tensegrity robot is a type of soft-rigid system, whose high compliance is prone to induce structural vibrations which traditionally should be avoided. However, a stable limit cycle oscillation at a natural frequency can be exploited to improve motion efficiency of tensegrity robots. By constructing a modal self-excitation system, we can produce limit cycle oscillation for tensegrity robots. Therefore, in this paper, we propose a method to realize modal self-excitation of underactuated tensegrity robots. A task-space second-order structural model of a tensegrity robot is developed and a mapping between forces of cable-space and that of task-space is derived. Meanwhile, we propose a cable sensitivity vector from modal shape to guide the selection of active cables in the tensegrity robot. A modal self-excitation system is designed by combining a bandpass filter with a describing function into the control system. In this way, a limit cycle oscillation at a frequency can be formed close to the desired natural frequency. The stability of this limit cycle oscillation is analyzed by perturbation method. Modal switching excitation is also achieved by switching between different modal excitation loops online. The effectiveness of the proposed modal self-excitation method for tensegrity robots is verified by several numerical simulations and tests.

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Section: Task and Modal Space Second-order Structural Modelmentioning

confidence: 99%

“…Tensegrity structures are composed of compressive and tensile members. [1][2][3][4][5] Since these structures can be deployable, resilient, and lightweight, they can be used in robotic platforms. [6][7][8][9][10][11] Tensegrity robots usually have redundant degrees of freedom and high compliance.…”

Section: Introductionmentioning

confidence: 99%

A modal self-excitation method of tensegrity robots

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…The main concept of PF is to approximate the probability density function (PDF) of the system random variables by discrete random sampling points, and replace the integral operation with the average value of sample to obtain the minimum variance estimation of the state. Based on Bayesian theory and sequential importance sampling (SIS) algorithm, particle filter shows significant advantages in model parameter estimation of nonlinear and non-Gaussian systems, and has been widely used in the field of life prediction, such as residual life prediction of lithium battery, 17,18 crack propagation prediction of planetary gear system, 19 health monitoring of tensegrity, 20 etc.…”

Section: Estimation Of Rolling Bearing Rul Based On Particle Filter A...mentioning

confidence: 99%

Convolution neural network based particle filtering for remaining useful life prediction of rolling bearing

Liu

Guo

Cheng

et al. 2022

Advances in Mechanical Engineering

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Aiming at the problem of remaining useful life prediction of rolling bearing in aero engine, a data-driven prediction method based on deep learning and particle filter is proposed. Initially, only the vibration data of rolling bearing in normal stage are trained by the deep convolution neural network. According to the feature distance between normal and degraded samples, the evolution features during the whole lifetime are extracted adaptively, and the health index of rolling bearing is constructed. Then, the alarm and failure threshold are determined by unsupervised clustering algorithm. Combined with the extracted feature, remaining useful life of rolling bearing is tracked and predicted by particle filter algorithm based on four parameter exponential model. Finally, the effectiveness of the proposed method is verified by three groups of whole lifetime test data of rolling bearings. Results show that the degradation feature extracted by deep learning method has higher prediction accuracy of 2.19%, 0.93%, and 1.43% respectively than RMS values, and has more stable performance and less influenced by the number of particles or resampling methods, which can better reflect the evolution trend of rolling bearing than the traditional feature.

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“…Tensegrity members undergo large deformation under external loading, which can be modelled by introducing geometric nonlinearity to the finite element modeling (FEM). In this article an explicit representation of the strain-displacement relationship is adopted following [4], which gives the locally linearized tangent stiffness matrix, K m (t) as follows,…”

Section: System Modelmentioning

confidence: 99%

“…Interacting filtering strategies, like interacting particle Kalman filter (IPKF), have been a breakthrough in this endeavor in which PF estimates the parameters while KF, nestled inside the PF, estimates the state variables [2,3]. However, since both the process and measurement models for tensegrity are nonlinear, KF needs to be replaced by EnKF [4] for SHM of tensegrity.…”

Section: Introductionmentioning

confidence: 99%