Intelligent fuzzy weighted input estimation method for the forces generated by an operating rotating machine

Lee, Ming‐Hui; Chen, Tsung-Chien

doi:10.1016/j.measurement.2011.02.013

Cited by 13 publications

(6 citation statements)

References 16 publications

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“…Similarly to artificial neural networks, fuzzy logic can be used for force identification [63]. With its help, it is possible to formally define imprecise and multi-meaning terms such as large force or small force which allows for easy generalization and incorporation of expert knowledge into the system .…”

Section: Force Identification With Use Of Fuzzy Logicmentioning

confidence: 99%

A review of methods for excitation force reconstruction

Mendrok¹,

Dworakowski²

2019

Diagnostyka

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In many mechanical systems research scenarios, including in particular control and monitoring of mechanical devices, reconstruction of external loads acting on a system is a matter of great importance. The paper provides taxonomy and review of available dynamic load identification methods in cases when a direct load applied to the structure is required to be reconstructed. Methods based on frequency and time domains as well as those relying on statistical and soft computing approaches are described. The authors present methods in possibly broad manner, including initial assumptions, workflow and some of the equations. For that reason, reader is able to initially assess methods' potential in particular applications and choose the method which best fit his needs.

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Section: Force Identification With Use Of Fuzzy Logicmentioning

confidence: 99%

A review of methods for excitation force reconstruction

Mendrok¹,

Dworakowski²

2019

Diagnostyka

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“…Chen et al firstly proposed the intelligent fuzzy weighted input estimation in 2007 and this method has been widely applied to estimate the input load on a beam structural system, vibration forces of a rotating machine and the unknown time-varying heat flux in real-time in recent years [19][20][21][22][23][24]. These studies had shown FWIE has the stability on estimation.…”

Section: Fuzzy Weighted Input Estimation (Fwie)mentioning

confidence: 99%

“…The proposed method could track the input signal rapidly and restrain the noise disturbance and improve the performance of estimator. The study had a good performance in heat transfer, beam and mechanical structure [19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

The Study to Apply Fuzzy Weighted Input Estimation for the Prediction of Target Trajectory in a Fire Control System

Chen¹,

Lee

Chen

2017

Transactions of the Canadian Society for Mechanical Engineering

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The fuzzy weighted input estimation (FWIE) is proposed in this paper to solve the problem of noise disturbance and combined with the three-dimensional motion equation of target trajectory to construct the tracking rule of fire control system. FWIE can estimate effectively the input data of maneuvering target acceleration to obtain the precise target state and solve the problems from the traditional Kalman filter which cannot compute the precise estimation of target state because of the input information in the system. Simulation results show that FWIE can estimate the change of target state rapidly and precisely compared with the extended Kalman filter and the proposed tracking rule can improve the fire control system to figure out the target intercepting points with shorter miss distance.

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“…The use of modal expansion causes errors in estimated fault parameters when limited responses are available (Sudhakar and Sekhar, 2011). In Shrivastava and Mohanty (2018), authors of the present article proposed an identification algorithm where forces are identified using a Kalman filter–based input estimation technique (Lee and Chen, 2011). This technique was originally proposed for inverse heat conduction problems (Tuan et al, 1996).…”

Section: Introductionmentioning

confidence: 99%

Identification of unbalance in a rotor-bearing system using Kalman filter–based input estimation technique

Shrivastava

Mohanty

2019

Journal of Vibration and Control

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Unbalance is a common machinery fault, which occurs because of uneven distribution of mass of a rotating component about its axis. Vibration-based monitoring techniques have been widely accepted for machinery fault diagnosis. This article presents experimental verification of a recently developed Kalman filter–based method for the identification of unbalance in rotor systems. The method is tested on an experimental test rig for different unbalance configurations and shaft speeds. The proposed technique is a model-based method, which requires a mathematical model of the rotor system along with response measurements. A rigid rotor model is used, and measured accelerations at bearing pedestals are used for unbalance parameter estimation. Bearing stiffnesses are estimated using a frequency domain parameter estimation technique with measured unbalance responses. Sensitivity analysis is also performed by altering the values of these estimated stiffnesses.

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Intelligent fuzzy weighted input estimation method for the forces generated by an operating rotating machine

Cited by 13 publications

References 16 publications

A review of methods for excitation force reconstruction

A review of methods for excitation force reconstruction

The Study to Apply Fuzzy Weighted Input Estimation for the Prediction of Target Trajectory in a Fire Control System

Identification of unbalance in a rotor-bearing system using Kalman filter–based input estimation technique

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