MEASURING THE SIX dof DRIVING POINT IMPEDANCE FUNCTION AND AN APPLICATION TO RB INERTIA PROPERTY ESTIMATION

Witter, M. C.; Brown, David L.; Blough, Jason R.

doi:10.1006/mssp.1999.1263

Cited by 4 publications

(3 citation statements)

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“…The accuracy of the above mentioned algorithms has already been verified for simple structures whose RB inertia properties are known 10,14–17 . Moreover, in ref.…”

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confidence: 85%

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Determining the Rigid-Body Inertia Properties of Cumbersome Systems: Comparison of Techniques in Time and Frequency Domain

Mucchi

Fiorati

Gregorio

et al. 2009

Experimental Techniques

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“…The accuracy of the above mentioned algorithms has already been verified for simple structures whose RB inertia properties are known 10,14–17 . Moreover, in ref.…”

mentioning

confidence: 85%

“…The IRM 1,2,14–17 has been used for various specimen types, which range from simple‐geometry types (e.g., beams, plates, simple structures) to complex‐geometry types 18 (e.g., exhaust system). It uses the mass line of the measured FRFs.…”

mentioning

confidence: 99%

Determining the Rigid-Body Inertia Properties of Cumbersome Systems: Comparison of Techniques in Time and Frequency Domain

Mucchi

Fiorati

Gregorio

et al. 2009

Experimental Techniques

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“…The mass line method needs "soft" suspension to get clear mass lines but the modal property method needs a little stiffer suspension (compared to the suspension for the mass line method) to get separated individual rigid body modes. The mass line method has been widely studied [40][41][42][43][44] and successfully integrated into some commercial software, such as the "Rigid Body Calculator" in LMS Test.Lab. Based on the selected section of a mass line, the inertia properties can be solved from an over-determined set of equations by the leastsquares method.…”

Section: Inertial Properties and Rigid Body Mode Testsmentioning

confidence: 99%

Improved automated diagnosis of misfire in internal combustion engines based on simulation models

Chen

Randall

2015

Mechanical Systems and Signal Processing

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In this paper, a new advance in the application of Artificial Neural Networks (ANNs) to the automated diagnosis of misfires in Internal Combustion engines(IC engines) is detailed. The automated diagnostic system comprises three stages: fault detection, fault localization and fault severity identification. Particularly, in the severity identification stage, separate Multi-Layer Perceptron networks (MLPs) with saturating linear transfer functions were designed for individual speed conditions, so they could achieve finer classification. In order to obtain sufficient data for the network training, numerical simulation was used to simulate different ranges of misfires in the engine. The simulation models need to be updated and evaluated using experimental data, so a series of experiments were first carried out on the engine test rig to capture the vibration signals for both normal condition and with a range of misfires. Two methods were used for the misfire diagnosis: one is based on the torsional vibration signals of the crankshaft and the other on the angular acceleration signals (rotational motion) of the engine block. Following the signal processing of the experimental and simulation signals, the best features were selected as the inputs to ANN networks. The ANN systems were trained using only the simulated data and tested using real experimental cases, indicating that the simulation model can be used for a wider range of faults for which it can still be considered valid. The final results have shown that the diagnostic system based on simulation can efficiently diagnose misfire, including location and severity.

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Sharp Inequalities for Driving Point Impedance Functions

Azeroğlu¹,

Örnek

Düzenli̇

2023

IEEE Trans. Circuits Syst. II

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MEASURING THE SIX dof DRIVING POINT IMPEDANCE FUNCTION AND AN APPLICATION TO RB INERTIA PROPERTY ESTIMATION

Cited by 4 publications

References 1 publication

Determining the Rigid-Body Inertia Properties of Cumbersome Systems: Comparison of Techniques in Time and Frequency Domain

Determining the Rigid-Body Inertia Properties of Cumbersome Systems: Comparison of Techniques in Time and Frequency Domain

Improved automated diagnosis of misfire in internal combustion engines based on simulation models

Sharp Inequalities for Driving Point Impedance Functions

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