Model based on the reinitialised partial moments for initialising output-error identification methods

Model-based methods can be applied to identify the most likely faults that cause the experimental response of a rotating machine. Sometimes, the objective function, to be minimized in the fault identification method, shows multiple sufficiently low values that are associated with different sets of the equivalent excitations by means of which the fault can be modeled. In these cases, the knowledge of the contribution of each normal mode of interest to the vibration predicted at each measurement point can provide useful information to identify the actual fault. In this paper, the capabilities of an original diagnostic strategy that combines the use of common fault identification methods with innovative techniques based on a modal representation of the dynamic behavior of rotating machines is shown. This investigation approach has been successfully validated by means of the analysis of the abnormal vibrations of a large power unit.

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Two Identification Methods for Dual-Rate Sampled-Data Nonlinear Output-Error Systems

Mathematical Problems in Engineering ◽

10.1155/2014/329437 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Jing Chen ◽

Ruifeng Ding

Keyword(s):

Stochastic Gradient ◽

The Other ◽

Identification Algorithm ◽

Sampled Data ◽

Unknown Parameters ◽

Auxiliary Model ◽

Identification Methods ◽

Numerical Example ◽

Output Error ◽

Polynomial Transformation

This paper presents two methods for dual-rate sampled-data nonlinear output-error systems. One method is the missing output estimation based stochastic gradient identification algorithm and the other method is the auxiliary model based stochastic gradient identification algorithm. Different from the polynomial transformation based identification methods, the two methods in this paper can estimate the unknown parameters directly. A numerical example is provided to confirm the effectiveness of the proposed methods.

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Identification of a Generator Fault by Model-Based Diagnostic Techniques

International Journal of Rotating Machinery ◽

10.1155/s1023621x04000302 ◽

2004 ◽

Vol 10 (4) ◽

pp. 293-300 ◽

Cited By ~ 3

Author(s):

P. Pennacchi ◽

A. Vania

Keyword(s):

Experimental Data ◽

Power Plant ◽

Diagnostic Techniques ◽

Rotating Machines ◽

Identification Methods ◽

Model Based

Model-based diagnostic techniques can be used successfully in the health analysis of rotormachinery. Unfortunately, a poor accuracy of the model of the fully assembled machine, as well as noise in the signals and errors in the evaluation of the experimental vibrations that are caused only by the impending fault, can affect the accuracy of the fault identifications. This can make it difficult to identify the type of actual fault as well as to evaluate with care its severity and position. This article shows some techniques that have been developed by the authors to measure the accuracy of the results obtained with model-based identification methods aimed to diagnose faults in rotating machines. In this article, the results obtained by means of the analysis of experimental data collected in a power plant are described. Finally, the capabilities of the developed methods are shown and discussed.

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Adaptive IIR Filtering and Output Error Identification: Robustness Analysis

1992 American Control Conference ◽

10.23919/acc.1992.4792350 ◽

1992 ◽

Author(s):

Sanjeev M. Naik ◽

P. R. Kumar

Keyword(s):

Robustness Analysis ◽

Error Identification ◽

Output Error

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