Wyniki wyszukiwania - BazTech

1

Application of wavelet – neural method to detect backlash zone in electromechanical systems generating noises

Tomczyk Marcin, Plichta Anna, Mikulski Mariusz

Applied Computer Science

|

2019

|

Vol. 15, no 4

93--108

EN

This paper presents a method of identifying the width of backlash zone in an electromechanical system generating noises. The system load is a series of rectangular pulses of constant amplitude, generated at equal intervals. The need for identification of the backlash zone is associated with a gradual increase of its width during the drive operation. The study uses wavelet analysis of signals and analysis of neural network weights obtained from the processing without supervised learning. The time-frequency signal representations of accelerations of the mechanical load components were investigated.

2

Application of image analysis to the identification of mass inertia momentum in electromechanical system with changeable backlash zone

Tomczyk Marcin, Plichta Anna, Mikulski Mariusz

Applied Computer Science

|

2019

|

Vol. 15, no 3

87--102

EN

This paper presents a new method of identification of inertia moment of reduced masses on a shaft of an induction motor drive being a part of an electromechanical system. The study shows the results of simulations performed on the tested model of a complex electromechanical system during some changes of a backlash zone width. An analysis of wavelet scalograms of the examined signals carried out using a clustering technique was applied in the diagnostic algorithm. The correctness of the earliest fault detection has been verified during monitoring and identification of mass inertia moment for state variables describing physical quantities of a tested complex of the electromechanical system.

3

Identification of the mass inertia moment in an electromechanical system based on wavelet–neural method

Tomczyk Marcin, Borowik Barbara, Borowik Bohdan

Applied Computer Science

|

2018

|

Vol. 14, no 2

96--111

EN

This paper presents the results of testing of a complex electromechanical system model. These results have been obtained for accepted in simulations the method of identifying an inertia moment of reduced masses on shaft of induction motor drive during the changes of a backlash zone width. The effectiveness of correct diagnostic conclusions enables coefficients analysis of testing signals wavelet expansion as well as weights of a supervised learning neural network. The earlier fault detection of five important state variables, which describe physical quantities of chosen complex electro-mechanical system has been verified for its correctness during the backlash zone width monitoring in the early stage of its gradual rise. The proposed here algorithm with mass inertia moment changes has proved to be an effective diagnostic method in the area of system changeable dynamic conditions and this has been shown in the resulting changes of backlash zone width.

4

Identification of a Backlash Zone in an Electromechanical System Containing Changes of a Mass Inertia Moment Based on a Wavelet–Neural Method

Tomczyk Marcin, Borowik Barbara, Mikulski Mariusz

Applied Computer Science

|

2018

|

Vol. 14, no 4

54--69

EN

In this article a new method of identification of a backlash zone width in a structure of an electromechanical system has been presented. The results of many simulations in a tested model of a complex electromechanical system have been taken while changing a value of a reduced masses inertia moment on a shaft of an induction motor drive. A wavelet analysis of tested signals and analysis of weights that have been obtained during a neural network supervised learning - have been applied in a diagnostic algorithm. The proposed algorithm of detection of backlash zone width, represents effective diagnostic method of a system at changing dynamic conditions, occurring also as a result of mass inertia moment changes.