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 electromechanical 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.

Tomczyk, M., Borowik, B., & Borowik, B. (2018). Identification of the mass inertia moment in an electromechanical system based on wavelet–neural method. Applied Computer Science, 14(2), 96-111. doi:10.23743/acs-2018-16

Tomczyk, Marcin, Barbara Borowik, and Bohdan Borowik. "Identification of the Mass Inertia Moment in an Electromechanical System Based on Wavelet–Neural Method." Applied Computer Science 14, no. 2 (2018): 96-111.

M. Tomczyk, B. Borowik, and B. Borowik, "Identification of the mass inertia moment in an electromechanical system based on wavelet–neural method," Applied Computer Science, vol. 14, no. 2, pp. 96-111, 2018.

Tomczyk M, Borowik B, Borowik B. Identification of the mass inertia moment in an electromechanical system based on wavelet–neural method. Applied Computer Science. 2018;14(2):96-111.