Abstract
This article addresses an improvement of a classification procedure on cracked rotors through Deep learning based on convolutional neural networks (CNNs). At first, a cracked rotor-bearing system is modeled by the finite element method (FEM), then throughout its start-up, the related time-domain responses are calculated numerically. In the following, as a pre-processing stage, continuous wavelet transform (CWT) and Short-time Fourier transform (STFT) are applied on the three various health conditions, i.e. without crack, shallow-cracked, and relatively deep-cracked shafts. The plots of CWT’s coefficients and STFT’s in these various classes are used as the input dataset in Deep learning based on CNNs and the three classes are introduced as the output. AlexNet with 25 layers is employed as the network. The results of the testing phase demonstrated that not only this expanded method has a reasonable capacity in the classification of cracked and healthy rotors, but it also can classify cracked rotors with different crack depths with a negligible error.
Highlights
In the course of a noticeable increase in energy consumption that is an ineluctable conclusion of the industrial revolution, effective maintenance of energy conversion appliances has become a favorite field in scientific investigations
By applying Deep learning procedure rotor-bearing-disc is classified based on its healthy condition
The system is modeled employing the finite element method, and its mass, stiffness, damping, and force matrices are extracted; crack in the system is modeled by applying strain energy procedure and extra flexibility of the crack is added to the element that contains a crack
Summary
In the course of a noticeable increase in energy consumption that is an ineluctable conclusion of the industrial revolution, effective maintenance of energy conversion appliances has become a favorite field in scientific investigations. To mention the importance of rudimentary faults identification in energy transformer instruments it is enough to say that delayed recognition of deficiencies can bring about catastrophic repercussions both in the labor force and cost. One of the most common machines in the energy field is the rotor system that normally operates at a high pace, and a large amount of high-price appurtenances are attached to it. Rotating systems are suffering from a wide range of mechanical and electrical faults such as unbalance, crack, rotor to stator rub, misalignment, and things like these areas. Crack accounts as a prevalent deficiency in the rotor system. Many factors can involve in creating a crack and this fault can be categorized with consideration of its angle with the shaft’s central axis, or its depth
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