Overview of existing methods for diagnosing faults in synchronous electric motors and methods for their detection. Classification and analysis of existing methods, their applicability in detecting faults, advantages and disadvantages. Three classes of possible faults in synchronous permanent magnet motors are considered and described: electrical faults, mechanical faults, and demagnetization. The article discusses three classes of diagnostic methods: based on the construction of a mathematical model of a real electric motor and modeling its errors, based on processing signals from sensors, and intelligent methods based on processing collected data using artificial intelligence. The following error detection methods based on modeling are considered: detection based on the model of the electrical schematic, based on the analytical model, and based on the digital simulation model. The following frequency-time analysis methods of the obtained signals from the sensors are considered: analysis using fast Fourier transform, short-time Fourier transform, wavelet transform, Hilbert-Huang transform, and Wigner-Ville distribution. The following intelligent diagnostic methods are considered: diagnosis using convolutional neural networks, recurrent neural networks, support vector machines, fuzzy logic, and sparse representation.

Keywords: Synchronous motor with permanent magnets, faults of electric motor, modeling, fast Fourier transform, wavelet transform, Hilbert-Huang transform, Wigner-Ville distribution, neural networks, fuzzy logic, support vector machine, sparse representation.

In this work, we studied the effect of fog on machine vision systems, in particular, on the correctness of the pattern recognition algorithm. As part of this work, a filter is implemented that eliminates distortions caused by fog. A corrective filter has been developed, an analysis of the operation of a neural network with images of various definitions has been carried out, on the basis of which recommendations have been made to improve the accuracy of pattern recognition.

Keywords: image processing, image filtering, machine vision systems, pattern recognition