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Infrared thermography can measure the temperature of a surface remotely. In this article authors present a diagnostic method of incipient fault detection. The proposed approach is based on pattern recognition. It uses monochrome thermal images of the rotor with the application of an area perimeter vector and a Bayes classifier. The investigations have been carried out for direct current motor without faults and motor with shorted rotor coils. The measurements were performed in the laboratory. The efficiency of recognition using the area perimeter vector and the Bayes classifier was 100 %. The investigations show that the method based on recognition of thermal images can be profitable for engineers. The proposed method can be applied in mining, metallurgy, fuel industry and in factories where electrical motors are used.

In this article results of diagnostic investigations of separately excited DC motor were presented. In diagnostics were applied a Fourier analysis method based on the fast Fourier transform (FFT) and a recognition method using Bayes classifier. In training process a set of the most important frequencies has been determined for which differences of corresponding signals in two states are the largest. Three categories of signals have been recognized in identification process: faultless state, state of the rotor broken one coil and state of the rotor shorted three coils.

Single-phase induction motors are used in the industry commonly. Induction motors are not expensive, so it is a reason to use them. Diagnostics of faults is very important. It prevents financial loss and unplanned downtimes causes by faults. In this paper the authors described fault diagnostic techniques of the single-phase induction motor. Presented techniques were based on the analysis of thermal images of electric motor. The authors measured and analysed 3 states of the single-phase induction motor. In this paper an original method of the feature extraction of thermal images called MoASoS (Method of Area Selection of States) was presented. The proposed method - MoASoS and an image histogram were used to form feature vectors. Classification of the obtained vectors was performed by NN (Nearest Neighbour classifier) and Gaussian Mixture Models (GMM). The described fault diagnostic techniques are useful for reliability of the single-phase induction motors and other rotating electrical machines such as: three-phase induction motors, synchronous motors, DC motors. [ABSTRACT FROM AUTHOR]

An article describes an early fault diagnostic technique based on acoustic signals. The presented technique was used for a single-phase induction motor. The authors measured and analysed following states of the motor: healthy single-phase induction motor, single-phase induction motor with faulty bearing, single-phase induction motor with faulty bearing and shorted coils of auxiliary winding. A feature extraction method called MSAF-20-MULTIEXPANDED (Method of Selection of Amplitudes of Frequency – Multiexpanded) was discussed. The MSAF-20-MULTIEXPANDED was used to create feature vectors. The obtained vectors were classified by NN (Nearest Neighbour classifier), NM (Nearest Mean classifier) and GMM (Gaussian Mixture Models). The proposed technique can be used for diagnosis of the single-phase induction motors. It can be also used for other types of rotating electric motors. [ABSTRACT FROM AUTHOR]