The utilization rate of permanent magnet synchronous motors (PMSM) is increasing in the industry today. Due to this fact, the high efficiency ratio of PMSMs has reached IE5 premium class efficiency. Therefore, the efficiency coefficient of the PMSM varies from 92% to 97%. As a result, this type of motor is replacing traditional asynchronous motor by falling into efficiency classes of IE1, IE2, IE3, and IE4, which range from 75% to 92% in the industry. Thus, the object of the research was to develop a method to determine the efficiency of permanent magnet synchronous motor applications in order to identify and verify the variating parameters. In this study, an innovative and safe method of PMSM testing when the nominal parameters of the motor are unknown was presented through research. Also, the comparison of PMSM oscillograms with different types of load types and phase shift oscillograms, generated using operation amplifier, were analyzed and is scrutinized. During the design process, the PMSM was projected for the IE5 premium efficiency class. However, after production, the PMSM sometimes does not match the nameplate parameters, which are declared by the factory. As a result, during the testing procedures, the PMSM nameplate parameters did not match the projected parameters. Facing the problem of the projected and tested efficiency mismatch, the PMSM highest efficiency determination experiments were performed in a laboratory in order to prove the highest efficiency of the PMSM. The results showed different PMSM input parameters. Furthermore, the experimental results of the PMSM testing were confirmed with electrical machines theory, and simulation results were performed with electrical circuits. The theory of PMSM operating in different values of input voltage is represented in graphical abstract.

In this research, electric motors faults and their identification is reviewed. Brushless direct-current (BLDC) motors stator fault identification using long short-term memory neural networks were analyzed. A proposed method of vibration data acquisition using cloud technologies with high accuracy, feature extraction using spectral entropy, and instantaneous frequency and standardization using mean and standard deviation was reviewed. Additionally, model training with raw and standardized data was compared. A total model accuracy of 97.10 percent was achieved. The proposed methods could successfully identify the motor stator status from normal, to loss of stator winding imminent and arcing, and lastly to open circuit in stator winding—motor needing to stop immediately—by using gathered data from real experiments, training the model and testing it theoretically.

In this article, a type of diagnostic tool for an asynchronous motor powered from a frequency converter is proposed. An all-purpose, effective, and simple method for asynchronous motor monitoring is used. This method includes a single vibration measuring device fixed on the motor’s housing to detect faults such as worn-out or broken bearings, shaft misalignment, defective motor support, lost phase to the stator, and short circuit in one of the phase windings in the stator. The gathered vibration data are then standardized and continuous wavelet transform (CWT) is applied for feature extraction. Using morl wavelets, the algorithm is applied to all the datasets in the research and resulting scalograms are then fed to a complex deep convolutional neural network (CNN). Training and testing are done using separate datasets. The resulting model could successfully classify all the defects at an excellent rate and even separate mechanical faults from electrical ones. The best performing model achieved 97.53% accuracy.

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