Vibration analysis of reconditioned high-speed electric motors

This paper discusses the issue of assessing the condition of three-phase induction motors (which are used as power units for tools on manufacturing lines in the furniture industry) following major repairs. These high-speed motors differ from standard motors in higher frequency power input, more durable bearings and reinforced structure of the terminal part of the rotor shaft. The rotational speed of the rotor is 10,000-18,000 rpm. Such high values of rotational speed trigger a situation in which exceeding the allowable limit of residual unbalance for the rotor unit damages the motor. The damage might necessitate a comprehensive repair. Such cases are frequent. The conducted studies resulted in designing a vibration analysis for assessing the condition of reconditioned high-speed motors (a method for controlling the quality of repairs). Both high- and low-frequency analyses with a signal selection module and the basic general measurements were applied. The analysis provides the possibility of verifying the efficacy of reconditioning in relation to the mechanical validity of the repaired motors

Infrared Thermography Based Defects Testing of Solar Photovoltaic Panel with Fuzzy Rule‐Based Evaluation

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Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques

In this paper, a fault detection and diagnosis approach adopted for an input-output feedback linearization (IOFL) control of induction motor (IM) drive is proposed. This approach has been employed to detect and identify the simple and mixed broken rotor bars and static air-gap eccentricity faults right from the start its operation by utilizing advanced techniques. Therefore, two techniques are applied: the model-based strategy, which is an online method used to generate residual stator current signal in order to indicate the presence of possible failures by means of the sliding mode observer (SMO) in the closed-loop drive. However, this strategy is not able to recognise the fault types and it can be affected by the other disturbances. Therefore, the offline method using the multi-adaptive neuro-fuzzy inference system (MANAFIS) technique is proposed to identify the faults and distinguish them. However, the MANAFIS required a relevant database to achieve satisfactory results. Hence, the stator current analysis based on the HFFT combination of the Hilbert transform (HT) and Fast Fourier transform (FFT) is applied to extract the amplitude of harmonics due to defects occur and used them as an input data set for the MANFIS under different loads and fault severities. The simulation results show the efficiency of the proposed techniques and its ability to detect and diagnose any minor faults in a closed-loop drive of IM