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In this study, the problems of mechanical unbalance, parallel and angular misalignments and their combinations are analyzed experimentally. Such frequent defects in the drives mainly in the major powers are also responsible for the bearings degradation. However, they have not raised the attention of researchers, given the complexity of their modeling. The combination of the phasic current signal analysis and the neutral current by the FFT supplemented by visual interpretation of patterns models these defects resulting from the 3D representation. The results obtained by using the proposed method show the efficiency to provide an accurate diagnosis of the state of the electric drive undergoing to isolated and combined mechanical defaults to a maintenance staff not necessarily expert of mechanical failure. The innovative approach validated experimentally on a 5.37 KW motor, offers an efficiency to provide an accurate diagnosis to a maintenance staff not necessarily composed of experts in this field.

The industry world uses machines and plants that are increasingly powerful and complex. The requirements of high safety, the reduction of the exploitation costs, and the control of the equipment availability give to maintenance a dominating role. The industrialists attach a great importance to the conditional maintenance of the revolving machines that use primarily the vibrations of their rotors. The temperature measurements in the stages can bring additional information to vibrations. In conditional preventive maintenance, the diagnosis of failures of the industrial systems, if it is carried out with effectiveness, represents one of the means to gain points of productivity. It consists in observing the symptoms of a failure and then identifying the cause using a logical reasoning founded on observations, that is, to dismount a deterministic mechanism between the cause and its effect. This research presents and discusses the decision making that is practically exerted with each stage in the procedure of industrial diagnosis and tool of assistance to the decision making. The approach used is Bayesian theory to reveal a defect masked by another in the low frequency (combined defect). The turbo compressor as an object of research, vibratory analyses, and thermography are the techniques used in this work. Copyright © 2011 John Wiley & Sons, Ltd.

Serious failures of wind turbine drive-trains occur in gear which plays an essential role. Owing to the complicated vibration signal of faulty gear and the characteristic fault frequency buried in the background noise. Thus, detecting a defect of this component with classical methods is a great challenge. In order to overcome this issue, a combined technique of time-frequency analysis based on Morlet wavelet coefficient (MWC) and fast kurtogram (MWC-FK) is proposed for gear fault detection. The Morlet wavelet (MW) is able to detect components impulses and the fast Kurtogram (FK) is appropriate for environmental noise elimination and extracts the impulses in the filtered signal. First, the wavelet coefficient is obtained using the continuous Morlet wavelet transform decomposition for further analysis. Then, the wavelet coefficient signal that has the highest value of the kurtosis index is chosen. Finally, the selected signal is filtered by an optimal band-pass filter based on fast kurtogram. In order to confirm the usefulness and robustness of the proposed method, a real vibration signal of wind-turbine pinion with fault is used in this work. The results have showed the efficiency of the proposed method in gear fault detection and the extraction of fault characteristic frequencies by the squared envelope spectrum (SES) of the filtered Morlet wavelet coefficient signal. KeywordsVibration analysis • Fault detection • Morlet wavelet coefficient features • Fast kurtogram (FK) • Wind turbine gearbox * Grabsia Naima