Monitoring of rotating machines by vibration analysis is a topic that has received a great interest in recent years. Moreover, the vibrations from a machine are affected greatly by the conditions of its operation (speed, load and so on). A significant challenge remains with the monitoring of gears under fluctuating operating conditions. An unexpected fault of gear may cause huge economic losses, even personal injury. In this study, a new method based on adaptive Morlet wavelet (AMW) is proposed for the analysis of vibration signals produced from a gear system under test in order to detect early the presence of faults. The mother Morlet wavelet is adapted with the gear vibration signal by setting parameters of the wavelet to balance the time-frequency resolution. The obtained optimal pair of parameters results in the best time-frequency resolution for the given vibration signal; and the fault detection problem is considered just as a simple signature search in the timescale domain using scalograms. An early indication of the presence of a gear defect is obtained at the 10th day of experimentation using the AMW-based method. Whereas, the gear system has a defect on the 12th day corresponding to the tooth damage which results in a complete change in the location of the AMW coefficients.

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This paper presents a new of economic and environmental study of a hybrid system (wind turbine and diesel generator) in Algerian desert regions using HOMER software. The principal interests of the hybrid system are clean production at the place of consumption, the combined use of resources, energy storage, and supply security. Using an experimental device outfitted with Homer software, models were developed and successfully compared to reality. The obtained results confirm that the proposed system proved to be accurate enough to distinguish energy transfers and fast enough to enable optimizing the sizing and handling of the system's energy transfers. The purpose of this paper is to confirm the robustness of the HOMER software and to check the technical, economic, and environmental criteria of the integrated system.

The supervision task of industrial systems is vital, and the prediction of damage avoids many problems. If any system defects are not detected in the early stage, this system will continue to degrade, which may cause serious economic loss. In industrial systems, the defects change the behaviour and characteristics of the vibration signal. This change is the signature of the presence of the defect. The challenge is the early detection of this signature. The difficulty of the vibration signal is that the signal is very noisy, non-stationary and non-linear. In this study, a new method for the early defect detection of a gear system is proposed. This approach is based on vibration analysis by finding the defect’s signature in the vibration signal. This approach has used the autocorrelation of Morlet wavelet transforms (AMWT). Firstly, simulation validation is introduced. The validation of the approach on a real system is given in the second validation part.

In this paper, several techniques and models proposed the spread of coronavirus (Covid-19) and determines approximately the final number of coronavirus infected cases as well as infection point (peak time) in Algeria. To see the goodness of the predicting techniques, a comparative study was done by calculating error indicators such as Root-Mean-Square Error (RMSE) and the sum of squared estimate of errors (SSE). The main technique used in this study is the logistic growth regression model widely used in epidemiology. The results only relate to the two months from the beginning of the epidemic in Algeria, which should be readjusted by integrating the new data over time, because hazardous parameters like possible relaxations (decrease of vigilance or laxity of society) can affect these results and generally cause a time lag in the curve. Hence, a re-estimation of the curves is always requested.