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.
PurposeThe purpose of this paper is to propose a distributed deep learning architecture for smart cities in big data systems.Design/methodology/approachWe have proposed an architectural multilayer to describe the distributed deep learning for smart cities in big data systems. The components of our system are Smart city layer, big data layer, and deep learning layer. The Smart city layer responsible for the question of Smart city components, its Internet of things, sensors and effectors, and its integration in the system, big data layer concerns data characteristics 10, and its distribution over the system. The deep learning layer is the model of our system. It is responsible for data analysis.FindingsWe apply our proposed architecture in a Smart environment and Smart energy. 10; In a Smart environment, we study the Toluene forecasting in Madrid Smart city. For Smart energy, we study wind energy foresting in Australia. Our proposed architecture can reduce the time of execution and improve the deep learning model, such as Long Term Short Memory10;.Research limitations/implicationsThis research needs the application of other deep learning models, such as convolution neuronal network and autoencoder.Practical implicationsFindings of the research will be helpful in Smart city architecture. It can provide a clear view into a Smart city, data storage, and data analysis. The 10; Toluene forecasting in a Smart environment can help the decision-maker to ensure environmental safety. The Smart energy of our proposed model can give a clear prediction of power generation.Originality/valueThe findings of this study are expected to contribute valuable information to decision-makers for a better understanding of the key to Smart city architecture. Its relation with data storage, processing, and data analysis.
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