Recently, researchers are investing more fervently in fault diagnosis area of electrical machines. The users and manufacturers of these various efforts are strong to contain diagnostic features in software for improving reliability and scalability. Internet of Things (IoT) has grown immensely and contributing for the development of recent technological advancements in industries, medical and various environmental applications. It provides efficient processing power through cloud, and presents various new opportunities for industrial automation by implementing IoT and industrial wireless sensor networks. The process of regular monitoring enables early detection of machine faults and hence beneficial for Industrial automation by providing efficient process control. The performance of fault detection and its classification by implementing machine-learning algorithms highly dependent on the amount of features involved. The accuracy of classification will adversely affect by the dimensionality features increment. To address these problems, the proposed work presents the extraction of relevant features based on oriented sport vector machine (FO-SVM). The proposed algorithm is capable for extracting the most relevant feature set and hence presenting the accurate classification of faults accordingly. The extraction of most relevant features before the process of classification results in higher classification accuracy. Moreover it is observed that the lesser dimensionality of propose process consumes less time and more suitable for cloud. The experimental analysis based on the implementation of proposed approach provides and solution for the monitoring of machine condition and prediction of fault accurately based on cloud platform using industrial wireless sensor networks and IoT service.
To improve the speed of global optimization algorithm, a class of global optimization algorithms for intelligent electromechanical control system with improved filling function is proposed. By attaching the intelligent managing system improving algorithm and the filling function procedure, the algorithm can stand out from the current particular optimal solution, avoid the phenomenon of falling into the local favorable solution in the process of algorithm iteration, make the algorithm find a better solution, and improve the efficiency of solving the multiextremum global improving problem. Multiextremum-seeking is an optimal control technique that works with unknown conditions while assuming that measurements of the plant’s input and output signals are accessible. The presented work is for an electromechanical system which will handle the low accuracy and untimely tendency of conventional systems which are used in various practical applications. Few learning algorithms have been developed to explicitly optimize mean average precision (MAP) due to computational constraints. The outcomes show that the convergence of the test functions F6 and F7 is not good when the MAPID algorithm is only used for optimization. The MAPID_FF algorithm not only ensures the convergence and optimization precision of the two test functions, but also reduces the optimization time compared with the filling function method. Compared with the filling function method, the improved algorithm has higher accuracy and faster speed, and it is not simple to fall into the local optimum, so the global optimal value is more accurate.
In the present study, vibration and damping characteristics of the multi‐walled carbon nanotubes (MWCNT) reinforced honeycomb embedded sandwich composite shell structures have been investigated numerically using finite element (FE) method. The efficacy of the FE method by deriving the governing equations using higher order theory is verified by comparing the natural frequencies assessed using ABAQUS 3D FE model. The influence of MWCNT reinforcement, support condition, and radius of curvature on the dynamic performance of honeycomb sandwich shell with carbon fiber reinforced polymer (CFRP) composite face sheets are explored. In addition, the optimal ply orientations of the various configurations of CFRP sandwich shells with MWCNT/GFRP honeycomb are identified using the developed FE model coupled with genetic algorithm (GA) to enrich the natural frequencies and loss factors. Further, it can be observed that the reinforcement of MWCNTs in honeycomb core, geometry of shell structure, and optimal ply orientations significantly influences the natural frequencies and loss factors of the sandwich composite shell structures.
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