Neural Network Monitoring Strategy of Cutting Tool Wear of Horizontal High Speed Milling

The wear of cutting tool degrades the quality of the product in the manufacturing processes. The online monitoring of the cutting tool wear level is very necessary to prevent the deterioration of the quality of machining. Unfortunately there is not a direct manner to measure the cutting tool wear online. Consequently we must adopt an indirect method where wear will be estimated from the measurement of one or more physical parameters appearing during the machining process such as the cutting force, the vibrations, or the acoustic emission etc. In this work, a neural network system is elaborated in order to estimate the flank wear from the cutting force measurement and the cutting conditions

Hybrid Control Using Adaptive Fuzzy Sliding Mode for Diagnosis of Stator Fault in PMSM

In nonlinear control systems when we have a non-constant parameters, conventional control laws may be insufficient because they are not robust especially when the requirement on accuracy and other characteristics dynamic systems are strict. We must use control laws insensitive against to parameter variations, disturbance and nonlinearities. For this purpose, several tools are proposed in the literature, which is quoted a hybrid fuzzy logic and variable structure control (Fl_VSC). This per presents an application of the fuzzy logic scheme to control the speed of PMSM by taking account of the presence of interturn short circuit fault. We were interested in the sliding mode control (SMC) of the PMSM using controller’s fuzzy logic controller (FLC) and Adaptive fuzzy logic controller (AFLC). The combination of these two theories has given great performance with fast dynamic response without overshoot. As it has a very robust control, insensitive against to parameters variation and external disturbances. Simulation results confirm the choice of hybrid controllers compared with the conventional controllers and grants a robust performance and precise response to the reference model regardless of load disturbance, stator faults and PMSM parameter uncertainties

Adaptive Fuzzy Gain of Power System Stabilizer to Improve the Global Stability

The lead-lag power system stabilizer has several parameters to be optimized.In fact, the number of these latter increases with the number of generators constituting the multi-machine system.In this work, we propose anew approach of an adaptive and robust PSS; it achieves encouraging results by adjusting the gain using fuzzy logic and in the same time we use the same PSSs for each machine. In the first place, we could check that the gain is among the most critical parameters of the lead lag PSS. The parameters are globally optimized by the genetic algorithm, after that an expertise on the speed and the gain variations allow the value prediction according to the velocity deviation. To validate our results, a robustness test was made on a multimachine system IEEE (3 machines 9 bus), for different loads and the results showed good performance and robustness of the presented PSS

IMPROVED LS-SVM USING ACO TO ESTIMATE FLASHOVER VOLTAGE OF POLLUTED INSULATORS

The reliability of insulators under polluted environment is one of the guiding factors in the insulation coordination of high voltage transmission lines. In order to improve understanding of the flashover phenomenon in polluted insulators, several experimental studies and mathematical approaches have been made‎ in‎ last‎ year’s.‎ In‎ this‎ paper,‎ the‎ critical flashover voltage behavior of polluted insulators has been calculated and a hybrid model between machine Learning (ML) and optimization technique has been proposed. For this purpose, firstly the ant colony optimization (ACO) technique is utilized to optimize the hyper-parameters needed in least squares support vector machines (LS-SVM). Then, a LS-SVM-ACO model is designed to establish a nonlinear model between the characteristics of the insulator and the critical flashover voltage. The data used to train the model and test its performance is derived from experimental measurements and a mathematical model. The results obtained from the proposed model are in good accord with other mathematical and experimental results of previous researchers