Yousif I. Al Mashhadany

Real-time controller for foot-drop correction by using surface electromyography sensor.

Foot drop is a disease caused mainly by muscle paralysis, which incapacitates the nerves generating the impulses that control feet in a heel strike. The incapacity may stem from lesions that affect the brain, the spinal cord, or peripheral nerves. The foot becomes dorsiflexed, affecting normal walking. A design and analysis of a controller for such legs is the subject of this article. Surface electromyography electrodes are connected to the skin surface of the human muscle and work on the mechanics of human muscle contraction. The design uses real surface electromyography signals for estimation of the joint angles. Various-speed flexions and extensions of the leg were analyzed. The two phases of the design began with surface electromyography of real human leg electromyography signal, which was subsequently filtered, amplified, and normalized to the maximum amplitude. Parameters extracted from the surface electromyography signal were then used to train an artificial neural network for prediction of the joint angle. The artificial neural network design included various-speed identification of the electromyography signal and estimation of the angles of the knee and ankle joints by a recognition process that depended on the parameters of the real surface electromyography signal measured through real movements. The second phase used artificial neural network estimation of the control signal, for calculation of the electromyography signal to be stimulated for the leg muscle to move the ankle joint. Satisfactory simulation (MATLAB/Simulink version 2012a) and implementation results verified the design feasibility.Foot drop is a disease caused mainly by muscle paralysis, which incapacitates the nerves generating the impulses that control feet in a heel strike. The incapacity may stem from lesions that affect the brain, the spinal cord, or peripheral nerves. The foot becomes dorsiflexed, affecting normal walking. A design and analysis of a controller for such legs is the subject of this article. Surface electromyography electrodes are connected to the skin surface of the human muscle and work on the mechanics of human muscle contraction. The design uses real surface electromyography signals for estimation of the joint angles. Various-speed flexions and extensions of the leg were analyzed. The two phases of the design began with surface electromyography of real human leg electromyography signal, which was subsequently filtered, amplified, and normalized to the maximum amplitude. Parameters extracted from the surface electromyography signal were then used to train an artificial neural network for prediction of the joint angle. The artificial neural network design included various-speed identification of the electromyography signal and estimation of the angles of the knee and ankle joints by a recognition process that depended on the parameters of the real surface electromyography signal measured through real movements. The second phase used artificial neural network estimation of the control signal, for calculation of the electromyography signal to be stimulated for the leg muscle to move the ankle joint. Satisfactory simulation (MATLAB/Simulink version 2012a) and implementation results verified the design feasibility.

SCARA Robot: Modeled, Simulated, and Virtual-Reality Verified

Articulated-morphology robots, with applications ranging from the basic to the sophisticated, have increased in importance and popularity, especially with decreasing costs of computers and increased studies on feasibility. The development of a complete mathematical model for industrial, selective compliance articulated (SCARA) robot arm including its servomotor dynamics, and simulation of the dynamics, are presented here, as are the analytical inverse kinematic problem (AIKP) and the forward kinematic solution with D-H parameters. The robot arm is built for trajectories in handling, manufacture, assembly, etc. The 3D virtual reality (VR) model realizing it builds and receives commands through a MATLAB/Simulink link, for the design to be simulated on MATLAB Version R2012a. The analytical solution of IKP and modelling under real phasic rule consideration are done here. The integrated approach improves system performance, cost-effectiveness, efficiency, dynamism, and high reality performance. The method’s effectiveness is proved, as is the faster response (settling). It is advantageous to industry, and real-time application is possible through interface cards.

Comparison of Fault Diagnosis Approaches in Industrial Wireless Networks: A Review

Wireless sensor networks have received increasing research attention and they can be found in every field of life. The industrial wireless sensor network is one of the boosting and emerging technologies for machine fault diagnosis and monitoring. This study provides a review on vibration fault diagnosis approaches in industrial wireless applications and discusses the causes of machine faults and challenges. Several advanced vibration approaches have been used to quantify machine operating conditions. These approaches provide a fault diagnosis mechanism and expert maintenance solutions through analysis of vibration. The review also shows a broad scope of research for developing a robust fault diagnosis approaches in the field of industrial wireless sensor networks.

Identify and Classify Vibration Signal for Steam Turbine Based on Neural Sleep Fuzzy System

Vibration in a steam turbine-generator is one of the many default problems, similar to thrust, crack and low or high speeds, all of which causes damage to the steam turbine if leaves unprotected . It leads to accidents and damages, when overcome the limit of alarm or danger zones. The protection of steam turbine generators from danger leads to reduced maintenances and augmented stability of power generation. The main proposal of this study is to identify and classify vibrations in alarm and shutdown zones, it is also intended to produce a smooth signal that can be used to adjust control value, which influences the vibration value during the start-up and power generation. We compared the series and parallel-connected Neural Network (N N) that is related to time and error to identify vibration acceleration signals and flow by sleep fuzzy sugeno s ystem, which are designed and simulated in MATLAB. The results showed that parallel-connected NN is superior to its series-connected counterpart with vibration signals, where the Neural-Sleep-Fuzz y system and the NSFS robust system produces zero voltages when it lacks vibrations, more so after receiving ali near signal to influence nonlinear signals of vibration. This study concluded that the Artificial Intelligent (AI) syste m with sleep fuzzy sugeno system can be implementing to classify the fault of optimal vibration signal limitation an d check the suitable treatment for this fault. Also, the analysis of results can conclude that using parallel NN is fast er and more accurate compared to series NN connection.

Gas Turbine bearing and vibration classification of using multi-layer Neural Network

Gas Turbine (GT) is a vital component to a power plant. This system contains many signals that are used to control and protect the GT from damage or accidents caused by vibration, speed, and temperature. Moreover, the vibrations of GT at dangerous levels might lead to damages to the system. In this paper, a concerted effort is made to identify the number of the bearing and vibration levels during operations. We designed and compared two types of the Neural Networks (NNs); series and parallel NNs. They are based on the two stages from NNs employed by MATLAB. The results indicated that the parallel NN is better, depending on the time training and the produced error. Moreover, the two stages of NNs can identify the bearing number and vibration situations. The structure of the NNs puts the system in sleep mode until the vibration is in high level, however, sleeping system leads to the reduction of power consumption when designing the hardware system.