The fault detection and diagnosis (FDD) along with condition monitoring (CM) and of rotating machinery (RM) have critical importance for early diagnosis to prevent severe damage of infrastructure in industrial environments. Importantly, valuable industrial equipment needs continuous monitoring to enhance the safety, reliability, and availability and to decrease the cost of maintenance of modern industrial systems and applications. However, induction motor (IM) has been extensively used in several industrial processes because it is cheap, reliable, and robust. Rolling bearings are considered to be the main component of IM. Undoubtedly, any failure of this basic component can lead to a serious breakdown of IM and for whole industrial system. Thus, many current methods based on different techniques are employed as a fault prognosis and diagnosis of rolling elements bearing of IM. Moreover, these techniques include signal/image processing, intelligent diagnostics, data fusion, data mining, and expert systems for time and frequency as well as time-frequency domains. Artificial intelligence (AI) techniques have proven their significance in every field of digital technology. Industrial machines, automation, and processes are the net frontiers of AI adaptation. There are quite developed literatures that have been approaching the issues using signals and data processing techniques. However, the key contribution of this work is to present an extensive review of CM and FDD of the IM, especially for rolling elements bearings, based on artificial intelligent (AI) methods. This study highlights the advantages and performance limitations of each method. Finally, challenges and future trends are also highlighted.

The motion-planning problem is well known in robotics; it aims to find a free-obstacle path from a starting point to a destination. To make use of actuation generosity and the fuzzy fast response behavior compared to other non-linear controllers, a fuzzy-based fault-tolerant control for an omnidirectional mobile robot with four Mecanum wheels is proposed. The objective is to provide the robot with an online scheme to control the robot motion while moving toward the final destination with avoiding obstacles in its environment and providing an adaptive solution for a combination of one or combination of the wheel’s faults. The faults happen when the wheel does not receive the control command signal from the controller; in this case, the robot can rotate freely due to the interaction with the ground. The principle of fuzzy-based control proposed by Sugeno is used to develop the motion controller. The motion controller consists of two main controllers: the Run-To-Goal, and the obstacle-avoidance controller. The outputs of these two controllers are superposed to get the net potential force on the robot. By its simplicity, the fuzzy controller can be suitable for online applications (online path planning in our case). To the best of our knowledge, this is the first fuzzy-based fault-tolerant controller for an omnidirectional robot. The proposed controller is tested by a set of simulation scenarios to check the proposed fuzzy tolerant control. Kuka OmniRob is used as an example of the omnidirectional robot in these simulation runs. Matlab is used to build the fuzzy-based fault-tolerant control, and the 3D simulation is developed on the CoppeliaSim software. We examine five distinct scenarios, each one with a different fault state. In all scenarios, the proposed algorithm could control the robot to reach its final destination with the absence and presence of an obstacle in the workspace, despite actuator faults, without crossing the workspace boundaries.

Wearable and flexible materials are replacing the conventional solid-state sensors in diffident biomedical applications. Hydrogel-based sensing elements offer several appealing inherent properties such as tissue resembling elasticity, accessibility for modification and robust mechanical performance. Their widely available and affordable raw components in-addition to straightforward synthesis and modification approach make hydrogels appealing material for flexible and wearable sensors in biomedical applications. This work demonstrates the development of new and sensitive material for strain sensing using polyvinyl alcohol (PVA) and κ-carrageenan (κCA) hydrogel comprising conductive polyaniline nanofibers (PANI NFs). The double-network hydrogel was produced via chemical crosslinking of PVA with Glutaraldehyde (GA) and physical crosslinking of κCA with potassium ions in a binary solvent system of deionized water and glycerol. The PANI NFs were then embedded in the hydrogel via the interfacial polymerization (IP) method of polyaniline nanofibers to significantly enhance the material properties and strain sensitivity of the pristine hydrogel. The obtained hydrogel has been involved in rigorous material characterization and sensing capability evaluation. The produced hydrogel demonstrated a high-water content (86.6%), high swelling percentage in acidic solutions, mechanical compressibility up to 60% at 400 kPa, high electrical conductivity of 2.11 S/m, and thermal stability ranging from − 26.9 to 120 ^oC. The hydrogel resulted in a linear response in its sensing performance of the applied stress (R² = 0.99). Also, the developed composite demonstrated a sensitivity of 1.5 mV/kPa in stress range from zero up to 170 kPa with response and recovery times of ~ 300 ms and 500 ms, respectively.

This research focuses on a collision-free real-time motion planning system for unmanned aerial vehicles (UAVs) in complex three-dimensional (3D) dynamic environments based on generalized potential force functions. The UAV must survive in such a complex heterogeneous environment while tracking a dynamic target and avoiding multiple stationary or dynamic obstacles, especially at low hover flying conditions. The system framework consists of two parts. The first part is the target tracking part employing a generalized extended attractive potential force into 3D space. In contrast, the second part is the obstacle avoidance part employing a generalized extended repulsive potential force into 3D space. These forces depend on the relative position and relative velocity between the UAV and respective obstacles. As a result, the UAV is attracted to a moving or stationary target and repulsed away from moving or static obstacles simultaneously in 3D space. Accordingly, it changes its altitude and projected planner position concurrently. A real-time implementation for the system is conducted in the SPACE laboratory to perform motion planning in 3D space. The system performance is validated in real-time experiments using three platforms: two parrot bebop drones and one turtlebot robot. The pose information of the vehicles is estimated using six Vicon cameras during real-time flights. The demonstrated results show the motion planning system’s effectiveness. Also, we propose a successful mathematical solution of the local minima problem associated with the potential field method in both stationary and dynamic environments.

This paper uses the EEG analysis to investigate the relationship between pre-learning stress, frontal lope and long-term memory in the brain. The stress on learning stage is a challenge, especially in academic life. Stress also on learning stage affects the retrieval or recall from the memory. Nowadays; there are many recent works have discovered the relationship between stress, learning and memory performance based on different techniques. Some of these techniques are biological methods. Moreover, these methods have discovered the effect of stress based on hormones levels such as cortisol, or based on physiological effects such as blood pressure. However, these techniques have given conflicting discoveries because of the instability of hormones and an extensive number of related elements. The main aim of this research is to discover the relationship between Pre-learning stress, frontal lope, and long-term memory retrieval using EEG signals. The experimental results indicate that there is a relationship between theta rhythm in the frontal lobe and long-term memory retrieval during Pre-learning stress.

In this paper, we present a robotic locomotor with inertia-based actuation. The goal of this system is to generate various gait modes of a baton, consisting of two masses connected with a massless rod. First, a model for a baton prototype called Pony I is presented. This model incorporates the inertial forces generated by a rotating single pendulum. The model also accounts for the friction forces that arise in the contact points of the baton with the ground surface. We also developed an experimental prototype for a baton with a single-pendulum actuator. Consequently, we compared the nonlinear dynamics of the analytical and experimental systems. An improved double-pendulum actuation system was proposed for better regulation of the locomotion of the system and the orientation of the centrifugal force. Finally, demonstrated that this system generated steady forward locomotion.