Applied Mechanics and Materials Vols. 764-765

Abstract: A rotary actuator that employs hydraulic oil as the power source has a direct rotary structure. It is an important structure and has been widely utilized in aircrafts and ships because of its advantages, including large torque/quality ratio, simple compact structure, and fast dynamic response. Huge damage may be caused when a rotary actuator breaks down during operation. However, only a few studies have focused on fault detection and performance assessment for rotary actuators. In this study, a method that detects the fault in and assesses the performance of the rotary actuator based on residual analysis is proposed. The data in normal state are utilized to build an observer with two radial basis function (RBF) neural networks. One RBF neural network is employed to estimate the expected output required to generate the residuals. The self-adaptive thresholds are obtained through the other RBF neural network. The residual is then inputted into the self-organizing mapping neural network trained by the residual values in normal state to normalize the performance of the rotary actuator into confidences values between 0 and 1. Finally, the detection and assessment of two typical faults of the rotary actuator are simulated. Results verify the efficiency of the proposed method.

Abstract: The image-guided system is the important issue for the automatic navigation. In this paper, a novel visually-guided disaster detection Robot (DD robot) is designed to carry out the automatic navigation function. Moreover, the DD robot is meticulously designed to detect gases or suspicious persons in the surrounding environment of the plant. Besides, we have successfully made our robots equipped with the devices by ourselves. These devices contain the disaster-detected sensors, the navigation module and the image module. In addition, we designed three operation modes: Automatic navigation mode (AN), Manual Remote Control mode (MRC) and the Visually-Guided mode (VG). Furthermore, the human-machine interface offers the users the view of information and also the details of the functions of the DD robot. The experimental results validate the practicality of the proposed visually-guided based automatic navigation system applied to disaster detection robots.

Abstract: Robot self-localization and obstacle avoidance has been one of the important topics in robotics. The sensing system which is more mature and using a laser range finder (LRF). But the biggest drawback is the LRF detection range is a plane, And for some high reflectivity of the object, will produce incorrect reflection data. So when the obstacle is not the detection range, or due to high reflectance data will generate an error and the positioning of the robot obstacle avoidance function error. This paper is the use of TLD (Tracking-Learning-Detection) image recognition system, to assist LRF do positioning and obstacle avoidance. And this imaging system can also be used while the robot with object tracking functions

Abstract: A thin-disc ultrasonic actuator using a piezoelectric buzzer is proposed as the actuating component for the shaft-driving type ultrasonic actuator. By placed the screw constraints on the metal sheet of a buzzer, a 3-phase reflected wave was constituted and propagated based on the purpose locations of constraints. This wave configuration could convert electrical energy to actuate the kinematical power for rotating the rotor. The input and output signals were acquisition according to the single-frequency exciting of system resonant frequency. The dynamic transfer function of a stator was obtained via the system identification technique, and, therefore, one model of a 3^th-order equivalent circuit was built in which the dynamic features and electromechanical characteristics were considered based on material oscillating behaviors. Because of the admittance transfer function derived from measured method, it is more representative than that of past issues through the theoretical deduction in materials, physics, and mechanics.

Abstract: Fault detection for aileron actuators mainly involves the enhancement of reliability and fault tolerant capability. Considering the complexity of the working conditions of aileron actuators, a fault detection method for an aileron actuator under variable conditions is proposed in this study. A bi-step neural network is utilized for fault detection. The first neural network, which is employed as the observer, is established to monitor the aileron actuator and generate the residual error. The other neural network generates the corresponding adaptive threshold synchronously. Faults are detected by comparing the residual error and the threshold. In considering of the variable conditions, aerodynamic loads are introduced to the bi-step neural network. The training order spectrums are designed. Finally, the effectiveness of the proposed scheme is demonstrated by a simulation model with different faults.

Abstract: A kind of hysteretic nonlinear model of magnetic shape memory alloy (MSMA) was developed in this paper, and the nonlinear dynamic characteristics of MSMA actuator were investigated. Van der Pol nonlinear item were introduced to interpret the hysteretic phenomena of strain-magnetic field intensity (MFI) curves of MSMA, and the constitutive relationship among strain, stress and MFI was obtained in partial least-square regression method to describe the variation of strain-MFI curves with stress. The result of significance test shows that all of the items in the constitutive model are significant, and the result of forecast test shows that the model can describe the hysteretic nonlinear characteristics of strain-MFI curves of MSMA in different stress well. Based on the MSMA model, the magneto-mechanical coupled model of MSMA actuator was developed, and the relationship between input magnetic signal and output displacement was obtained. The nonlinear dynamic characteristics of MSMA actuator were discussed, and phenomena of accuracy aggravation of MSMA actuator in high-frequency magnetic field were explained. Finally the theoretical results were proved by experiment. The new MSMA model has simple form and is easy to be analyzed in theory, which is helpful for the application of MSMA actuator in engineering fields.

Abstract: This study uses a wheeled mobile robot (WMR) to explore unknown indoor environment and build up a map of the unknown environment. The robot utilizes laser measurement sensor with a indoor localization system to detect obstacles and identify unknown environment. The localization system provides the position of the robot and is used for map comparison. Fuzzy theory is applied to controller design. The proposed control scheme can control the wheeled mobile robot move along walls and avoid obstacles. The Iterative Closest Point (ICP) and the KD-tree are utilized. With sensed data of obstructions and walls, a map of unknown environment can be generated by curve fitting methods.

Abstract: This paper presents simulation of multibody manufacturing systems with the support of numerical tools. The dynamic and cybernetic characteristics of driving system are discussed. Simple prototype models of robot arm and machine tool’s driving system are quickly established in Computer Aided Design (CAD) software inwhich the whole specification of material, inertia and so on are involved. The prototypes therefore are simulated in RecurDyn- a Computer Aided Engineering (CAE) software. The models are driven by controllers built in Matlab/Simulink via co-simulation. The results are suitable with theory and able to exploied for expansion of complexly effective factors. The research indicates that dynamic analysis and control could be done via numerical method instead of directly dynamic equation creation for multibody manufacturing systems.

Abstract: Hydraulic servo system is highly nonlinear. Building an accurate model of the system and predicting its remaining life are difficult. Thus, this study focuses on the prediction of the Hydraulic servo System based on Support vector regression (SVR). Elman neural network is utilized to build an observer to estimate the normal state output. The residual that contains a large amount of fault information is obtained, by calculating the difference between the estimated and actual values. Then we defined degradation index (DI) value which reflect the health of the system to normalize the residual. Lastly, a prediction model based on SVR established. The algorithm is verified by experiment.

Abstract: By using image processing and automatic control technologies, this study designs an unmanned ground vehicle (UGV) that can be operated by various control devices, such as laptops and joysticks. A camera is set up on the UGV to capture the sight around it, and then can send the video to the back end system in a wireless manner. Besides, we design an attacking system that allows users to do the zeroing correction for shooting the target precisely. Moreover, a shooting training system is designed to improve the convenience for target practice. It not only can send the result to the back end system immediately but also can simulate the enemy movement to make the training more reality.