Fault Diagnosis for Gearbox Based on Improved Empirical Mode Decomposition

The application of the improved empirical mode decomposition (EMD) theory in gearbox fault diagnosis has been studied in this paper, and the transient features of gearbox vibration signals are shown. Based on using EMD, an improved algorithm of orthogonal empirical mode decomposition (OEMD) is put forward and is applied to extract the fault feature. Finally, fault diagnosis application in a gearbox is used as an example to prove the feasibility of the proposed method

A New Synergistic Forecasting Method for Short-Term Traffic Flow with Event-Triggered Strong Fluctuation

Directing against the shortcoming of low accuracy in short-term traffic flow prediction caused by strong traffic flow fluctuation, a novel method for short-term traffic forecasting based on the combination of improved grey Verhulst prediction algorithm and first-order difference exponential smoothing is proposed. Firstly, we constructed an improved grey Verhulst prediction model by introducing the Markov chain to its traditional version. Then, based on an introduced dynamic weighting factor, the improved grey Verhulst prediction method, and the first-order difference exponential smoothing technique, the new method for short-term traffic forecasting is completed in an efficient way. Finally, experiment and analysis are carried out in the light of actual data gathered from strong fluctuation environment to verify the effectiveness and rationality of our proposed scheme

Disturbance observer enhanced variable gain controller for robot teleoperation with motion capture using wearable armbands

Disturbance observer (DOB) based controller performs well in estimating and compensating for perturbation when the external or internal unknown disturbance is slowly time varying. However, to some extent, robot manipulators usually work in complex environment with high-frequency disturbance. Thereby, to enhance tracking performance in a teleoperation system, only traditional DOB technique is insufficient. In this paper, for the purpose of constructing a feasible teleoperation scheme, we develop a novel controller that contains a variable gain scheme to deal with fast-time varying perturbation, whose gain is adjusted linearly according to human surface electromyographic signals collected from Myo wearable armband. In addition, for tracking the motion of operator’s arm, we derive five-joint-angle data of a moving human arm through two groups of quaternions generated from the armbands. Besides, the radial basis function neural networks and the disturbance observer-based control (DOBC) approaches are fused together into the proposed controller to compensate the unknown dynamics uncertainties of the slave robot as well as environmental perturbation. Experiments and simulations are conducted to demonstrated the effectiveness of the proposed strategy

CloudProphet: A Machine Learning-Based Performance Prediction for Public Clouds

Computing servers have played a key role in developing and processing emerging compute-intensive applications in recent years. Consolidating multiple virtual machines (VMs) inside one server to run various applications introduces severe competence for limited resources among VMs. Many techniques such as VM scheduling and resource provisioning are proposed to maximize the cost-efficiency of the computing servers while alleviating the performance inference between VMs. However, these management techniques require accurate performance prediction of the application running inside the VM, which is challenging to get in the public cloud due to the black-box nature of the VMs. From this perspective, this paper proposes a novel machine learning-based performance prediction approach for applications running in the cloud. To achieve high accuracy predictions for black-box VMs, the proposed method first identifies the running application inside the virtual machine. It then selects highly-correlated runtime metrics as the input of the machine learning approach to accurately predict the performance level of the cloud application. Experimental results with state-of-the-art cloud benchmarks demonstrate that our proposed method outperforms the existing prediction methods by more than 2x in terms of worst prediction error. In addition, we successfully tackle the challenge in performance prediction for applications with variable workloads by introducing the performance degradation index, which other comparison methods fail to consider. The workflow versatility of the proposed approach has been verified with different modern servers and VM configurations.Comment: 15 pages, 11 figures, summited to IEEE Transactions on Sustainable Computin

Trajectory online adaption based on human motion prediction for teleoperation

In this work, a human motion intention prediction method based on an autoregressive (AR) model for teleoperation is developed. Based on this method, the robot's motion trajectory can be updated in real time through updating the parameters of the AR model. In the teleoperated robot's control loop, a virtual force model is defined to describe the interaction profile and to correct the robot's motion trajectory in real time. The proposed human motion prediction algorithm acts as a feedforward model to update the robot's motion and to revise this motion in the process of human-robot interaction (HRI). The convergence of this method is analyzed theoretically. Comparative studies demonstrate the enhanced performance of the proposed approach

Cooperative manipulation of deformable objects by single-leader-dual-follower teleoperation

This paper proposes a method for singleleader-dual-follower (SLDF) teleoperation, where one robot (direct-follower robot, DFR) is directly teleoperated and the other robot (assisting-follower robot, AFR) can autonomously cooperate with DFR to hold and move a deformable object, with the contact force regulated to a desired value. Since AFR does not know its partners movement, firstly, it achieves position alignment with DFR by using the contact force. Secondly, we develop an adaptive movement estimation algorithm according to Lyapunov theory, such that DFRs position is estimated in the presence of dynamic uncertainties. Finally, we adopt the impedance control to generate a reference trajectory for AFR to track, in order to maintain the desired contact force. Several simulations are conducted on a platform with two threedegrees-of-freedom (3-DOFs) robots. Experiments are performed with a dual-arm robot (Baxter), results of which demonstrate the feasibility and effectiveness of the proposed method