Wind turbines are widely installed as the new source of cleaner energy production. Dynamic and random stress imposed on the generator bearing of a wind turbine may lead to overheating and failure. In this paper, a data-driven approach for condition monitoring of generator bearings using temporal temperature data is presented. Four algorithms, the support vector regression machine, neural network, extreme learning machine, and the deep belief network are applied to model the bearing behavior. Comparative analysis of the models has demonstrated that the deep belief network is most accurate. It has been observed that the bearing failure is preceded by a change in the prediction error of bearing temperature. An exponentially-weighted moving average (EWMA) control chart is deployed to trend the error. Then a binary vector containing the abnormal errors and the normal residuals are generated for classifying failures. LS-SVM based classification models are developed to classify the fault bearings and the normal ones. The proposed approach has been validated with the data collected from 11 wind turbines.

To maximize energy extraction, the nacelle of a wind turbine follows the wind direction. Accurate prediction of wind direction is vital for yaw control. A tandem hybrid approach to improve the prediction accuracy of the wind direction data is developed. The proposed approach in this paper includes the bilinear transformation, effective data decomposition techniques, long-short-term-memory recurrent neural networks (LSTM-RNNs), and error decomposition correction methods. In the proposed approach, the angular wind direction data is firstly transformed into time-series to accommodate the full range of yaw motion. Then, the continuous transformed series are decomposed into a group of subseries using a novel decomposition technique. Next, for each subseries, the wind directions are predicted using LSTM-RNNs. In the final step, it decomposed the errors for each predicted subseries to correct the predicted wind direction and then perform inverse bilinear transformation to obtain the final wind direction forecasting. The robustness and effectiveness of the proposed approach are verified using data collected from a wind farm located in Huitengxile, Inner Mongolia, China. Computational results indicate that the proposed hybrid approach outperforms the other single approaches tested to predict the nacelle direction over short-time horizons. The proposed approach can be useful for practical wind farm operations.

Compliant grasping is crucial for secure handling objects not only vary in shapes but also in mechanical properties. We propose a novel soft robotic gripper with decoupled stiffness and shape control capability for performing adaptive grasping with minimum system complexity. The proposed soft fingers conform to object shapes facilitating the handling of objects of different types, shapes, and sizes. Each soft gripper finger has a length constraining mechanism (an articulable rigid backbone) and is powered by pneumatic muscle actuators. We derive the kinematic model of the gripper and use an empirical approach to simultaneously map input pressures to stiffness control and bending deformation of fingers. We use these mappings to demonstrate decoupled stiffness and shape (bending) control of various grasping configurations. We conduct tests to quantify the grip quality when holding objects as the gripper changes orientation, the ability to maintain the grip as the gripper is subjected to translational and rotational movements, and the external force perturbations required to release the object from the gripper under various stiffness and shape (bending) settings. The results validate the proposed gripper's performance and show how the decoupled stiffness and shape control can improve the grasping quality in soft robotic grippers.

Soft robots are inherently compliant and have a solid potential for realizing human-friendly and safe robots. However, despite continued research highlighting the potential of soft robots, they remain primarily confined to laboratory settings. Inspired by spider monkeys' tails, this paper proposes a hybrid soft robot (HSR) design. We detail the design objectives and methodology to improve the controllable stiffness range and achieve independent stiffness and shape control. We extend the curve-parametric approach to obtain a kinematic model of the proposed HSR. We experimentally demonstrate that the proposed HSR has about a 100% stiffness range increase than a previous soft robot design with identical physical dimensions. In addition, we empirically map HSR's bending shape-pressure-stiffness and present an application example - a soft robotic gripper - to demonstrate the decoupled nature of stiffness and shape variations. Experimental results show that the proposed HSR can be successfully used in applications where independent stiffness and shape control is desired.

Soft robotic manipulators (SRM) are biologically inspired from animal appendages such as elephant trunks and octopuses’ arms. In contrast to traditional rigid robotic manipulators, SRMs are made of flexible material and generate motion through structural deformation. Thus, they more easily adapt to unstructured environments. Teleoperated SRMs can be used in spaces harmful or impractical to humans (i.e., nuclear radiation or minimally invasive surgical sites). Limited research is available on the factors that affect human performance during the teleoperation of SRMs. We present two Human-Robot Interfaces (HRI) and conduct user studies on an SRM in object handling tasks and assess the performance of teleoperators using spatial input devices and augmented reality-based input devices. The user interaction is quantified for two types of controllers (Direct and Indirect) in an immersive pick and place operation. A System Usability Scale (SUS) questionnaire is administered to assess the usability of each HRI. Results suggest that the users perform more effectively, make fewer errors using the Indirect Control HRI and participants rated the Indirect Control HRI as more usable regardless of the hardware device.

Snakes are a remarkable evolutionary success story. Numerous snake-inspired robots have been proposed over the years. Soft robotic snakes (SRS), with their continuous and smooth bending capability, can better mimic their biological counterparts' unique characteristics. Prior SRSs are limited to planar operation with a limited number of planar gaits. We propose a novel SRS with spatial bending ability and investigate snake locomotion gaits beyond the planar gaits of the state-of-the-art systems. We derive a complete floating-base kinematic model of the SRS and use the model to derive joint-space trajectories for serpentine and inward/outward rolling locomotion gaits. These gaits are experimentally validated under varying frequency and amplitude of gait cycles. The results qualitatively and quantitatively validate the proposed SRSs' ability to leverage spatial bending to achieve locomotion gaits not possible with current SRS designs.