Aiming at fault visualization and automatic feature extraction, this paper presents a new and intelligent bearing fault diagnostic method by combining Symmetrized Dot Pattern (SDP) representation with Squeeze-and-Excitation enabled Convolutional Neural Network (SE-CNN) model. Graphical representations of bearing states are shown intuitively by using SDP method. Meanwhile, optimal parameters during SDP images generation are selected to enhance the image resolution for distinctly distinguishing different bearing states, and create corresponding bearing fault sample sets. To automatically and effectively extract SDP image features, the channel attention mechanism using squeeze-and-excitation (SE) network is integrated with the CNN network. The proposed SE-CNN based diagnostic framework has ability to assign certain weight to each feature extraction channel, and further enforce the bearing diagnosis model focusing on the major features, meanwhile reduce the redundant information. The final diagnosis task is realized by the Softmax classifier located behind the SE-CNN model. Experimental results prove that the proposed method not only achieves the classification rate over 99%, but also has better generalization ability and stability.

This letter reports a piezoelectric cantilever-pendulum design for multi-directional energy harvesting. A pendulum is attached to the tip of a piezoelectric cantilever-type energy harvester. This design aims at taking advantage of the nonlinear coupling between the pendulum motion in 3-dimensional space and the beam bending vibration at resonances. Experimental studies indicate that, under properly chosen parameters, 1:2 internal resonance can be induced, which enables the multi-directional energy harvesting with a single cantilever. The advantages of the design with respect to traditional piezoelectric cantilever are examined.

Accurately and intelligently identifying faults of the planetary gearbox is essential in the safe and reliable operation and maintenance of the mechanical drive system. Recently, fault diagnosis of planetary gearbox has acquired tremendous progress, especially with the rising popularity of deep learning (DL). However, most methods are standard supervised learning where the input is directly mapped to a fault type, and with strong feedback. Also, their learning ways are static and unlike human learning that gradually acquires knowledge by interaction with the environment. To a certain extent, these deficiencies reduce the generalization and intelligence level of DL-based fault diagnosis methods. Besides, due to harsh working conditions, signals acquired often have strong noise and nonlinear features, leading to relatively low accuracy if raw signals are used as the input directly. Thus, this article proposes a new fault diagnosis method based on time-frequency representation and deep reinforcement learning (DRL). We first define fault diagnosis as a sequential decision-making problem in the classification Markov decision process. Next, the vibration signals are converted to uniform-sized TF maps by synchro-extracting transform to enhance the robustness of feature representation. Finally, a diagnosis agent is built and trained in the framework of DRL to learn the optimal classification policy automatically. Experimental results show that this method not only achieves better generalization and stability with an overall accuracy of over 99.5% in single-speed load cases but also outperforms others in multiwork conditions.

This letter reports designs of adaptive metasurfaces capable of modulating incoming wave fronts of elastic waves through electromechanical-tuning of their cells. The proposed elastic metasurfaces are composed of arrayed piezoelectric units with individually connected negative capacitance elements that are online tunable. By adjusting the negative capacitances properly, accurately formed, discontinuous phase profiles along the elastic metasurfaces can be achieved. Subsequently, anomalous refraction with various angles can be realized on the transmitted lowest asymmetric mode Lamb wave. Moreover, designs to facilitate planar focal lenses and source illusion devices can also be accomplished. The proposed flexible and versatile strategy to manipulate elastic waves has potential applications ranging from structural fault detection to vibration/noise control.

Piezoelectric cantilevers are widely used in vibration energy harvesting. Simple cantilever-based harvesters are mostly unidirectional. In this article, we develop a cantilever-pendulum system that can harvest vibratory energy of excitations from an arbitrary direction. The new design consists of a traditional piezoelectric cantilever with a pendulum attached to the tip. It is shown analytically and experimentally that with proper parametric combination this system can induce modal energy interchange between beam vibration and pendulum motions due to 1:2 internal resonance, which ultimately yields multi-directional energy harvesting by a single cantilever. The underlying mechanisms of this design are analyzed in detail.

This letter reports a tunable acoustic prism featuring continuous beam steering for transverse waves at a single frequency. The proposed prism is comprised of arrayed piezoelectric unit-cells with individually connected inductive shunt circuits. Taking advantage of wave velocity shifting in the vicinity of the local resonant frequency of unit-cells, we can steer the harmonic transverse wave by adjusting the inductive loads, i.e., tuning the inductances. This beam steering mechanism is facilitated by add-on piezoelectric circuitry through electro-mechanical coupling, whereas the host medium is not altered. Our analysis shows that the propagation direction of an acoustic wave has a tuning range of more than 30°. This tunable prism can be used as an acoustic metamaterial for various devices operating at broadband frequencies.