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Lots of recent deep learning based intelligent fault diagnosis methods of planetary gearbox have achieved satisfactory accuracy with balanced training samples. Nevertheless, the fault samples are generally far less than healthy samples in practical engineering, and the collected data samples usually contain lots of noise, making it difficult to achieve accurate fault diagnosis. In order to solve these problems, this paper proposes a new method called novel multi-scale dilated convolutional neural network with long short-term memory (CNN-LSTM). Firstly, a novel multi-scale dilated CNN is constructed using new dilated strategy to enrich the coverage of the fields of view and avoid the loss of original information, which could adequately mine the distinguishing features of small samples. Secondly, an adaptive weight unit combined with LSTM is designed to fuse the distinguishing features and improve their robustness to noise. Finally, to pay more attention to the small samples and easily confused samples, a new-type loss function called enhanced cross entropy is developed. The test and analysis of the planetary gearbox data sets prove that the proposed method shows better diagnosis performance than other comparison methods using unbalanced training samples.

This paper addresses active structural acoustic control of a flexible plate using piezoelectric actuators. The analytical model of a thin plate with simply supported boundary conditions is derived from Hamilton's principle and a control model represented by the transfer function is obtained. Optimal locations of the piezoelectric actuators are found such that it minimizes the radiated sound power at the farfield. With optimally designed actuators, an controller is designed by using the loop shaping design procedure to achieve robust acoustic control of the proposed system subjected to parameter variations and external disturbances. Control performance and robustness are presented in both frequency and time domains in order to demonstrate the effectiveness of the proposed approach. In addition, the comparison between the proposed robust controller and the conventional adaptive Filtered-x LMS algorithm is undertaken.

In this paper, a decentralized discrete variable structure control via mixed H2/H infinity design was developed. In the beginning, the H2-norm of output error and weighted control input was minimized to obtain a control such that smaller energy consumption with bounded tracking error was assured. In addition, a suitable selection of this weighted function (connected with frequency) could reduce the effect of disturbance on the control input. However, an output disturbance caused by the interactions among subsystems, modeling error, and external load deteriorated system performance or even brought about instability. In this situation, the H infinity-norm of weighted sensitivity between output disturbance and output error was minimized to attenuate the effect of output disturbance. Moreover, an appropriate selection of this weighted function (related to frequency) could reject the corresponding output disturbance. No solution of Diophantine equation was required; the computational advantage was especially dominated for low-order system. For further improving system performance, a switching control for every subsystem was designed. The proposed control (mixed H2/H infinity DDVSC) was a three-step design method. The stability of the overall system was verified by Lyapunov stability criterion. The simulations and experiments of mobile robot were carried out to evaluate the usefulness of the proposed method.