In recent years, machine learning techniques have been widely used to solve many problems for fault diagnosis. However, in many real-world fault diagnosis applications, the distribution of the source domain data (on which the model is trained) is different from the distribution of the target domain data (where the learned model is actually deployed), which leads to performance degradation. In this paper, we introduce domain adaptation, which can find the solution to this problem by adapting the classifier or the regression model trained in a source domain for use in a different but related target domain. In particular, we proposed a novel deep neural network model with domain adaptation for fault diagnosis. Two main contributions are concluded by comparing to the previous works: first, the proposed model can utilize domain adaptation meanwhile strengthening the representative information of the original data, so that a high classification accuracy in the target domain can be achieved, and second, we proposed several strategies to explore the optimal hyperparameters of the model. Experimental results, on several real-world datasets, demonstrate the effectiveness and the reliability of both the proposed model and the exploring strategies for the parameters.

In this paper, we present an adaptive prescribed-time control method for a class of nonlinear tele-robotic systems with actuator faults and position error constraints. Extended from prescribed-time stability, practically prescribed-time stability (PPTS) is proposed for the first time aiming at stability analysis and control synthesis of nonlinear systems with disturbance and uncertainty. We show that, under the control scheme in the framework of PPTS, the system states are guaranteed to converge to a user-defined set (physically realizable) within user-defined settling time (physically realizable). Based on PPTS, an adaptive fault-tolerant controller is developed by integrating a novel exponential-type Barrier Lyapunov function. Rigorous stability analysis based on back-stepping approach proves that, under the proposed control strategy, synchronization errors converge to a user-defined residual set within predefined settling time and never exceed the prescribed range. Universal performance indexes, including the settling time, residual-set, accuracy, and overshoot, can be user-defined and only dependent on fewer user-defined parameters. Simulation results illustrate the effectiveness of the developed control scheme.

Intelligent unmanned autonomous systems are some of the most important applications of artificial intelligence (AI). The development of such systems can significantly promote innovation in AI technologies. This paper introduces the trends in the development of intelligent unmanned autonomous systems by summarizing the main achievements in each technological platform. Furthermore, we classify the relevant technologies into seven areas, including AI technologies, unmanned vehicles, unmanned aerial vehicles, service robots, space robots, marine robots, and unmanned workshops/intelligent plants. Current trends and developments in each area are introduced.

Limited by operation time window and working space, the space teleoperation task needs to be completed within an expected time while ensuring that the end effector meets the physical constraints. Meanwhile, the interaction with unknown environments would cause uncertainty in the closed-loop system, which brings great challenges to the control design. To solve the above problems, the control performance issue for a class of space teleoperation systems subject to multiple constraints and interaction uncertainties is investigated in this paper. The force interaction with the human operator/space environment is represented by interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy systems, where the uncertain equivalent mass and damping parameters can be effectively described and captured by IT2 membership functions. In order to reduce the communication burden and satisfy the constraints of settling time, transient-state performance and operating space, a time-varying threshold event-triggered control scheme together with exponential-type Lyapunov function (EBLF) is developed for the first time. We show that, with the proposed controller, the synchronization tracking errors are guaranteed to converge to a user-defined residual set within pre-assigned settling time, and never exceed the prescribed range despite unknown control direction and actuator faults, which solves the long-standing constraint issue with more flexibility due to the fact that the related constraints can be arbitrarily specific within the physically available range. Moreover, the convergence set is only dependent on fewer user-defined parameters rather than approximation errors, which provides an effective analysis technique to deal with the difficulty that the convergence accuracy is difficult to calculate quantitatively in the presence of unknown disturbance. Detailed simulation results are provided to show the effectiveness and merit of the proposed control strategy.

As bearings are the most common components of mechanical structure, it will be helpful to research bearing fault and diagnose the fault as early as possible in case of suffering greater losses. This paper proposes a deep neural network algorithm framework for bearing fault diagnosis based on stacked autoencoder and softmax regression. The simulation results verify the feasibility of the algorithm and show the excellent classification performance. In addition, this deep neural network represents strong robustness and eliminates the impact of noise remarkably. Last but not least, an integrated deep neural network method consisting of ten different structure parameter networks is proposed and it has better generalization capability.