A Deep Neural Network-Based Feature Fusion for Bearing Fault Diagnosis.

This paper presents a novel method for fusing information from multiple sensor systems for bearing fault diagnosis. In the proposed method, a convolutional neural network is exploited to handle multiple signal sources simultaneously. The most important finding of this paper is that a deep neural network with wide structure can extract automatically and efficiently discriminant features from multiple sensor signals simultaneously.

A modified generic second order algorithm with fixed-time stability.

This paper introduces a modified second order sliding mode algorithm with fixed-time stability analysis based on the Lyapunov function approach. An existing second order sliding mode algorithm is generalized, which provides superior features on convergence rate, accuracy, and robustness against a class of perturbations. The performance of the proposed algorithm is compared with existing algorithms through designing observers first.

Prescribed performance adaptive finite-time control for uncertain horizontal platform systems.

This paper presents a new approach to the design of prescribed performance adaptive control for uncertain horizontal platform systems with the finite-time convergence. Following an appropriate performance function and error transformation, a new adaptive control law is proposed by using a novel integral non-singular terminal sliding mode surface. The proposed approach simultaneously guarantees that (i) the transient responses of the closed-loop system possess some advanced properties such as the existence of the prespecified lower bound of the convergence rate and of the pre-established upper bound of the maximum overshoot; and (ii) the finite-time convergence of the state trajectories/tracking errors to zero.