Bearing fault diagnosis has extensively exploited vibration signals because of their rich information about bearing health conditions. However, this approach is expensive because the measurement of vibration signals requires external accelerometers. Moreover, in machine systems that are inaccessible or unable to be installed external sensors, the vibration signal-based approach is impracticable. Otherwise, motor current signals are easily measured by the inverters which are available components of those systems. Therefore, the motor current-signal-based bearing fault diagnosis approach has attracted considerable attention from researchers. However, the performance of this approach is still not good as the vibration signal-based approach, especially in the case of fault diagnosis for external bearings (the bearings which installed outside of the electric motors). Accordingly, this paper proposes a motor current-signal-based fault diagnosis method utilizing deep learning and information fusion that can be applied to external bearings in rotary machine systems. The proposed method uses raw signals from multiple phases of the motor current as direct input, features are extracted from the current signals of each phase. Then each feature set is classified separately by a convolutional neural network. To enhance the classification accuracy, a novel decision-level information fusion technique is introduced to fuse information from all of the utilized convolutional neural networks. The problem of decision-level information fusion is transformed into a simple pattern classification task, which can be solved effectively by familiar supervised learning algorithms. The effectiveness of the proposed fault diagnosis method is verified through experiments carried out with actual bearing fault signals.

We propose a backstepping global fast terminal sliding mode control for trajectory tracking control of industrial robotic manipulators in this article. An integral of the global fast terminal sliding mode surface is firstly suggested to improve the dynamic performance and fast convergence of Sliding Mode Control (SMC) and Terminal SMC (TSMC), which also obtains a finite-time convergence. A controller is then developed from the proposed sliding surface using the backstepping control method and High-Order SMC (HOSMC) to ensure the global stability of the control system. Thanks to this proposed method, the controller provides small position and velocity control errors with less oscillation, smooth control torque, and convergence of the control errors in the short time. The stability and convergence also are guaranteed with Lyapunov theory. Finally, computer simulation verifies the effectiveness of the designed controller.

Bearing‐fault‐diagnosis problem can be conceived as a pattern recognition problem, which includes three main phases: feature extraction, feature selection and feature classification. Thus, to improve the performance of the whole bearing‐fault‐diagnosis system, the performance of each phase must be improved. The aim of this study is threefold. First, in the feature extraction step, a new feature extraction technique based on non‐local‐means de‐noising and empirical mode decomposition is developed to more accurately obtain fault‐characteristic information. Second, in the feature selection phase, a novel two‐stage feature selection, hybrid distance evaluation technique (DET)–particle swarm optimisation (PSO), is proposed by combining DET and PSO to select the superior combining feature subset that discriminates well among classes. Third, in the classification phase, a comparison among three types of popular classifiers: K‐nearest neighbours, probabilistic neural network and support‐vector machine is done to figure out the sensitivity of each classifier corresponding to the irrelevant and redundant features and the curse of dimensionality; then, find out a most suitable classifier incorporating with feature selection phase. The experimental results for the vibration signal of the bearing are shown to verify the effectiveness of the proposed fault‐diagnosis scheme.