Deep learning-based approaches for diagnosing bearing faults have attracted considerable attention in the last years. However, in real-world applications, these methods face challenges. For proper training of these models, a considerable amount of labeled data are necessary, and due to limitations in industry, obtaining this amount of data may not be possible. Because of load variations, the distribution of training and test data may vary, which reduces the accuracy of the trained model for various working conditions. Furthermore, noise has a significant impact on bearing fault diagnosis performance in real-world industrial applications. This study introduced the deep subdomain adaptation convolutional neural network (DSACNN) method to overcome these challenges in real scenarios. The local maximum mean discrepancy (LMMD) method reduces the difference between each class distribution in the source and target domains. We validated our proposed method by CWRU bearing dataset under various loads and noise with different SNRs. The results show that DSACNN outperforms other comparative methods in anti-noise performance and reduction of domain distribution discrepancies.

In this study, first a model predictive control (MPC) solution is utilized to control the quadrotor model in both fault-free and faulty modes by employing a cost function. For this aim, the obtained non-linear continuous model has been discretized after linearization around an equilibrium point. Then, a fault detection and diagnosis (FDD) and fault tolerant control (FTC) strategy is proposed based on MPC in order to neutralize the effect of fault. The method of FDD and FTC designing is based on analytical redundancy relations (ARRs) which detects and tolerates the fault by producing a residual signal. Therefore, this method presents a complete FTC solution to fault issue without using observer. In addition, this solution has robustness against the uncertainties and disturbances which may challenge the model in practical situations. The discussed fault in this paper is a sensor fault which is modeled as an undesired function which affects the sensor of roll angle. However, the proposed FTC can tolerate all types of faults. The results of implementation of this method are validated under various simulation scenarios.

Quick and timely fault detection is of great importance in control systems reliability. Undetected faulty sensors could result in irreparable damages. Although fault detection and isolation (FDI) methods in control systems have received much attention in the last decade, these techniques have not been applied for some classes of nonlinear systems yet. This paper deals with the issues of sensor fault detection and isolation for a class of Lipschitz uncertain nonlinear system. By introducing a coordinate transformation matrix for states and output, the original system is first divided into two subsystems. The first subsystem is affected by uncertainty and disturbance. The second subsystem just has sensor faults. The nonlinear term is separated to linear and pure nonlinear parts. For fault detection, two sliding mode observers (SMO) are designed for the two subsystems. The stability condition is obtained based on the Lyapunov approach. The necessary matrices and parameters are obtained by solving the linear matrix inequality (LMI) problem. Furthermore, two sliding mode observers are designed for fault isolation. Finally, the effectiveness of the proposed approach is illustrated by simulation examples.