Fault detection and classification of motor bearings under multiple operating conditions.

The article presents a framework for fault detection and classification to monitor the condition of motor bearings under multiple operating conditions. The condition monitoring of motor bearings is crucial for failure prevention, as bearings are prone to failure in challenging working environments. Intelligent fault diagnosis methods driven by deep learning and model-based approaches have been widely adopted to address these concerns.

Design of a computationally efficient observer-based distributed fault detection and isolation scheme in second-order networked control systems.

This paper presents a novel directional observer-based fault detection and isolation scheme for second-order networked control systems (NCS). The directional unknown input observer (UIO) tool is exploited to study the problem of distributed fault detection and isolation (FDI). Two design schemes with global and partial/local network models are proposed to solve the distributed FDI problem.

Distributed fault detection and isolation in second order networked systems in a cyber-physical environment.

Modern industrial processes and cyber-physical systems (CPS) are prone to anomalies both due to cyber and physical perturbations. Cyber disturbances or attacks being more hazardous may give birth to a series of multiple coordinated faults. In order to detect and isolate such faults, this paper proposes a novel distributed fault detection and isolation scheme for second-order networked systems.