This paper presents a hybrid model-based fault diagnosis system to distinguish different types of faults and provide accurate fault information under complex fault situations. In the proposed system, a bank of hybrid fault observers (HFOs) is designed for the actuator fault detection and sensor fault location, and a nonlinear fault-tolerance estimator (NFTE) is built for the actuator fault estimation. In addition, a switching structure is designed to vary the working process between banks of HFOs and the NFTE. To prove the effectiveness of the proposed approach, a series of simulations was conducted based on a nonlinear aeroengine model. The results verified that the proposed hybrid diagnosis system is capable of locating the sensor fault, detecting the actuator faults, and estimating the actuator faults effectively.

The beamforming method is capable of localizing the acoustic emission source in a large-scale structure but its accuracy relies strongly on the assumed propagation speed and it is quite time consuming to apply in online monitoring. This paper proposes a fast beamforming method to localize an acoustic emission source in a thin-walled structure with unknown wave speed. Firstly, the Bartlett beamforming method (BBM) is introduced into broadband Lamb wave signal processing to develop an L-shape array-based damage source localization method for a thin-walled structure. Secondly, the fast Bartlett beamforming method (FBBM) is proposed based on the characteristics of BBM. Finally, the pencil-lead break test is carried out to validate the proposed method. The test results show that the FBBM can accurately localize the damage source by any given probable wave speed much more rapidly than traditional delay-and-sum beamforming.

Flywheel bearing is a key mechanical part of a satellite. Its health plays important role in fatigue life of the satellite. However, it is rather difficult to diagnose the health state of the bearings due to the complex satellite system. This study attempts to propose a three-direction (3D) correlation dimension method to diagnose bearings of a satellite flywheel at three typical states based on K-medoids clustering technology. A set of spatial spheres representing different bearings are modeled to recognize these three states of bearings. To avoid misdiagnosis or loss of the bearing state, a twice-cluster scheme is employed. A series of tests are carried out to observe the effectiveness of the proposed method. The result shows that the proposed method is capable of diagnosing different states of bearings and its accuracy is higher than 99% at given conditions.