Qiang Pan

The aim of this study was to solve the frequently occurring rotor-stator rub-impact fault in aero-engines without causing a significant reduction in efficiency. We proposed a fault mitigation scheme, using shape memory alloy (SMA) wire, whereby the tip clearance between the rotor and the stator is adjusted. In this scheme, an acoustic emission (AE) sensor is utilized to monitor the rub-impact fault. An active control actuator is designed with pre-strained two-way SMA wires, driven by an electric current via an Arduino control board, to mitigate the rub-impact fault once it occurs. In order to investigate the feasibility of the proposed scheme, a series of tests on the material properties of NiTi wires, including heating response rate, ultimate strain, free recovery rate, and restoring force, were carried out. A prototype of the actuator was designed, manufactured, and tested under various conditions. The experimental result verifies that the proposed scheme has the potential to mitigate or eliminate the rotor-stator rub-impact fault in aero-engines.

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.

The distributed control architecture is expected to dominate the gas turbine engine (GTE) control systems in future, in which the sensors and actuators are connected to the controllers via a network. Hence, the control problem of network-enabled high-performance distributed engine control (DEC) becomes important in modern gas turbine control systems. Due to the properties of the network, the packet dropouts must be considered. This study introduces a distributed control system architecture based on a networked cascade control system (NCCS). Typical turboshaft engine-distributed controllers are designed based on the NCCS framework with a H∞ output feedback under random packet-dropouts. The sufficient robust mean-stable conditions are derived via the Lyapunov stability theory and linear matrix inequality approach. And the effectiveness of the presented method is illustrated through a series of simulations.

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.