Blade tip clearance is one of the important parameters affecting the performance, safety and stability of a gas turbine engine. However, it is difficult to measure the tip clearance in real time and accurately during the development and test process of an engine. In order to promote the development of tip clearance–measuring technology and the optimal design of the gas turbine engine, some typical measuring methods of tip clearance and a novel measuring method based on AC discharge are introduced. In this article, the significance for measuring tip clearance of an engine is illustrated first. Then, operating principles, characteristics and developments of those typical measurement approaches are introduced. After that, these methods are analyzed, and the particular characteristic of each measuring approach is summarized.

Almost all of the wear debris generated during the operation of the machine is suspended in the circulating lubricating oil. The analysis of the wear debris in the lubricating oil can effectively monitor the wear state of the machine and provide early warning of failures. An overview on inductive sensors for measuring wear debris in lubrication is introduced. To begin with, the significance of analyzing the wear debris in lubricating oil is explained and the working principle of the inductive wear sensors is illustrated. Furthermore, the development of inductive wear sensors and the key limitations are summarized. Finally, some rest factors affecting the sensor and the processing method of the induction signal aliasing are discussed, and the future development trend is prospected. It is pointed out that developing high sensitivity wear debris inductive sensors, increasing sensor throughput, and solving the problem of aliasing of detection signals are the following issues that should be further studied in the future.

Sensor fault diagnosis is necessary to ensure the normal operation of a gas turbine system. However, the existing methods require too many resources and this need can’t be satisfied in some occasions. Since the sensor readings are directly affected by sensor state, sensor fault diagnosis can be performed by extracting features of the measured signals. This paper proposes a novel fault diagnosis method for sensors based on wavelet entropy. Based on the wavelet theory, wavelet decomposition is utilized to decompose the signal in different scales. Then the instantaneous wavelet energy entropy (IWEE) and instantaneous wavelet singular entropy (IWSE) are defined based on the previous wavelet entropy theory. Subsequently, a fault diagnosis method for gas turbine sensors is proposed based on the results of a numerically simulated example. Then, experiments on this method are carried out on a real micro gas turbine engine. In the experiment, four types of faults with different magnitudes are presented. The experimental results show that the proposed method for sensor fault diagnosis is efficient.

Due to the poor working conditions of an engine, its control system is prone to failure. If these faults cannot be treated in time, it will cause great loss of life and property. In order to improve the safety and reliability of an aero-engine, fault diagnosis, and optimization method of engine control system based on probabilistic neural network (PNN) and support vector machine (SVM) is proposed. Firstly, using the German 3 W piston engine as a control object, the fault diagnosis scheme is designed and introduced briefly. Then, the fault injection is performed to produce faults, and the data sample for engine fault diagnosis is established. Finally, the important parameters of PNN and SVM are optimized by particle swarm optimization (PSO), and the results are analyzed and compared. It shows that the engine fault diagnosis method based on PNN and SVM can effectively diagnose the common faults. Under the optimization of PSO, the accuracy of PNN and SVM results are significantly improved, the classification accuracy of PNN is up to 96.4%, and the accuracy of SVM is up to 98.8%, which improves the application of them in fault diagnosis technology of aero-piston engine control system.