Biblio

Nowadays, the detection of acoustical emission is widely used for fault diagnosis of gas insulated substations (GIS) in normal operation and factory tests, which is called 'non-conventional' method recommended in the standard IEC TS 62478-2016 and GIGRE D1.33 444. In this paper, to develop a data analyzer for acoustic detection (AD) system to make an assistant diagnosis for technical personnel or equipment operation and maintenance personnel, based on the previous research on the experimental research, pattern identification with phase compensation and the software development, the algorithm model design and its application is given in detail. For the acoustical emission signals (n, ti, qi), the BP artificial neural network optimized by genetic algorithm (GA-BP) is used as a classifier based on the fingerprint consisting of several statistic operators, which are derivate form typical 2D histograms of PRPD with identification with phase compensation (IPC). Experimental results show that the comprehensive algorithm model designed for identification is practical and effective.

At present, the acoustic emission (AE) method has the advantages of live measurement and easy fault location, so it is very suitable for insulation defect detection of power equipments such as GIS, etc. While the conventional AE detection system or instruments always can't give a right discrimination result, because them always work based on the reference voltage or phase information from an auxiliary 220V voltage signal source rather than the operation high voltage (HV) with the real phase information corresponding to the detected AE pulsed signals. So there is a random phase difference between the reference phase and operation phase. The discharge fingerprint formed by the detected AE pulsed signals with reference phase using the same processing process is compared to the discharge fingerprint database formed in the HV laboratory with the real phase information, therefore, the system may not be able to discriminate the discharge mode of the field measured data from GIS in substation operation. In this paper, in order to design and develop a general fast analysis algorithm model for PD acoustic detection system to make an assistant diagnosis, the pattern identification with phase compensation was designed and applied. The results show that the method is effective and useful to deatl with AE signals meased in operation situation.

The engine is a significant and dynamic component of the aircraft. Because of the complicated structure and severe operating environment, the fault detection of the engine has always been the key and difficult issue in the field of reliability. Based on an engine and the acoustic emission technology, we propose a method of identifying fault types and determining different components in the engine by constructing the attenuation coefficient. There are several common faults of engines, and three different types of fault sources are generated experimentally in this work. Then the fault signal of the above fault sources propagating in different engine components are obtained. Finally, the acoustic emission characteristics of the fault signal are extracted and judged by the attenuation coefficient. The work effectively identifies different types of faults and studies the effects of different structural components on the propagation of fault acoustic emission signals, which provides a method for the use of acoustic emission technology to identify the faults types of the engine and to study the propagation characteristics of AE signals on the engine.*