Research of Electrostatic Monitoring Signal Conditioning Technology
|School||Nanjing University of Aeronautics and Astronautics|
|Course||Traffic Information Engineering \u0026 Control|
|Keywords||electrostatic monitoring signal conditioning filtering feature extraction Labview|
As a new type of monitoring method, electrostatic monitoring technology was used in the aircraftengine condition monitoring field. It provides a new technical means for conditional based maintenance(CBM) and prognostic and health management (PHM). Through the application of the electrostatic mo-nitoring technique, aircraft engine early fault warning become possible. In the current stage, signalconditioning in the early period is the main bottleneck restricting problem of the electrostatic mon-itoring. Therefore, research on the electrostatic monitoring signal conditioning technology, exploresuitable signal filtering technology and related feature extraction technology has a vital significance.Based on the electrostatic monitoring theory and requirements for its signal process, we researchon the signal conditioning technology for the electrostatic signal. First, we focus on the characteristic ofthe electrostatic signal. In time domain, it put up a strongly impact performance. Besides, frequencydomain shows that the electrostatic signal often influenced by power interference and white noisesignal. On account of the he signal characteristics and noise characterization and the basis of theanalysis of mechanism, we clear the key point for electrostatic signal denoising process. Then, weresearch on the EMD decomposition. Through the use of its time-frequency features, this paper studyon the wavelet-HHT spectrum joint filtering method, provide the feasibility and the advantage of thismethod. Secondly, in view of traditional time domain signal features vulnerable to situations incentiveshortage in electrostatic monitoring,the entropy theory is introduced into electrostatic signalmonitoring field, for checking the equipment failure conditions. Experiment data shows that theentropy feature only sensitivity to abrasion loss. At the same time, the characteristics in the whole lifeunder the condition of changing trend is analyzed,the result provides the basis for future electrostaticsignal condition monitoring applied. Finally, the filtering method and feature extraction methods isrealized in Labview platform, on the basis, we build electrostatic signal monitoring platform. Thisplatform reach the target that real time data denoising, features monitoring and online time frequencyanalysis, provides a platform for further research on electrostatic monitoring technology.