Research On The Time-frequency Analysis Method To Extract Early Fault Features Of Rotating Machinery

Vibration signals are usually very complex when the rotating mechanical equipment is running. After a variety of vibration factors are integrated, vibration signals of mechanical system that are gained are necessarily non-stationary non-linear multi-component signals, and different non-stationary characteristic also indicates different form of mechanical fault. At present, the time-frequency analysis technique is widely used in the mechanical fault feature extraction, but how to more accurately extract early (weak) fault features from signals is still a hot and difficult point of research of the discipline. Against this background,an intensive study is given to basic features of rotating machinery and early faults, multi-resolution analysis technique, Hilbert-Huang transform, multi-resolution EMD method, time and frequency domain average techniques, cyclostationarity theory, local mean decomposition-based time-frequency analysis method and other signal analysis and processing technologies, and to rotating machinery fault signal noise cancellation, demodulation, characteristic amplification technology, time-frequency representation and other feature extraction techniques.Conduct a review and compare of currently commonly-used time-frequency analysis tools, briefly introduce its development process and research status, and analyze characteristics and early diagnostic methods of rotating machinery early fault signals.Because of the cyclostationarity characteristic, vibration signals of rotating machinery are converted to a smooth cyclic domain to conduct an average treatment that can better extract characteristic signals to filter out interference. The time domain average is not only capable of better resolution of multiple periodic components to achieve multiple-fault detection in signals, and but is also capable of strengthening the energy of early (or weak) fault signals and making the characteristics more prominent. The EMD method of multi-resolution analysis, firstly bring forward firstly by me, is used to gain instantaneous frequency of signal; then conduct a frequency domain average treatment for signals, and use the gear drive fault detection experimental device to do experiments; results show that this method is effective and feasible.The time domain average is a more effective method of signal analysis and pretreatment method for extracting periodic signals directly related to the rotary frequency; but when the rotating machinery runs unsteadily, the method is out of work. In this article, I firstly analyze the cyclostationarity theory and the time domain average method, and in order to raise the time domain average diagnostic accuracy and extend its applicable scope, combine the fitting of cubic curve of instantaneous speed of gearbox input shaft with the re-sampling of signal to put forward time domain average extraction method of non-synchronous characteristic signals; experimental results show that the method is effective.Currently, there are multiple kinds of demodulation methods of rotating machinery fault vibration signals, but these methods are only applicable to single-component AM-FM signals. Compared with the EMD method, the local mean decomposition is a new emerging time-frequency analysis method, with many unique excellent features. In this article, I am based on the LMD method to analyze limitations of direct calculation of instantaneous frequency, and put forward a multi-component signal processing method that combines the LMD method with the energy operator demodulation. As the LMD method is similar to the EMD method, I am also based on the extreme value point to define the local mean function and the local envelop function, and similarly, the end effect existed. Based on analyzing the production source of LMD end effect, I bring forward an end effect treatment method that a data extension is achieved through the characteristic trend sine function method. At last, the experiment made by me shows that the LMD-based energy operator demodulation method can be effectively used in the early-fault diagnosis of rotating machinery, and that the end effect treatment method that is adopted is simple and effective.Virtual instrument technology are studied in this article. On the basis of the above-mentioned theoretical research results, a diagnosis instrument of early fault of gearbox is successfully developed. With excellent features of both virtual instrument and traditional hardware instrument as well as a powerful signal analysis capability, this instrument is applicable to scientific experiments and complex signal analysis in engineering. Accuracy and stability of instrument function are verified through a large number of simulation experiments and practical engineering applications.At the end of this article, I summarize the work done in this article, and give an outlook of next research direction.