| The automatic equipment as production tools in the modern iron and steel production enterprises, is undoubtedly most important and critical.And a large part of the iron and steel production facilities are low-speed and heavy-load equipment which is low-speed, heavy loaded and mainly has intermittent movement. This type of equipment has complex structure and is relatively high level of automation. Once there is a sudden failure, the maintenance is difficulty, maintenance costs are high, and it even interrupts the entire production, resulting in enormous economic losses. In order to detect the early weak fault signal in advance, the major research of this paper is the application of chaotic oscillator to detect weak sinusoidal signal.The main work of the subject includes the following:(1) Researched the theory and application of chaotic oscillator in weak signal detection. Introducted auto-correlation method to study how to detect the amplitude of the signal using Duffing oscillator, and proposed two improved relative detective methods.(2) In order to judge the Phase plane diagram quantitatively , Hu's moment invariants and Euclidean distance were researched by simulation. And weak characteristic signal were detected successfully using the two methods.(3) As the advantage of Hu's moment invariants, how to conduct the quantitative identification of chaotic Phase plane diagram with it was studied, and proposed the adaptive detection scheme. Its feasibility was verified by simulation and engineering data analysis.(4) As complicated modulation signal is always found in industrial production and it's difficult for tradional detection methods to find the hidden fault signal, the Duffing oscillator detective method combining with auto-correlation and demodulation method was researched since Duffing oscillator is sensitive to weak signal and is immunity to noise. Two engineering cases were analyzed with this method and the fault characteristic signal was extracted successfully. |
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