Fault Diagnosis Of Slewing Bearing Based On Chaotic Oscillator

With the continuous development of our economy and the improvement of thelevel of industrialization, Mechanical equipment began to play a more important role inreal life, and slewing bearing as a key component of mechanical equipment made agreat contribution to the development of the national economy. Slewing bearings aremostly used for low speed and heavy load situations, the signal is weak and easy tobe submerged under the normal signal, the fault diagnosis is always a difficult problem.In recent years, chaos theory as a new way to play a very good effect in the detection ofweak signals, in order to detect the study of chaotic system is suitable for weak signal,this paper studied on the slewing bearing fault diagnosis based on chaotic oscillatortheory.In this paper，the structure form motion characteristics of slewing bearings wereintroduced, and some typical faults of slewing bearings are summarized, thecharacteristics of frequency on the pitting fault was analyzed and calculated. A smallexcavator slewing bearing as the research object, combining with the workingcharacteristics of hydraulic excavator, build slewing bearing test bench and the signalacquisition system, the fault signals were collected in different speed. Before carryingout the fault diagnosis analysis of slewing bearing, slewing bearing is to analyze thestress and contact analysis, and important parameters on the contact stiffness arecalculated in detail, then using PRO-E and ADAMS analysis of3D modeling anddynamics on. In order to verify the correctness of the simulation model of slewingbearing for friction torque analysis through the comparison and simulation value thetheoretical value to verify the conclusion.Due to the lower speed and the larger bearing of slewing bearing, it is often easy to drown fault signal in the normal signal and noise signal among the traditional faultdiagnosis method is not applicable, it is necessary to use other methods of slewingbearing fault diagnosis. Firstly, the wavelet transform and Hilbert transform methods,decomposition and reconstruction of signals using wavelet transform, then analyzed thedemodulation and spectrum using Hilbert transforms, confirmed to contain the faultcharacteristic frequency experiment and simulation signal. After the introduction ofchaos theory and its analysis, and the fault signal by adding a new chaotic system-modified Rossler chaotic system, the phase diagram to determine whether changes ofslewing bearing fault, and through the calculation of the Lyapunov index toquantitatively determine the fault characteristic frequency and come to a conclusion.Finally, through the analysis of simulation data and experimental data to verify itscorrectness of each other, and proof modified Rossler chaotic system is feasible for thedetection of weak signal.