Research On Intelligent Diagnosis To Equipment Fault And Structural Crack Based On Vibration

The fault of mechanical main parts usually results in production breaking and waster, the severe fault even do harm to person. Plate and beam is the main element of naval architecture and ocean engineering. Crack damage of plates and beams is the main fault forms of structure. With more and more large structure appearing in our lives, if the crack were not diagnosis in time, the expanding crack would damage the whole structure in the end. Once the accidence occurred, it cannot be recovered. Naval architecture and ocean engineering includes both mechanical equipment and engineering structure, such as motor engine and deck. Fault of both structure and equipment might bring severe accident. To avoid accident in advance, fault diagnosis in time to both structure and equipment is necessary. Crack diagnosis to beam is more perfect than that of plate. Crack diagnosis to plate will be emphasized in this dissertation. The diagnosis to crack is based on vibration without considering the stress influence from the crack tip.Spectral analysis based on vibration theory is practical in engineering. Fault of screw compressor rotor is analyzed based on vibration spectrum analysis. The fault frequency range is at a higher range from the fundamental rotating frequency. Diagnose the rotor primarily and get an effective result.FEM numerical simulation to cracks with different location, different length,different depth and different angle is taken. Mode tangent angle variance ratio ( Atga)of the cantilever plate in two directions is gotten from the beam mode slope variance ratio.Naval architecture and ocean engineering, such as oceanographic platform, is a complex structure with random load and actuator. Its underwater part is difficult to be inspected. It is necessary to make a good choice with appropriate diagnosis index. Because it is difficult to measure the strain by pasting strain gauge under the water, mode variance for cracks is measured on the platform. Diagnose the crack using the vibration information of mode and frequency variances. Five diagnosis indexes areused in this dissertation. &tga is the most sensitive index after researching ondifferent cracks. Influence both to natural frequency and Atga by different cracks isanalyzed. Closer to the clasped edge, longer crack, lager angle crack, and deeper crack can all bring bigger influence to the dynamic features.The fault mode of screw compressor is usually shown as the faults of rotors. There have no specific fault modes of rotor by far. Consult with the fault modes of rotating mechanics and bevel wheel, the fault modes of screw compressor rotor are founded together under the practical engineering. Consult the fuzzy neural networksfault mode under five times fundamental frequency, apply fuzzy neural networks fault diagnosis to a higher nine frequencies range, which begins from the fundamental frequency to the 28 times frequency. When diagnose crack, divide five diagnosis feature into three groups. Make three groups diagnosis features as the FNN input to diagnose the crack's location, length, depth and angle.Fuzzy neural networks (FNN) diagnosis to screw compressor using noise signal is established. FNN&DF diagnosis mode with vibration signal and noise single is presented. Vibration signal and noise signal from different data resource are data fused when diagnose the screw compressor rotor.Based on the multi sensor data fusion model and established FNN&DF diagnosis model to diagnose cracks. Different crack diagnosis features from the same data resource are fused when the cantilever plate crack is diagnosed. The FNN&DF diagnosis precision is much more higher that of FUZZY NEURAL NETWORKS.Modal experiment of cracked cantilever is carried out. A database of the relation between 17 kinds of cracked plates and mode and natural frequency is established. So did the intact plate. FNN&DF diagnosis to the actual cracked cantilever aluminum plate. Compared the diagnosis result with the actual cracked plates. It is improved that FNN&DF diagnos...