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Screw Life Prediction Based on Accelerometer and Compact Support Gaussian Fuzzy Neural Networks

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To evaluate accurately screw residual life in the process of machining operation, accelerometers are used to construct an on-line monitoring system, and relation between screw life and vibration signals is modeled by neural network. Two B&K 4321 three-way accelerometer and a B&K 4368 accelerometer are installed to monitor the changing trend of screw pair life. The empirical mode decomposition (EMD) and Hilbert-Huang transform (HHT) are introduced to process vibration signals. First, signals are separated into several intrinsic mode functions (IMFs) by using EMD. Then the features of each IMF can be obtained. Key features to screw life are selected according to correlation coefficient. Second, the relation between screw life and vibration features was built by Compact Support Gaussian Fuzzy Neural Networks (CSGFNN), which parameters are optimized by an adaptive learning algorithm. Finally, screw residual life could be given by proposed model. The experimental results show maximum error is 328 hours and minimum error is 49 hours; meet the need of active maintenance.
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Document Type: Research Article

Publication date: 2011-10-01

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