Performance analysis of rotating machinery using enhanced cerebellar model articulation controller (E-CMAC) neural networks

For today's sophisticated machinery systems, on-line predictive maintenance has become a most reliable and cost effective method for machinery maintenance. An effective fault monitoring and diagnosis tool is able to recognize the characteristics, conditions, and developing trends of an operating machinery system, and is able to estimate fault severity of the system quantitatively. In order to meet the requirements, a new approach combining cerebellar model articulation controller (CMAC) neural networks and advanced vibration monitoring methods has been presented. A test rig consisting of two rotating hubs driven by a d.c. motor was used to produce different machine imbalance levels. A two-stage experiment is proposed. First, a CMAC network is trained with pre-defined imbalance conditions and a test data set is given to check the trained CMAC for imbalance severity prediction. Second, a CMAC network is trained by using a set of different imbalance levels and is then given an untrained condition to test the CMACs ability of imbalance severity interpolation. In addition, several potential capabilities of CMAC networks will be discussed and shown. The properties of the CMAC are notable. If implemented properly, CMAC can be used as an effective machinery condition monitor and fault severity estimation tool.

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