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Ferrography wear debris in lubricating oil contains abundant worthy information about the state of the machinery and equipment. In order to develop an online monitoring system based on condition maintenance and fault diagnosis, wear debris needs to be identified automatically. Through various tribological experiments, a dataset of seven kinds of wear debris was established. In this study, DenseNet121 was used as the base network to construct a DCNN model (FWDNet) using the transfer learning method. FWDNet obtained an accuracy of 90.15% through a 10-fold crossvalidation test. The results indicate that FWDNet and DCNN mode is suitable for the identification of wear debris and can be used in actual condition monitoring systems in the future.

FWDNet: A Novel Recognition Network for Ferrography Wear Debris Image Analysis