WP-DRnet: A novel wear particle detection and recognition network for automatic ferrograph image analysis

Ferrography plays an important role in wear analysis for machine condition monitoring, in which effective and efficient wear particle analysis is regarded as a crucial pre-requisite. An automatic wear particle detection and classification process is developed here using a cascade of two convolutional neural networks and a support vector machine (SVM) classifier. The neural networks are used for particle detection and recognition while particle classification is conducted in the SVM. This structure ensures that the computation expense is reduced and the accuracy is improved. The proposed network is verified using a large number of ferrograph images. Results show that high classification accuracies are obtained. Furthermore, the proposed approach can be further developed and applied in online machine condition monitoring applications. •A novel convolutional neural network, called the WP-DRnet, is developed for automatic wear particle detection, recognition and classification.•Wear particle analysis by deep learning algorithm can reduce the errors caused by hand-crafted features determined by human experts.•The WP-DRnet is constructed with a cascade of two convolutional neural networks and a multi-SVM classifier.•The WP-DRnet is applied directly on the captured ferrograph images to reduce the computation expense and improve accuracy.