Light convolutional neural network by neural architecture search and model pruning for bearing fault diagnosis and remaining useful life prediction

Abstract

Convolutional Neural Network (CNN) has been extensively used in bearing fault diagnosis and Remaining Useful Life (RUL) prediction. However, accompanied by CNN’s increasing performance is a deeper network structure and growing parameter size. This prevents it from being deployed in industrial applications with limited computation resources. To this end, this paper proposed a two-step method to build a cell-based light CNN by Neural Architecture Search (NAS) and weights-ranking-based model pruning. In the first step, a cell-based CNN was constructed with searched optimal cells and the number of stacking cells was limited to reduce the network size after influence analysis. To search for the optimal cells, a base CNN model with stacking cells was initially built, and Differentiable Architecture Search was adopted after continuous relaxation. In the second step, the connections in the built cell-based CNN were further reduced by weights-ranking-based pruning. Experiment data from the Case Western Reserve University was used for validation under the task of fault classification. Results showed that the CNN with only two cells achieved a test accuracy of 99.969% and kept at 99.968% even if 50% connections were removed. Furthermore, compared with base CNN, the parameter size of the 2-cells CNN was reduced from 9.677MB to 0.197MB. Finally, after minor revision, the network structure was adapted to achieve bearing RUL prediction and validated with the PRONOSTIA test data. Both tasks confirmed the feasibility and superiority of constructing a light cell-based CNN with NAS and pruning, which laid the potential to realize a light CNN in embedded systems.

Gearbox fault diagnosis method based on lightweight channel attention mechanism and transfer learning

“…It has found an increasingly wide utilization in fault diagnosis. In deep learning methods, convolutional neural network (CNN) utilizes the idea of weight sharing and local sense to decrease complexity and computational cost of network 12 , 13 . It has significant advantages 2D image classification.…”

Wear fault diagnosis in hydro-turbine via the incorporation of the IWSO algorithm optimized CNN-LSTM neural network

“…Convolutional neural networks (CNNs) have been widely used in rotating machinery fault diagnosis due to their powerful ability for automatic feature extraction 21 . The network has demonstrated excellent performance in recognizing the health condition of rotating machinery.…”

In-situ tool wear condition monitoring during the end milling process based on dynamic mode and abnormal evaluation

“…With the rapid development of machine learning technology, artificial intelligence (AI) based fault diagnosis and prediction have increasingly become an important strategy for equipment safety and service monitoring 12 . Via related intelligent algorithms, the data-driven diagnostic method can adaptively identify equipment operation status information from existing data without the need of prior knowledge for professional technicians 13 .…”