Algorithm

Residual

Engineering

Downtime

Operating system

Turbofan

Data mining

Computer science

Pooling

Pattern recognition (psychology)

Mathematics

Automotive engineering

Mathematical analysis

Artificial intelligence

Artificial neural network

Prognostics

Fuse (electrical)

Generalization

Convolutional neural network

Machine learning

Electrical engineering

Convolution (computer science)

Computers & Industrial Engineering

Remaining useful life prediction is crucial in smart manufacturing systems due to many advantages of early prognostics, i.e., downtime reduction, service time prolongation, ultimate work efficiency improvement, and cost-saving. However, the conventional methods highly depend on feature selection and extraction, the accuracy and generalization can not be guaranteed. Inspired by the development of deep learning, a new method is proposed. Firstly, a parallel one-dimensional convolutional neural network (CNN)and the pooling layer were developed to extract and fuse features from the multiple signals. The dilated convolution with the residual connection and attention mechanism were specially developed to deal with the features from the pooling layers. After that, the regression layer was designed to generate the remaining useful life (RUL). Moreover, to verify the prognostic performance of the proposed method, two experiments, including cutting tool wear prediction and turbofan engine RUL prediction, were conducted. The proposed model was compared with the related works, and the results showed that the new method was more robust and accurate than currently published methods.

A novel multi-scale CNN and attention mechanism method with multi-sensor signal for remaining useful life prediction