Fog computing and convolutional neural network enabled prognosis for machining process optimization

Authors: Liang, Y.C., Li, W.D., Lu, X. and Wang, S.

Journal: Journal of Manufacturing Systems

Volume: 52

Pages: 32-42

ISSN: 0278-6125

DOI: 10.1016/j.jmsy.2019.05.003


Cloud enabled prognosis systems have been increasingly adopted by manufacturing industries. The effectiveness of the cloud systems is, however, crippled by the high latency of data transfer between shop floors and the cloud. To overcome the limitation, this paper presents an innovative fog enabled prognosis system for machining process optimization. The system functions include: (1) dynamic prognosis - Convolutional Neural Network (CNN) based prognosis is implemented to detect potential faults from customized machining processes. Pre-processing mechanisms of the CNN are designed for partitioning and de-noising monitored signals to strengthen the performance of the system in practical manufacturing situations; (2) an innovative fog enabled prognosis architecture for machining process optimization – it consists of a terminal layer, a fog layer and a cloud layer to minimize data traffic and improve system efficiency. Under the architecture, monitored signals during machining collected on the terminal layer are processed using the trained CNN deployed on the fog layer to efficiently detect abnormal situations. Intensive computing activities like training of the CNN and system re-optimization responding to detected faults are carried out dynamically on the cloud layer to leverage its computation powers. The system was validated in a UK machining company. With the system deployment, the efficiency of energy and production was improved for 29.25% and 16.50% on average. In comparison with a cloud system, this fog system achieved 70.26% reduction in the bandwidth requirement between shop floors and cloud, and 47.02% reduction in data transfer time. This research, sponsored by EU projects, demonstrates that industrial artificial intelligence can facilitate smart manufacturing practices effectively.

Source: Scopus