  With the rapid development of manufacturing industry, machine fault diagnosis
has become increasingly significant to ensure safe equipment operation and
production. Consequently, multifarious approaches have been explored and
developed in the past years, of which intelligent algorithms develop
particularly rapidly. Convolutional neural network, as a typical representative
of intelligent diagnostic models, has been extensively studied and applied in
recent five years, and a large amount of literature has been published in
academic journals and conference proceedings. However, there has not been a
systematic review to cover these studies and make a prospect for the further
research. To fill in this gap, this work attempts to review and summarize the
development of the Convolutional Network based Fault Diagnosis (CNFD)
approaches comprehensively. Generally, a typical CNFD framework is composed of
the following steps, namely, data collection, model construction, and feature
learning and decision making, thus this paper is organized by following this
stream. Firstly, data collection process is described, in which several popular
datasets are introduced. Then, the fundamental theory from the basic
convolutional neural network to its variants is elaborated. After that, the
applications of CNFD are reviewed in terms of three mainstream directions, i.e.
classification, prediction and transfer diagnosis. Finally, conclusions and
prospects are presented to point out the characteristics of current
development, facing challenges and future trends. Last but not least, it is
expected that this work would provide convenience and inspire further
exploration for researchers in this field.
