  In this era of advanced manufacturing, it's now more crucial than ever to
diagnose machine faults as early as possible to guarantee their safe and
efficient operation. With the massive surge in industrial big data and
advancement in sensing and computational technologies, data-driven Machinery
Fault Diagnosis (MFD) solutions based on machine/deep learning approaches have
been used ubiquitously in manufacturing. Timely and accurately identifying
faulty machine signals is vital in industrial applications for which many
relevant solutions have been proposed and are reviewed in many articles.
Despite the availability of numerous solutions and reviews on MFD, existing
works often lack several aspects. Most of the available literature has limited
applicability in a wide range of manufacturing settings due to their
concentration on a particular type of equipment or method of analysis.
Additionally, discussions regarding the challenges associated with implementing
data-driven approaches, such as dealing with noisy data, selecting appropriate
features, and adapting models to accommodate new or unforeseen faults, are
often superficial or completely overlooked. Thus, this survey provides a
comprehensive review of the articles using different types of machine learning
approaches for the detection and diagnosis of various types of machinery
faults, highlights their strengths and limitations, provides a review of the
methods used for condition-based analyses, comprehensively discusses the
available machinery fault datasets, introduces future researchers to the
possible challenges they have to encounter while using these approaches for MFD
and recommends the probable solutions to mitigate those problems. The future
research prospects are also pointed out for a better understanding of the
field. We believe this article will help researchers and contribute to the
further development of the field.
