Citation: Ali, A.; Abdelhadi, A.
Condition-Based Monitoring and
Maintenance: State of the Art Review.
Appl. Sci. 2022, 12, 688. https://
doi.org/10.3390/app12020688
Academic Editors: João Carlos de
Oliveira Matias and Paolo Renna
Received: 18 December 2021
Accepted: 8 January 2022
Published: 11 January 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Review
Condition-Based Monitoring and Maintenance: State of the
Art Review
Ahad Ali
1
and Abdelhakim Abdelhadi
2,
*
1
A. Leon Linton Department of Mechanical, Robotics and Industrial Engineering, Lawrence Technological
University, Southfield, MI 48075, USA; sali@ltu.edu
2
Masters of Engineering Management Program, Department of Engineering Management, Prince Sultan
University, Riyadh 11586, Saudi Arabia
* Correspondence: abdelhadi@psu.edu.sa
Abstract:
Manufacturing firms face great pressure to reduce downtime as well as maintenance
costs. Condition-based maintenance (CBM) can be used to effectively manage operations and
maintenance by monitoring detailed machine health information. CBM policies and the development
of the mathematical models have been growing recently. This paper provides a review of the
theoretical and practical development in the field of condition-based maintenance and its current
advancements. Standard CBM platform could make it effective and efficient in implementation and
performance improvement.
Keywords: maintenance; condition-based monitoring; artificial intelligence
1. Introduction
Condition monitoring is the process of observing a set of parameters and/or variables
that indicate that the state of the system under investigation. It plays a significant role
in the maintenance, management, and sustainable operations of various sectors, such as
manufacturing industries [
1
–
4
], transportation [
5
,
6
], energy [
7
–
9
], natural resources [
10
–
13
],
both natural and human-made disasters [
14
,
15
], and healthcare [
16
,
17
]. In most cases,
sensors and/or micro-controllers are widely used to perform condition monitoring [
18
,
19
].
In particular, in this paper, our focus is to provide a comprehensive review on various
condition monitoring approaches and their applications in the maintenance in manufactur-
ing industries.
In terms of manufacturing industries, condition monitoring generally recommends
a set of maintenance requirements in the event of failure or malfunctioning of the vital
devices or equipment of the system concerned; thus, the entire process can also be termed
as condition-based maintenance (CBM) [
20
–
22
]. It can be viewed as (i) a mechanism of
preventive maintenance, thus effective in planning maintenance scheduling; (ii) a compre-
hensive tool for assessing both of the diagnostic and prognostic conditions; (iii) an assistive
method in configuring system requirements and also enhancing the capability of conduct-
ing regular evaluation and/or maintenance operations; and (iv) a technique to optimize
the operational availability of devices, equipment, and modules of various systems [
23
,
24
].
In general, it provides several advantages, such as (i) reducing downtime and maintenance
expense by eliminating unnecessary maintenance; (ii) providing an early failure detection
to increase asset availability, avoiding unnecessary downtime; (iii) supporting continu-
ous improvement ensuring accurate and consistent response to developing conditions;
(iv) providing better decision making for operations, engineering, and maintenance staff;
(v) providing integration of control, safety, and maintenance environments; (vi) facilitating
the opening of an operator’s time to manage assets; (vii) enabling an organization to turn
data into actionable and valuable information; (viii) evaluating the equipment activities
Appl. Sci. 2022, 12, 688. https://doi.org/10.3390/app12020688 https://www.mdpi.com/journal/applsci
