AI-Driven Prognostics for Centrifugal Pumps: A Review

Citation: Khalid, S.; Jo, S.-H.; Shah,
S.Y.; Jung, J.H.; Kim, H.S. Artificial
Intelligence-Driven Prognostics and
Health Management for Centrifugal
Pumps: A Comprehensive Review.
Actuators 2024,13, 514. https://
doi.org/10.3390/act13120514
Academic Editor: Zhuming Bi
Received: 30 October 2024
Revised: 22 November 2024
Accepted: 9 December 2024
Published: 10 December 2024
Copyright: © 2024 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
Artificial Intelligence-Driven Prognostics and Health
Management for Centrifugal Pumps: A Comprehensive Review
Salman Khalid 1, Soo-Ho Jo 1, Syed Yaseen Shah 2, Joon Ha Jung 2,* and Heung Soo Kim 1,*
1Department of Mechanical, Robotics, and Energy Engineering, Dongguk University, 30 Pil-dong 1 Gil,
Jung-gu, Seoul 04620, Republic of Korea; [email protected] (S.K.); [email protected] (S.-H.J.)
2Department of Industrial Engineering, Ajou University, Suwon 16499, Republic of Korea;
[email protected]
*Correspondence: [email protected] (J.H.J.); [email protected] (H.S.K.)
Abstract: This comprehensive review explores data-driven methodologies that facilitate the prog-
nostics and health management (PHM) of centrifugal pumps (CPs) while utilizing both vibration
and non-vibration sensor data. This review investigates common fault types in CPs, while placing a
specific emphasis on artificial intelligence (AI) approaches, including machine learning (ML) and
deep learning (DL) techniques, for fault diagnosis and prognosis. A key innovation of this review is
its in-depth analysis of cutting-edge methods, such as adaptive thresholding, hybrid models, and
advanced neural network architectures, aimed at accurately predicting the remaining useful life
(RUL) of CPs under varying operational conditions. This review also addresses the limitations and
challenges of the current AI-driven methodologies, offering insights into potential solutions. By
synthesizing these methodologies and presenting practical applications through case studies, this
review provides a forward-looking perspective to empower industry professionals and researchers
with effective strategies to ensure the reliability and efficiency of centrifugal pumps. These findings
could contribute to optimizing industrial processes and advancing health management strategies for
critical components.
Keywords: centrifugal pumps (CPs); fault diagnosis; prognostics; artificial intelligence; machine
learning; deep learning
1. Introduction
A centrifugal pump (CP) is a piece of mechanical equipment that transports a fluid
from one place to another. Since manufacturing facilities and power plants use fluids often,
CPs are widely used in many industrial sites [ 1 , 2 ]. A survey by an energy organization
underscored that electric motor-driven machines, particularly CPs, are responsible for
65% of the energy consumption in industry [ 3 ]. However, CPs are prone to mechanical
failure because they have rotational parts that interact with fluids. The features of the
fluids in the CPs vary, and the fluid demand fluctuates, which generates faults and failures
in the pumps [ 4 – 6 ]. Cavitation and hydraulic instability occur frequently, and system-
related concerns can be generated as well [ 7 – 9 ]. Additionally, inadequate maintenance,
insufficient lubrication, misalignment, imbalances, and seal leakages decrease the reliability
of CPs [10–14] . If the malfunctions of pumps are not managed in a suitable period of
time, these failures and faults will lead to significant maintenance costs, energy waste,
disruptions in manufacturing processes, and potential safety hazards [15].
Effective health monitoring for CPs is essential in maintaining the optimal performance
of the machines. Traditional maintenance strategies, which perform maintenance after
severe faults are identified, have limitations [ 16 , 17 ]. However, recent advancements in PHM
for engineering systems have been modifying the maintenance strategies for CPs [18–20] .
As the cost of data acquisition has been decreased, the continuous online monitoring of
CPs has facilitated proactive maintenance strategies, such as data-driven PHM.
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Generally, PHM for CPs can be classified into three methodologies: physics-based,
data-driven, and hybrid approaches [ 18 , 21 – 23 ]. The physics-based method uses physical
models, which are based on basic scientific principles. However, the physical modeling
of a complex system requires simplified theoretical assumptions. Therefore, real-world
complexities, such as inconsistent operating conditions, unexpected events, and interactions
between the fluid and structure, can reduce the accuracy of physical models [ 21 ]. Hence,
the physics-based method can be used in limited situations. In contrast, the data-driven
approach leverages real-time sensor data and advanced analytical techniques to identify
patterns of anomalies, which might have been missed by physics-based methods [ 24 , 25 ].
The data-driven strategy can consider various operating conditions and offer insights into
complex machine behaviors, whereas a significant amount of time and effort is required to
establish a complex physical model. By inferring the latent relationships between the data
and the pattern, the data-driven methodology can improve the maintenance performance
of pump systems [ 26 ]. Furthermore, the hybrid approach bridges the gap between physics-
based and data-driven methods. The hybrid technique can combine physical knowledge
and real-world insights [27–29].
The emergence of data-driven techniques has led to a significant shift in addressing
maintenance for CPs. By utilizing real-time sensor data and advanced algorithms, data-
driven methods provide valuable insights to enable a suitable decision, which helps to
detect faults in the early stages. In addition, using real data significantly increases the
prediction accuracy of RUL for CPs as well [ 5 , 30 – 32 ]. Despite the increase in the reliability
of CPs due to advanced data analysis techniques, the domain of data-driven fault diagnosis
and prognosis in CPs faces various challenges [ 33 – 35 ]. One of the challenges is that the data
from various sensors aggregate to a substantial volume. To handle such big data, efficient
data analysis techniques and large computational capacities are required. Moreover, the
quality of sensor data must be monitored closely, since an error in data acquisition can lead
to a misleading result. In addition, the integration of sensor data requires advanced data
fusion techniques [ 36 , 37 ]. Additionally, a robust prognosis model is needed to estimate the
RUL of a pump accurately. These challenges highlight the need for continuous research
and innovation to refine data-driven methodologies.
The PHM framework for CPs begins with data collection, as various sensors capture
real-time pump health information. After data collection, the acquired sensor data undergo
preprocessing to ensure a formatted and organized dataset. These refined data then go
through feature extraction, which extracts physical details about the behavior of the target
system. Following the feature extraction step, the focus shifts to fault diagnosis. In the
diagnosis phase, the anomalies and patterns are analyzed thoroughly to identify specific
fault mechanisms, which enables corrective actions in time. The final stage of the PHM
framework involves predicting the RUL of the CP using data-driven techniques. These
data-driven PHM steps enhance the ability to detect faults such as cavitation, misalignment,
imbalances, bearing faults, and impeller cracks. Moreover, if the RUL can be predicted
accurately, the maintenance schedule can be optimized to reduce the maintenance cost.
In short, efficient centrifugal pump operations can be possible. Figure 1illustrates the
key components of the data-driven PHM framework specifically designed for centrifugal
pumps. The framework begins with fault identification, followed by data acquisition using
various sensors, such as vibration, pressure, temperature, flow rate, and acoustic sensors.
The collected data are then subjected to preprocessing techniques, including denoising,
normalization, and standardization, to ensure data quality and reliability. Subsequently,
advanced ML and ML techniques, such as SVM, kNN, ANNs, CNNs, and RNNs, are
employed for fault diagnosis. Finally, the framework integrates RUL prediction to sup-
port predictive maintenance strategies and ensure the optimal operation of CPs. This
systematic approach highlights the significance of data-driven methodologies in modern
PHM systems.

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Actuators 2024, 13, x FOR PEER REVIEW 3 of 33
Figure 1. Data-driven PHM framework for centrifugal pumps.
The distinctive contribution of this review paper lies in the comprehensive coverage
of cuing-edge methodologies, including fault identication, fault diagnosis, and prog-
nosis for CPs. By highlighting advanced techniques, this paper presents novel insights to
transform pump maintenance strategies. Moreover, the in-depth exploration of RUL pre-
diction addresses a critical aspect of pump performance, providing a valuable resource
for practitioners and researchers. A key strength of this review is the inclusion of carefully
selected case studies, illustrating the application of the methodologies in real-world sce-
narios. These case studies were chosen based on their relevance to centrifugal pump fault
diagnosis and RUL prediction, the diversity in the methodological approaches (ML and
DL), the coverage of varied fault scenarios, such as cavitation and bearing wear, and their
validation using experimental or industrial data. Studies demonstrating signicant
Figure 1. Data-driven PHM framework for centrifugal pumps.
The distinctive contribution of this review paper lies in the comprehensive coverage
of cutting-edge methodologies, including fault identification, fault diagnosis, and prog-
nosis for CPs. By highlighting advanced techniques, this paper presents novel insights
to transform pump maintenance strategies. Moreover, the in-depth exploration of RUL
prediction addresses a critical aspect of pump performance, providing a valuable resource
for practitioners and researchers. A key strength of this review is the inclusion of care-
fully selected case studies, illustrating the application of the methodologies in real-world
scenarios. These case studies were chosen based on their relevance to centrifugal pump
fault diagnosis and RUL prediction, the diversity in the methodological approaches (ML
and DL), the coverage of varied fault scenarios, such as cavitation and bearing wear, and
their validation using experimental or industrial data. Studies demonstrating significant

Actuators 2024,13, 514 4 of 31
improvements in diagnostic accuracy and maintenance efficiency are emphasized to ensure
actionable insights.
2. Common Fault Types and the Significance of AI-Driven PHM in CPs
CPs play an essential role in various industrial applications by delivering fluids to
a target point. These systems are widely employed for their efficiency, simplicity, and
versatility in managing fluid transportation across diverse sectors, including water supply,
oil and gas, chemical processing, and more [ 38 ]. To comprehend the advancements in the
AI-driven PHM of CPs, this section describes the fundamentals of CPs and the common
fault types.
2.1. Common Fault Types of Centrifugal Pumps
Like other engineering machines, CPs are susceptible to various faults, despite their
robust design. A fault in a CP can decrease the performance and the usage life. Understand-
ing these common fault types is crucial in developing effective AI-driven PHM strategies.
Some of the most frequently encountered fault types of CPs include cavitation, impeller
faults, seal leakages, bearing faults, imbalanced operation, and overheating. Each fault
type is described below.
1. Cavitation: Cavitation occurs when the pressure of the fluid drops below its vapor
pressure, causing the formation and subsequent collapse of vapor bubbles within the
pump. Cavitation leads to the erosion of the impeller surfaces, decreased efficiency,
and potential mechanical damage [7,39].
2. Impeller Erosion and Corrosion: The impeller is exposed to the abrasive action of
the fluid being pumped, leading to erosion over time. Corrosion, often due to the
properties of the pumped fluid, can further degrade the impeller’s integrity [40,41].
3. Seal Leakage: Faulty seals can result in fluid leakage, leading to a decrease in efficiency,
environmental concerns, and potential safety hazards [14,42,43].
4. Bearing Wear: Bearings can experience wear and excessive force due to misalignment,
leading to increased friction, reduced efficiency, and mechanical failure [44–46].
5. Unbalanced Operation: Imbalanced forces within the pump can cause excessive
vibration, leading to the premature wear of components, structural damage, and a
decreased system lifespan [47,48].
6. Overheating: Prolonged operation at elevated temperatures can cause the degrada-
tion of the pump components and lubricants, potentially resulting in catastrophic
failures [49].
Understanding the fundamental aspects of CPs and the common fault types provide
the groundwork for the exploration of advancements in AI-driven PHM strategies, as
discussed in the subsequent sections.
2.2. Significance of AI-Driven PHM for CP
The integration of data-driven PHM into CPs represents a monumental advancement
that transcends traditional maintenance practices [ 50 ]. These AI-driven approaches offer a
list of benefits that elevate the reliable and efficient operation of CPs.
■ Early Fault Detection and Prevention: At the heart of the significance of AI-driven
PHM in CPs lies its ability to detect and prevent faults at their inception. By con-
tinuously collecting and analyzing real-time sensor data, the system can identify
subtle deviations from normal operating conditions. This early detection allows main-
tenance teams to address emerging issues before they escalate into major failures.
The result is reduced downtime, minimized production interruptions, and enhanced
operational continuity.
■ Predictive Maintenance: AI-driven PHM enables a shift from reactive or fixed-
schedule maintenance to predictive maintenance strategies. By leveraging historical
data and advanced analytics, the system can predict when maintenance is required

Actuators 2024,13, 514 5 of 31
based on the actual condition of the pump components. This optimization of main-
tenance schedules leads to efficient resource utilization, reduced maintenance costs,
and extended equipment lifespans. Moreover, predictive maintenance helps in elim-
inating unnecessary interventions, reducing downtime, and boosting the overall
operational efficiency.
■ Improved Operational Efficiency: The significance of AI-driven PHM becomes evident
in its contribution to operational efficiency. By closely monitoring and analyzing vari-
ous operational parameters, the system can identify inefficiencies and deviations from
optimal performance. This insight allows operators to fine-tune pump operations,
ensuring that they run at peak efficiency. Improved energy utilization, minimized
wear and tear, and optimized fluid flow contribute to overall operational cost savings
and increased productivity.
■ Tailored Decision-Making: AI-driven PHM empowers operators and maintenance
personnel with actionable insights. The wealth of information extracted from sensor
data and historical trends enables informed decision-making. Operators can make
real-time adjustments, maintenance teams can prioritize tasks based on urgency, and
managers can allocate resources effectively. This tailored decision-making enhances
the agility and responsiveness of operations, ensuring that actions align with the
specific needs and conditions of the centrifugal pump system.
■ Long-Term Equipment Health Management: By continually monitoring the health
of CPs and identifying potential issues, AI-driven PHM extends the lifespan of the
equipment. Addressing problems early prevents progressive deterioration and min-
imizes the occurrence of catastrophic failures. This not only reduces the need for
frequent replacements but also promotes sustainability by curbing waste and conserv-
ing resources. The key capabilities of AI-driven PHM are summarized in Table 1.
Table 1. Key capabilities of AI-driven PHM for centrifugal pump systems.
Key Capabilities Description Benefits
Early Fault Detection
and Prevention
Proactive identification and
prevention of potential
system faults
Minimizes downtime and
reduces repair costs
Predictive Maintenance
and Optimization
Anticipation of maintenance
needs for optimal system
performance
Enhances equipment lifespan
and efficiency
Improved Efficiency
Enhancement of system
performance and
resource utilization
Increases overall
operational efficiency
Tailored Decision-Making Customized decision support
based on real-time data
Enables precise and informed
decision-making
Long-Term Equipment Health
Continuous monitoring for
prolonged equipment lifespan
Ensures sustained and
reliable operation
3. AI-Driven Fault Diagnosis for Centrifugal Pumps
Fault diagnosis for CPs ensures the reliable operation of the pump by accurately
identifying the fault type. As various sensor data are used for fault diagnosis, artificial
intelligent (AI)-driven approaches have gained prominence for their ability to effectively
identify fault patterns in large datasets. This section delves into the utilization of AI-driven
fault diagnosis in CPs, encompassing ML and DL techniques.
3.1. Machine Learning-Based Fault Diagnosis
ML has become an essential tool in fault diagnosis for CPs [ 51 ]. ML methods leverage
historical sensor data and sophisticated algorithms to deliver robust AI-driven solutions