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Actuators
Special Issues
Fault Diagnosis and Prognosis in Actuators
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Fault Diagnosis and Prognosis in Actuators

A special issue of Actuators (ISSN 2076-0825). This special issue belongs to the section "Control Systems".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 2148

Special Issue Editors

Prof. Dr. Chao Zhang

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Guest Editor
Department of Integrated Technology and Control Engineering, School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, Shaanxi, China
Interests: system fault diagnosis and prognosis; testability design and safety analysis; deep learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhenbao Liu

E-Mail Website
Guest Editor
School of Civil Aviation, Northwestern Polytechnical University, Xi’an 710072, China
Interests: system fault diagnosis and prognosis; flight control system; unmanned aerial vehicle
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jie Chen

E-Mail Website
Guest Editor
School of Civil Aviation, Northwestern Polytechnical University, Xi’an 710072, China
Interests: system fault diagnosis and prognosis; testability design and safety analysis; flight control and actuator design
Dr. Qingbin Gao

E-Mail Website
Guest Editor
School of Mechanical Engineering and Automation, Harbin Institute of Technology (Shenzhen), Shenzhen 518000, China
Interests: time-delay system; servo control system; robot modeling and control; robot teleoperation

Special Issue Information

Dear Colleagues,

Various novel types of actuators are widely being applied in fields such as aerospace, marine, automotive, energy and power. As the primary components of systems, their long-term reliable operation is crucial; however, actuator faults are common in practical systems, which significantly affect quality and efficiency, potentially leading to serious safety incidents. Therefore, the diagnosis and prognosis of actuator faults is becoming increasingly important yet challenging.

This Special Issue aims to consolidate the latest research and developments in fault diagnosis and prognosis in actuators. We will explore innovative methodologies and technologies to be used for real-time fault detection, rapid fault isolation, accurate fault identification, efficient health management, and predictive maintenance and the application of artificial intelligence in fault diagnosis and prognosis.

We welcome contributions that explore theoretical models, computational studies, experimental validations, and practical implementations across various complex engineering domains. We encourage the submission of papers on a broad range of issues, including, but not limited to, the following:

  • The fault detection of various actuators;
  • The fault isolation of different actuators;
  • The fault identification of various actuators;
  • Fault diagnosis and prognosis based on signal processing;
  • Fault diagnosis and prognosis based on theoretical models;
  • Data-driven fault diagnosis and prognosis;
  • Machine learning and artificial intelligence in fault diagnosis and prognosis;
  • Typical application cases.

Prof. Dr. Chao Zhang
Prof. Dr. Zhenbao Liu
Prof. Dr. Jie Chen
Dr. Qingbin Gao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Actuators is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • actuators
  • fault diagnosis
  • fault prognosis
  • fault detection
  • fault isolation
  • fault identification
  • signal processing
  • data-driven
  • machine learning
  • artificial intelligence
  • health management 
  • predictive maintenance
 

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

33 pages, 12471 KB  
Article
Neural Network-Augmented Actuation Control System Designed for Path Tracking of Autonomous Underwater-Transportation Systems Under Sensor and Process Noise
by Faheem Ur Rehman, Syed Muhammad Tayyab, Hammad Khan, Aijun Li and Paolo Pennacchi
Actuators 2026, 15(5), 246; https://doi.org/10.3390/act15050246 - 30 Apr 2026
Viewed by 218
Abstract
Underwater-transportation systems have significant potential for both military and commercial applications. Neural Network (NN)-based control offers enhanced robustness for actuators to manage the states of autonomous underwater-transportation systems which include Rigid-Connection Transportation Systems (RCTSs), Flexible-Connection Transportation Systems (FCTSs) and Leader–Follower-Formation Control Transportation Systems [...] Read more.
Underwater-transportation systems have significant potential for both military and commercial applications. Neural Network (NN)-based control offers enhanced robustness for actuators to manage the states of autonomous underwater-transportation systems which include Rigid-Connection Transportation Systems (RCTSs), Flexible-Connection Transportation Systems (FCTSs) and Leader–Follower-Formation Control Transportation Systems (LFFCTSs). In this study, NN-Augmented Control (NNAC) is applied to the aforementioned three transportation systems to enable accurate path tracking by the actuators installed onboard these systems under both ideal operating conditions and in the presence of sensor and process noise. The Extended Kalman Filter (EKF) is employed to estimate the system states under noisy conditions. The results demonstrate that NNAC provides robust and adaptive control of actuators, achieving efficient trajectory tracking via the transportation systems despite the influence of sensor and process noise disturbances. NNAC predominance was also observed in comparison with the conventional PID controller. Among the transportation configurations under the NNAC strategy, the RCTS exhibited the highest tracking accuracy with the lowest power consumption by the actuators. The power consumption of actuators installed on the LFFCTS was marginally higher than that of the RCTS. However, the translational motion accuracy of the follower vehicle in the LFFCTS was the lowest due to indirect actuation control through the formation controller. In contrast, actuators in the FCTS showed the highest power consumption while motion accuracy was comparatively lowest, attributed to the increased complexity of its dynamic positioning requirements. Full article
(This article belongs to the Special Issue Fault Diagnosis and Prognosis in Actuators)
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27 pages, 8918 KB  
Article
Fault Diagnosis of Portal Crane Gearboxes Based on Improved CWGAN-GP and Multi-Task Learning
by Yongsheng Yang, Zuohuang Liao and Heng Wang
Actuators 2026, 15(4), 223; https://doi.org/10.3390/act15040223 - 16 Apr 2026
Viewed by 549
Abstract
With increasing port automation and operational intensity, the gearboxes of gantry cranes widely used in bulk cargo terminals are prone to bearing and gear failures under prolonged heavy loads, intense vibrations, and complex operating conditions. Since fault samples often exhibit imbalanced distributions, this [...] Read more.
With increasing port automation and operational intensity, the gearboxes of gantry cranes widely used in bulk cargo terminals are prone to bearing and gear failures under prolonged heavy loads, intense vibrations, and complex operating conditions. Since fault samples often exhibit imbalanced distributions, this imposes two higher requirements on diagnostic methods—first, the ability to effectively address sample imbalance and, second, the capability to simultaneously identify multiple fault categories. To address these challenges, this paper proposes a joint diagnostic method integrating an improved Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (CWGAN-GP) and Multi-Task Learning (MTL). First, the modified CWGAN-GP performs conditional augmentation for minority fault classes, evaluating synthetic sample authenticity and diversity through multiple metrics. Subsequently, a multi-channel diagnostic network is constructed, in which vibration signals are fed into two parallel sub-networks: time–frequency features are extracted from the Short-Time Fourier Transform (STFT)-based time–frequency representations via a residual-block Convolutional Neural Network (CNN), while temporal features are captured from the raw time-domain signal using a Bidirectional Long Short-Term Memory (Bi-LSTM) with an attention mechanism. An attention fusion layer then integrates these two feature types, enabling joint classification of bearings and gears within a multi-task learning framework. Experimental validation on public gearbox datasets and port gantry crane gearbox datasets demonstrates that this method achieves an average diagnostic accuracy exceeding 97%. The proposed method reduces the impact of class imbalance, thereby improving the accuracy and stability of multi-task fault identification. Full article
(This article belongs to the Special Issue Fault Diagnosis and Prognosis in Actuators)
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31 pages, 4855 KB  
Article
Research on Hybrid Control Methods for Electromechanical Actuation Systems Under the Influence of Nonlinear Factors
by Xingye Ding and Yong Zhou
Actuators 2025, 14(11), 526; https://doi.org/10.3390/act14110526 - 29 Oct 2025
Cited by 1 | Viewed by 800
Abstract
With the comprehensive digitalization and electrification of aircraft, electromechanical actuation systems (EAS) have been increasingly applied. However, EAS are affected by various nonlinear factors, such as friction and mechanical backlash, which can compromise system stability and control accuracy, thereby reducing the operational lifespan [...] Read more.
With the comprehensive digitalization and electrification of aircraft, electromechanical actuation systems (EAS) have been increasingly applied. However, EAS are affected by various nonlinear factors, such as friction and mechanical backlash, which can compromise system stability and control accuracy, thereby reducing the operational lifespan of the EAS. This study focuses on these two nonlinear factors and proposes a hybrid control approach to mitigate their effects. In the speed loop of the EAS, a Super-Twisting sliding mode controller combined with a generalized proportional–integral observer (GPIO) is designed, while in the position loop, a hybrid controller integrating a radial basis function (RBF) neural network with sliding mode control is implemented. Leveraging the advantages of numerical analysis in SIMULINK and dynamic simulation in ADAMS, a co-simulation framework is established to evaluate the hybrid control algorithm under nonlinear effects. Furthermore, a control test bench for the control surface transmission system is constructed to analyze the dynamic and static performance of the system under different control strategies and input commands. The experimental results show that, compared with the PID control, the hybrid control method reduces the steady-state error and vibration amplitude of the step response displacement by 51% and 75%, respectively, and decreases the amplitude of speed fluctuations by 75%. For the sinusoidal response, the displacement lag is reduced by 76%, and the amplitude of speed fluctuations is reduced by 50%. Full article
(This article belongs to the Special Issue Fault Diagnosis and Prognosis in Actuators)
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