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Machines
Special Issues
Advanced Condition Monitoring and Predictive Maintenance for Mechatronic-Hydraulic Systems
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Advanced Condition Monitoring and Predictive Maintenance for Mechatronic-Hydraulic Systems

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Electrical Machines and Drives".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 5390

Special Issue Editors

Dr. Pengfei Liang

E-Mail Website
Guest Editor
School of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, China
Interests: industrial big data analysis; artificial intelligence algorithm; signal analysis and processing; machine health monitoring
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shaogan Ye

E-Mail Website
Guest Editor
Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen 361005, China
Interests: noise and vibration control in fluid power; artificial intelligence in fault diagnosis of fluid power components and systems
Special Issues, Collections and Topics in MDPI journals
Dr. Qun Chao

E-Mail Website
Guest Editor
State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: smart hydraulic components; health monitoring and intelligent maintenance; digital twins of electro-hydraulic systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As indispensable components in aerospace, intelligent manufacturing, and energy industries, mechatronic-hydraulic systems present distinctive challenges for condition monitoring and predictive maintenance. Their hybrid integration of mechanical, electronic, and hydraulic subsystems, coupled with multi-modal interactions under dynamic and often harsh operating conditions, necessitates innovative monitoring approaches that transcend traditional single-domain diagnostic techniques. This complexity demands advanced sensor fusion strategies, robust data-driven prognostic models, and adaptive maintenance frameworks capable of addressing cross-disciplinary failure mechanisms.

This Special Issue calls for original research submissions focusing on recent advances in diagnostic and prognostic technologies for mechatronic-hydraulic systems. We particularly welcome groundbreaking research that pioneers novel methodological frameworks or demonstrates the transformative real-world implementation of algorithms on complex engineering systems. Through this Special Issue, we aim to provide researchers and practitioners with valuable, cutting-edge knowledge while inspiring readers with promising new ideas and future research directions. High-quality submissions that bridge theory and practice are especially welcome.

Research topics that are of interest for this Special Issue include, but are not limited to, the following:

  • Advanced fault diagnosis, remaining life prediction, and maintenance decision methods for mechatronic-hydraulic systems or their subsystems;
  • Physics of failure modeling and digital twins;
  • Health monitoring sensors and multi-source data fusion;
  • Edge computing solutions for scalable predictive maintenance;
  • Managing uncertainty and risk in predictive maintenance;
  • Design and management of predictive maintenance platforms.

Dr. Pengfei Liang
Prof. Dr. Shaogan Ye
Dr. Qun Chao
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. Machines 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

  • mechatronic-hydraulic systems
  • condition monitoring
  • predictive maintenance
  • sensors
  • artificial intelligence
  • digital twin

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Published Papers (6 papers)

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Research

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21 pages, 6173 KB  
Article
Adaptive Digital Twin Framework for PMSM Thermal Safety Monitoring: Integrating Bayesian Self-Calibration with Hierarchical Physics-Aware Network
by Jinqiu Gao, Junze Luo, Shicai Yin, Chao Gong, Saibo Wang and Gerui Zhang
Machines 2026, 14(2), 138; https://doi.org/10.3390/machines14020138 - 24 Jan 2026
Cited by 21 | Viewed by 664
Abstract
To address the limitations of parameter drift in physical models and poor generalization in data-driven methods, this paper proposes a self-evolving digital twin framework for PMSM thermal safety. The framework integrates a dynamic-batch Bayesian calibration (DBBC) algorithm and a hierarchical physics-aware network (HPA-Net). [...] Read more.
To address the limitations of parameter drift in physical models and poor generalization in data-driven methods, this paper proposes a self-evolving digital twin framework for PMSM thermal safety. The framework integrates a dynamic-batch Bayesian calibration (DBBC) algorithm and a hierarchical physics-aware network (HPA-Net). First, the DBBC eliminates plant–model mismatch by robustly identifying stochastic parameters from operational data. Subsequently, the HPA-Net adopts a “physics-augmented” strategy, utilizing the calibrated physical model as a dynamic prior to directly infer high-fidelity temperature via a hierarchical training scheme. Furthermore, a real-time demagnetization safety margin (DSM) monitoring strategy is integrated to eliminate “false safe” zones. Experimental validation on a PMSM test bench confirms the superior performance of the proposed framework, which achieves a Root Mean Square Error (RMSE) of 0.919 °C for the stator winding and 1.603 °C for the permanent magnets. The proposed digital twin ensures robust thermal safety even under unseen operating conditions, transforming the monitoring system into a proactive safety guardian. Full article
(This article belongs to the Special Issue Advanced Condition Monitoring and Predictive Maintenance for Mechatronic-Hydraulic Systems)
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20 pages, 20362 KB  
Article
Node-Incremental-Based Multisource Domain Adaptation for Fault Diagnosis of Rolling Bearings with Limited Data
by Di Deng, Wei Li, Jiang Liu and Yan Qin
Machines 2026, 14(1), 71; https://doi.org/10.3390/machines14010071 - 6 Jan 2026
Cited by 2 | Viewed by 480
Abstract
Bearing fault diagnosis is essential for ensuring the safe and reliable operation of rotating machinery. However, accurate and timely fault identification with limited data remains a significant challenge. This study proposes a novel node-incremental-based multisource domain adaptation (NiMDA) approach for bearing fault diagnosis. [...] Read more.
Bearing fault diagnosis is essential for ensuring the safe and reliable operation of rotating machinery. However, accurate and timely fault identification with limited data remains a significant challenge. This study proposes a novel node-incremental-based multisource domain adaptation (NiMDA) approach for bearing fault diagnosis. The method employs a cloud model to adaptively extract fault-sensitive information while accounting for uncertainties across multiple wavelet packet decomposition levels. Subsequently, node incremental domain adaptation (NiDA) is used to construct a base classifier utilizing limited labeled data from both target and source domains. This approach reduces discrepancies between marginal and conditional distributions across different domain feature spaces during the node-increment process, resulting in a compact domain-adaptation structure. Robust diagnostic performance is achieved through parallel ensemble learning of NiDAs across multiple source domains. The experimental results demonstrate that NiMDA significantly outperforms state-of-the-art bearing fault diagnosis methods in few-shot scenarios, achieving improvements of 30.52%, 42.31%, 10.31%, 26.08%, 25.59%, and 7.98% over WDCNN, MCNN-LSTM, Bayesian-RF, DM-RVFLN, Five-shot, and ESCN, respectively, while maintaining satisfactory diagnostic speed. Full article
(This article belongs to the Special Issue Advanced Condition Monitoring and Predictive Maintenance for Mechatronic-Hydraulic Systems)
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25 pages, 1472 KB  
Article
Predicting Operational Reliability of the Directional Control Valves of the Hydraulic Press System Using Taguchi Method and Regression Analysis
by Borivoj Novaković, Mica Djurdjev, Luka Djordjević, Vesna Drakulović, Ljiljana Radovanović and Velibor Premčevski
Machines 2025, 13(12), 1124; https://doi.org/10.3390/machines13121124 - 7 Dec 2025
Cited by 1 | Viewed by 711
Abstract
This paper presents a study that investigates the operational reliability of directional control valves used in hydraulic press systems by applying the Taguchi method and regression analysis. The research focuses on key hydraulic parameters—kinematic viscosity, internal leakage, pressure, and temperature—to identify their influence [...] Read more.
This paper presents a study that investigates the operational reliability of directional control valves used in hydraulic press systems by applying the Taguchi method and regression analysis. The research focuses on key hydraulic parameters—kinematic viscosity, internal leakage, pressure, and temperature—to identify their influence on valve reliability. Three valves (DCV1–DCV3) were tested under identical conditions using an L8 orthogonal array to optimize the experimental design while maintaining statistical validity. The Taguchi analysis revealed that internal leakage is the dominant factor affecting valve reliability, consistently confirmed across all statistical evaluations, including signal-to-noise (S/N) ratios and ANOVA results. Regression models were developed for each valve to quantify the effect of each factor and showed excellent predictive accuracy (R2 > 98%). The study concludes that minimizing internal leakage, maintaining lower temperatures, and applying higher operating pressures significantly enhance valve reliability, while viscosity had negligible effect within the tested range. Valve DCV2 demonstrated the highest predicted reliability. These findings offer valuable insights for the optimization of hydraulic valve design and maintenance strategies, contributing to the improved performance and longevity of industrial hydraulic systems. Full article
(This article belongs to the Special Issue Advanced Condition Monitoring and Predictive Maintenance for Mechatronic-Hydraulic Systems)
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15 pages, 1542 KB  
Article
The Research on Multi-Objective Maintenance Optimization Strategy Based on Stochastic Modeling
by Guixu Xu, Pengwei Jiang, Weibo Ren, Yanfeng Li and Zhongxin Chen
Machines 2025, 13(8), 633; https://doi.org/10.3390/machines13080633 - 22 Jul 2025
Viewed by 1281
Abstract
The traditional approach that separates remaining useful life prediction from maintenance strategy design often fails to support efficient decision-making. Effective maintenance requires a comprehensive consideration of prediction accuracy, cost control, and equipment safety. To address this issue, this paper proposes a multi-objective maintenance [...] Read more.
The traditional approach that separates remaining useful life prediction from maintenance strategy design often fails to support efficient decision-making. Effective maintenance requires a comprehensive consideration of prediction accuracy, cost control, and equipment safety. To address this issue, this paper proposes a multi-objective maintenance optimization method based on stochastic modeling. First, a multi-sensor data fusion technique is developed, which maps multidimensional degradation signals into a composite degradation state indicator using evaluation metrics such as monotonicity, tendency, and robustness. Then, a linear Wiener process model is established to characterize the degradation trajectory of equipment, and a closed-form analytical solution of its reliability function is derived. On this basis, a multi-objective optimization model is constructed, aiming to maximize equipment safety and minimize maintenance cost. The proposed method is validated using the NASA aircraft engine degradation dataset. The experimental results demonstrate that, while ensuring system reliability, the proposed approach significantly reduces maintenance costs compared to traditional periodic maintenance strategies, confirming its effectiveness and practical value. Full article
(This article belongs to the Special Issue Advanced Condition Monitoring and Predictive Maintenance for Mechatronic-Hydraulic Systems)
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27 pages, 7068 KB  
Article
Semi-Supervised Fault Diagnosis Method for Hydraulic Pumps Based on Data Augmentation Consistency Regularization
by Siyuan Liu, Jixiong Yin, Zhengming Zhang, Yongqiang Zhang, Chao Ai and Wanlu Jiang
Machines 2025, 13(7), 557; https://doi.org/10.3390/machines13070557 - 26 Jun 2025
Cited by 2 | Viewed by 1082
Abstract
Due to the scarcity of labeled samples, the practical engineering application of deep learning-based hydraulic pump fault diagnosis methods is extremely challenging. This study proposes a semi-supervised learning method based on data augmented consistency regularization (DACR) to address the issue of lack of [...] Read more.
Due to the scarcity of labeled samples, the practical engineering application of deep learning-based hydraulic pump fault diagnosis methods is extremely challenging. This study proposes a semi-supervised learning method based on data augmented consistency regularization (DACR) to address the issue of lack of labeled data in diagnostic models. It utilizes augmented data obtained from the improved symplectic geometry modal decomposition method as additional perturbations, expanding the feature space of limited labeled samples under different operating conditions of the pump. A high-confidence label prediction process is formulated through a threshold determination strategy to estimate the potential label distribution of unlabeled samples. Consistent regularization loss is introduced in labeled and unlabeled data, respectively, to regularize model training, reducing the sensitivity of the classifier to additional perturbations. The supervised loss term ensures that the predictions of the augmented labeled samples are consistent with the true labels. Meanwhile, the unsupervised loss term can be used to minimize the difference between the distributions of unlabeled samples for different augmented versions. Finally, the proposed method is combined with Kolmogorov–Arnold Network (KAN). Comparative experiments based on data from two models of hydraulic pumps verify the superior recognition performance of this method under low label rate. Full article
(This article belongs to the Special Issue Advanced Condition Monitoring and Predictive Maintenance for Mechatronic-Hydraulic Systems)
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Review

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41 pages, 2153 KB  
Review
A Review of Domain-Adaptive Continual Deep Learning Remaining Useful Life Estimation for Bearing Fault Prognosis Under Evolving Data Distributions
by Stamatis Apeiranthitis, Christos Drosos, Avraam Chatzopoulos, Michail Papoutsidakis and Evangellos Pallis
Machines 2026, 14(4), 412; https://doi.org/10.3390/machines14040412 - 8 Apr 2026
Viewed by 352
Abstract
Estimating remaining useful life (RUL) and predicting bearing faults based on data-driven models have become central components of modern Prognostics and Health Management (PHM) systems. Although deep learning models have demonstrated strong performance under controlled and stationary operating conditions, their reliability in real-world [...] Read more.
Estimating remaining useful life (RUL) and predicting bearing faults based on data-driven models have become central components of modern Prognostics and Health Management (PHM) systems. Although deep learning models have demonstrated strong performance under controlled and stationary operating conditions, their reliability in real-world industrial and marine environments is limited. In practice, operating conditions, sensor properties, and degradation mechanisms evolve continuously over time, leading to non-stationary and shifting data distributions that violate the assumptions of conventional static learning approaches. To address these challenges, two research areas have gained increasing attention: Domain Adaptation (DA), which aims to mitigate distribution discrepancies across operating conditions or machines, and Continual Learning (CL), which enables models to learn sequentially while mitigating catastrophic forgetting. However, existing studies often examine these paradigms in isolation, limiting their effectiveness in long-term deployments, where domain shifts and temporal evolution coexist. This paper presents a comprehensive and systematic review of data-driven methods for bearing fault prognosis and remaining useful life (RUL) prediction under evolving data distributions, adopting the framework of Domain-Adaptive Continual Learning (DACL). By jointly examining the DA and CL methods, this review analyses how these approaches have been individually and implicitly combined to cope with non-stationarity, knowledge retention, and limited label availability in practical PHM scenarios. We categorised existing methods, highlighted their underlying assumptions and limitations, and critically assessed their applicability to long-term, real-world monitoring systems. Furthermore, key open challenges, including scalability, robustness under sequential domain shifts, uncertainty handling, and plasticity–stability trade-offs, are identified, and research directions are outlined based on the identified limitations and practical deployment requirements of the proposed method. This review aims to establish a structured and critical reference framework for understanding the role of domain-adaptive CL in data-driven prognostics, clarifying current research trends, limitations, and open challenges in evolving data distributions. Full article
(This article belongs to the Special Issue Advanced Condition Monitoring and Predictive Maintenance for Mechatronic-Hydraulic Systems)
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