Bo Zhu^1,#, Enzhi Dong^1,#, Zhonghua Cheng^1,*, Xianbiao Zhan², Kexin Jiang¹, Rongcai Wang ³

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-26, 2026, DOI:10.32604/cmc.2025.069194 - 10 November 2025

Abstract With the increasing complexity of industrial automation, planetary gearboxes play a vital role in large-scale equipment transmission systems, directly impacting operational efficiency and safety. Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment, leading to excessive maintenance costs or potential failure risks. However, existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes. To address these challenges, this study proposes a novel condition-based maintenance framework for planetary gearboxes. A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals, which were then processed using a… More >

Szymon Piątek¹, Anna Dańska-Bidzińska^2,*, Paweł Derlatka², Bartosz Szymanowski³, Renata Duchnowska³, Aleksandra Zielińska⁴, Natalia Sawicka⁴, Aleksander Gorzeń⁵, Wojciech Michalski⁶, Mariusz Bidziński¹

Oncology Research, Vol.33, No.11, pp. 3405-3416, 2025, DOI:10.32604/or.2025.068609 - 22 October 2025

Abstract Objectives: Monitoring of Cancer Antigen 125 (CA125) during ovarian cancer (OC) maintenance treatment with poly(ADP-ribose) polymerase inhibitors (PARPis) may be insufficient when using Gynecologic Cancer Intergroup (GCIG) biochemical progression criteria. This study aimed to evaluate the usefulness of CA125 monitoring in detecting OC recurrence during PARPis maintenance treatment. Methods: This multicenter retrospective cohort study included patients with primary OC who achieved complete or partial response after first-line platinum-based chemotherapy followed by PARPis maintenance treatment. Progression was defined using Response Evaluation Criteria in Solid Tumors (RECIST) and GCIG biochemical criteria. New biochemical progression definitions, based on… More >

Acácio M. R. Amaral^1,2,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3825-3859, 2025, DOI:10.32604/cmc.2025.067179 - 23 September 2025

Abstract Power converters are essential components in modern life, being widely used in industry, automation, transportation, and household appliances. In many critical applications, their failure can lead not only to financial losses due to operational downtime but also to serious risks to human safety. The capacitors forming the output filter, typically aluminum electrolytic capacitors (AECs), are among the most critical and susceptible components in power converters. The electrolyte in AECs often evaporates over time, causing the internal resistance to rise and the capacitance to drop, ultimately leading to component failure. Detecting this fault requires measuring the… More >

Shuo Zhang, Hanwu He, Yueming Wu^*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 801-826, 2025, DOI:10.32604/cmc.2025.066540 - 29 August 2025

Abstract Virtual maintenance, as an important means of industrial training and education, places strict requirements on the accuracy of participant pose perception and assessment of motion standardization. However, existing research mainly focuses on human pose estimation in general scenarios, lacking specialized solutions for maintenance scenarios. This paper proposes a virtual maintenance human pose estimation method based on multi-scale feature enhancement (VMHPE), which integrates adaptive input feature enhancement, multi-scale feature correction for improved expression of fine movements and complex poses, and multi-scale feature fusion to enhance keypoint localization accuracy. Meanwhile, this study constructs the first virtual maintenance-specific… More >

Zongqi Li¹, Hongwei Zhao^2,*, Jianyong Guo²

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 345-357, 2025, DOI:10.32604/cmes.2025.066175 - 31 July 2025

Abstract Monitoring the condition of road infrastructure is crucial for maintaining its structural integrity and ensuring safe transportation. This study proposes a deep learning framework based on Temporal Convolutional Networks (TCN) integrated with Adaptive Parametric Rectified Linear Unit (APReLU) to predict future road subbase strain trends. Our model leverages time-series strain data collected from embedded triaxial sensors within a national highway, spanning August 2021 to June 2022, to forecast strain dynamics critical for proactive maintenance planning. The TCN-APReLU architecture combines dilated causal convolutions to capture long-term dependencies and APReLU activation functions to adaptively model nonlinear strain More >

Ricardo Dintén^*, Marta Zorrilla

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 239-265, 2025, DOI:10.32604/cmes.2025.065461 - 31 July 2025

Abstract Predictive maintenance often involves imbalanced multivariate time series datasets with scarce failure events, posing challenges for model training due to the high dimensionality of the data and the need for domain-specific preprocessing, which frequently leads to the development of large and complex models. Inspired by the success of Large Language Models (LLMs), transformer-based foundation models have been developed for time series (TSFM). These models have been proven to reconstruct time series in a zero-shot manner, being able to capture different patterns that effectively characterize time series. This paper proposes the use of TSFM to generate… More >

Jie Lin^*, Hongchi Shen, Tingting Pei, Yan Wu

Energy Engineering, Vol.122, No.6, pp. 2449-2463, 2025, DOI:10.32604/ee.2025.065163 - 29 May 2025

Abstract This study proposes a novel visual maintenance method for photovoltaic (PV) modules based on a two-stage Wiener degradation model, addressing the limitations of traditional PV maintenance strategies that often result in insufficient or excessive maintenance. The approach begins by constructing a two-stage Wiener process performance degradation model and a remaining life prediction model under perfect maintenance conditions using historical degradation data of PV modules. This enables accurate determination of the optimal timing for post-failure corrective maintenance. To optimize the maintenance strategy, the study establishes a comprehensive cost model aimed at minimizing the long-term average cost… More >

Mudasir Dilawar^*, Muhammad Shahbaz

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5091-5114, 2025, DOI:10.32604/cmc.2025.064090 - 19 May 2025

Abstract In the era of Industry 4.0, condition monitoring has emerged as an effective solution for process industries to optimize their operational efficiency. Condition monitoring helps minimize unplanned downtime, extending equipment lifespan, reducing maintenance costs, and improving production quality and safety. This research focuses on utilizing Bayesian search-based machine learning and deep learning approaches for the condition monitoring of industrial equipment. The study aims to enhance predictive maintenance for industrial equipment by forecasting vibration values based on domain-specific feature engineering. Early prediction of vibration enables proactive interventions to minimize downtime and extend the lifespan of critical… More >

Prashanth B. S^1,*, Manoj Kumar M. V.^2,*, Nasser Almuraqab³, Puneetha B. H⁴

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4979-4998, 2025, DOI:10.32604/cmc.2025.060881 - 19 May 2025

Abstract Ever since the research in machine learning gained traction in recent years, it has been employed to address challenges in a wide variety of domains, including mechanical devices. Most of the machine learning models are built on the assumption of a static learning environment, but in practical situations, the data generated by the process is dynamic. This evolution of the data is termed concept drift. This research paper presents an approach for predicting mechanical failure in real-time using incremental learning based on the statistically calculated parameters of mechanical equipment. The method proposed here is applicable… More >

Jie Zou^1,2, Ping Wu^2,*, Jianwei Chen³, Weijie Fan², Yidong Xu²

Structural Durability & Health Monitoring, Vol.19, No.3, pp. 441-471, 2025, DOI:10.32604/sdhm.2024.058864 - 03 April 2025

Abstract As an essential part of the urban infrastructure, underground utility tunnels have a long service life, complex structural performance evolution and dynamic changes both inside and outside the tunnel. These combined factors result in a wide variety of disaster risks during the operation and maintenance phase, which make risk management and control particularly challenging. This work first reviews three common representative disaster factors during the operation and maintenance period: settlement, earthquakes, and explosions. It summarizes the causes of disasters, key technologies, and research methods. Then, it delves into the research on the intelligent operation and More >