Bo Zhu^#, Enzhi Dong^#, Zhonghua Cheng^*, Kexin Jiang, Chiming Guo, Shuai Yue

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074009 - 10 February 2026

Abstract Accurate prediction of remaining useful life serves as a reliable basis for maintenance strategies, effectively reducing both the frequency of failures and associated costs. As a core component of PHM, RUL prediction plays a crucial role in preventing equipment failures and optimizing maintenance decision-making. However, deep learning models often falter when processing raw, noisy temporal signals, fail to quantify prediction uncertainty, and face challenges in effectively capturing the nonlinear dynamics of equipment degradation. To address these issues, this study proposes a novel deep learning framework. First, a new bidirectional long short-term memory network integrated with More >

Wei Chen, Yang Wu^*, Tingting Pei, Jie Lin, Guojing Yuan

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.070905 - 27 January 2026

Abstract In view of the insufficient utilization of condition-monitoring information and the improper scheduling often observed in conventional maintenance strategies for photovoltaic (PV) modules, this study proposes a predictive maintenance (PdM) strategy based on Remaining Useful Life (RUL) estimation. First, a RUL prediction model is established using the Transformer architecture, which enables the effective processing of sequential degradation data. By employing the historical degradation data of PV modules, the proposed model provides accurate forecasts of the remaining useful life, thereby supplying essential inputs for maintenance decision-making. Subsequently, the RUL information obtained from the prediction process is… More >

Atheer Aleran¹, Hanan Almukhalfi¹, Ayman Noor¹, Reyadh Alluhaibi², Abdulrahman Hafez³, Talal H. Noor^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.070741 - 12 January 2026

Abstract Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs. Conventional maintenance methods, such as reactive maintenance (i.e., run to failure) or time-based preventive maintenance (i.e., scheduled servicing), prove ineffective for complex systems with many Internet of Things (IoT) devices and sensors because they fall short in detecting faults at early stages when it is most crucial. This paper presents a predictive maintenance framework based on a hybrid deep learning model that integrates the capabilities of Long Short-Term Memory (LSTM) Networks and Convolutional Neural Networks (CNNs). The framework… 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 >

Yongfeng Tai¹, Xingyu Yan², Xiangyi Geng³, Lin Mu⁴, Mingshun Jiang², Faye Zhang^2,*

Structural Durability & Health Monitoring, Vol.19, No.2, pp. 365-383, 2025, DOI:10.32604/sdhm.2024.053998 - 15 January 2025

Abstract The remaining useful life prediction of rolling bearing is vital in safety and reliability guarantee. In engineering scenarios, only a small amount of bearing performance degradation data can be obtained through accelerated life testing. In the absence of lifetime data, the hidden long-term correlation between performance degradation data is challenging to mine effectively, which is the main factor that restricts the prediction precision and engineering application of the residual life prediction method. To address this problem, a novel method based on the multi-layer perception neural network and bidirectional long short-term memory network is proposed. Firstly,… More >

Liyao Yang¹, Hongyan Ma^1,2,3,*, Yingda Zhang¹, Wei He¹

Energy Engineering, Vol.122, No.1, pp. 243-264, 2025, DOI:10.32604/ee.2024.057500 - 27 December 2024

Abstract Accurately estimating the State of Health (SOH) and Remaining Useful Life (RUL) of lithium-ion batteries (LIBs) is crucial for the continuous and stable operation of battery management systems. However, due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance, direct measurement of SOH and RUL is challenging. To address these issues, the Twin Support Vector Machine (TWSVM) method is proposed to predict SOH and RUL. Initially, the constant current charging time of the lithium battery is extracted as a health indicator (HI), decomposed using Variational Modal Decomposition (VMD), and… More >

Sen-Hui Wang^1,2,*, Xi Kang¹, Cheng Wang¹, Tian-Bing Ma¹, Xiang He², Ke Yang^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1405-1427, 2024, DOI:10.32604/cmes.2024.049281 - 20 May 2024

Abstract Accurately predicting the remaining useful life (RUL) of bearings in mining rotating equipment is vital for mining enterprises. This research aims to distinguish the features associated with the RUL of bearings and propose a prediction model based on these selected features. This study proposes a hybrid predictive model to assess the RUL of rolling element bearings. The proposed model begins with the pre-processing of bearing vibration signals to reconstruct sixty time-domain features. The hybrid model selects relevant features from the sixty time-domain features of the vibration signal by adopting the RReliefF feature selection algorithm. Subsequently,… More >

Tiantian Liang^*, Runze Wang, Xuxiu Zhang, Yingdong Wang, Jianxiong Yang

Structural Durability & Health Monitoring, Vol.17, No.5, pp. 433-455, 2023, DOI:10.32604/sdhm.2023.029331 - 07 September 2023

Abstract In this study, an optimized long short-term memory (LSTM) network is proposed to predict the reliability and remaining useful life (RUL) of rolling bearings based on an improved whale-optimized algorithm (IWOA). The multi-domain features are extracted to construct the feature dataset because the single-domain features are difficult to characterize the performance degeneration of the rolling bearing. To provide covariates for reliability assessment, a kernel principal component analysis is used to reduce the dimensionality of the features. A Weibull distribution proportional hazard model (WPHM) is used for the reliability assessment of rolling bearing, and a beluga… More > Graphic Abstract

Ping-Huan Kuo^1,2, Ting-Chung Tseng¹, Po-Chien Luan², Her-Terng Yau^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.1, pp. 133-151, 2022, DOI:10.32604/cmes.2022.020350 - 18 July 2022

Abstract This work is focused on developing an effective method for bearing remaining useful life predictions. The method is useful in accurately predicting the remaining useful life of bearings so that machine damage, production outage, and human accidents caused by unexpected bearing failure can be prevented. This study uses the bearing dataset provided by FEMTO-ST Institute, Besançon, France. This study starts with the exploration of neural networks, based on which the biaxial vibration signals are modeled and analyzed. This paper introduces pre-processing of bearing vibration signals, neural network model training and adjustment of training data. The More >

S. Kalyani^*, K. Venkata Rao, A. Mary Sowjanya

Structural Durability & Health Monitoring, Vol.15, No.4, pp. 317-334, 2021, DOI:10.32604/sdhm.2021.016975 - 23 November 2021

Abstract Internet of Things systems generate a large amount of sensor data that needs to be analyzed for extracting useful insights on the health status of the machine under consideration. Sensor data of all possible states of a system are used for building machine learning models. These models are further used to predict the possible downtime for proactive action on the system condition. Aircraft engine data from run to failure is used in the current study. The run to failure data includes states like new installation, stable operation, first reported issue, erroneous operation, and final failure.… More >