A Multi-Factor Driven Model for Locomotive Axle Temperature Prediction Based on Multi-Stage Feature Engineering and Deep Learning Framework by Guangxi Yan, Yu Bai, Chengqing Yu, Chengming Yu

“…To realize accurate real-time condition monitoring and fault diagnosis, a new multi-data-driven model based on reinforcement learning and deep learning is proposed in this paper. …”
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Dynamic Modelling and Experimental Characterization of a Self-Powered Structural Health-Monitoring System with MFC Piezoelectric Patches by Gianpietro Di Rito, Mario Rosario Chiarelli, Benedetto Luciano

“…The model is finally used to characterize a condition-monitoring algorithm in terms of false alarms rejection and vulnerability to dormant faults, by simulating built-in tests to be performed during maintenance.…”
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Fault Diagnosis Methods Based on Machine Learning and its Applications for Wind Turbines: A Review by Tongda Sun, Gang Yu, Mang Gao, Lulu Zhao, Chen Bai, Wanqian Yang

“…With the increase in the installed capacity of wind power systems, the fault diagnosis and condition monitoring of wind turbines (WT) has attracted increasing attention. …”
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ADVANCES IN RENEWAL DECISION-MAKING UTILISING THE PROPORTIONAL HAZARDS MODEL WITH VIBRATION COVARIATES by Pieter-Jan Vlok, Jasper L. Coetzee

“…Two of the main techniques to achieve this is through Condition Monitoring (such as vibrat ion monitoring and oil anal ysis) and Statistical Failure Analysis (typically using probabilistic techniques) . …”
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Integrated medical and nursing ward rounds in a patient with hemorrhagic stroke (1例出血性中风患者的医护一体化查房体会) by YAO Di (姚娣), ZHANG Xiaoxue (张晓雪), DING Yan (丁雁)

“…By analyzing the patient's clinical data, doctors and nurses jointly discussed the key nursing content and formulated nursing measures, including illness condition monitoring, airway management, health education and rehabilitation care. …”
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Graph Feature Fusion-Driven Fault Diagnosis of Complex Process Industrial System Based on Multivariate Heterogeneous Data by Fengyuan Zhang, Jie Liu, Xiang Lu, Tao Li, Yi Li, Yongji Sheng, Hu Wang, Yingwei Liu

“…The stable operation of the process industrial system, which is integrated with various complex equipment, is the premise of production, which requires the condition monitoring and diagnosis of the system. Recently, the continuous development of deep learning (DL) has promoted the research of intelligent diagnosis in process industry systems, and the sensor system layout has provided sufficient data foundation for this task. …”
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Magnetohydrodynamic mass and heat transport over a stretching sheet in a rotating nanofluid with binary chemical reaction, non-fourier heat flux, and swimming microorganisms by Bagh Ali, Imran Siddique, Anum Shafiq, Sohaib Abdal, Ilyas Khan, Afrasyab Khan

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Design optimisation of wind turbine towers with reliability-based calibration of partial safety factors by Shaikha Al-Sanad, Jafarali Parol, Lin Wang, Athanasios Kolios

“…For a site-specific design of wind turbine towers, the details of the load parameters, such as the type of distributions and the coefficient of variation, can be obtained through the condition monitoring system. With these information, the PSFs can be calibrated based on the reliability method, meeting the target reliability index and avoiding over or under engineering of wind turbine tower structures. …”
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Differential Equation-Based Prediction Model for Early Change Detection in Transient Running Status by Xin Wen, Guangyuan Chen, Guoliang Lu, Zhiliang Liu, Peng Yan

“…The effectiveness of the proposed method is demonstrated using three representative real-engineering applications: external loading status monitoring, bearing health status monitoring and speed condition monitoring. The method is also compared with those benchmark methods reported in the literature. …”
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ASR: Practical investigative techniques and field monitoring systems used to assess ASR for service life modeling. by Matteini Irene, Noyce Paul, Crevello Gina

“…The paper will discuss the development of the approach, from visual indicators identifying condition hierarchies, to long term condition monitoring for various concrete parameters combined with laboratory testing (expansion and residual alkalis) and mathematical modeling. …”
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Detection of Nonlinear Behavior Induced by Hard Limiting in Voltage Source Converters in Wind Farms Based on Higher-order Spectral Analysis by Zetian Zheng, Shaowei Huang, Qiangsheng Bu, Chen Shen, Jun Yan

“…Since the 1960s, the higher-order statistics (HOS) theory has become a powerful tool for the detection of nonlinear behavior (DNB) in production quality control wherein it has mainly been applied to mechanical condition monitoring and fault diagnosis. This study focuses on the hard limiters of the voltage source converter (VSC) control systems in the wind farms and attempts to detect the nonlinear behavior caused by bi- or uni-Iateral saturation hard limiting using the HOS analysis. …”
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Investigation of Time-Varying Cable Tension of Bridges Using Time-Frequency Reassignment Techniques Based on Structural Health Monitoring Data by Maosen Cao, Shuaitao Hu, Xin Zhang, Shixiang Zhang, Dragoslav Sumarac, Jiayi Peng

“…The results show that the proposed algorithm can be used to identify time-varying tension and assess the service performance of cables, providing a new path for real-time condition monitoring of bridges in service.…”
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Increasing the Adoption of Prognostic Systems for Health Management in the Power Industry by V. Catterson, J. Costello, G. West, S. Mcarthur, C. Wallace

“…This information can be supplied by diagnostic and prognostic systems respectively, utilizing data from condition monitoring systems and inspections to derive status and likely future behavior. …”
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Multi-Running State Health Assessment of Wind Turbines Drive System Based on BiLSTM and GMM by Tao Liang, Zhaochao Meng, Gaofeng Xie, Shurui Fan

“…With the continuous elevation of demand for large-scale wind turbines and operation & maintenance cost an increasing interest has been rapidly generated on CM (Condition Monitoring) system. The main components of wind turbines are the focus on all CM as they overall lead to high repair costs and equipment downtime. …”
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Bearing failure diagnosis and prognostics modeling in plants for industrial purpose by Henry Ogbemudia Omoregbee, Bright Aghogho Edward, Mabel Usunobun Olanipekun

“…The forecasting of the future condition, the remaining operating life, or probability of stable system behavior, based on data from acquired condition monitoring is referred to as prognosis which is an important part of the CBM process. …”
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Numerical Simulation of Multi-Nozzle Droplet Evaporation Characteristics for Desulfurization Wastewater by Xinrui Guo, Jiangbo Wu, Xiaoze Du, Yaocong Zhang, Shuqin Feng, Shujun Liu

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An Adaptive Remaining Useful Life Estimation Approach for Newly Developed System Based on Nonlinear Degradation Model by Xi Wang, Changhua Hu, Xiaosheng Si, Zhenan Pang, Ziqiang Ren

“…In order to utilize the condition monitoring (CM) data of the service system up to date, we present a parameter estimation method based on the expectation maximization algorithm to adaptively estimate and update the model parameters online. …”
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Study of GaN Hall Effect Magnetic Sensors by Vlad Marsic, Soroush Faramehr, Isha Maini, David A. J. Moran, Petar Igic

“…The GaN Hall sensors are the latest advancements in non-intrusive active monitoring, designed to be integrated on GaN power transistors to enable in-situ condition monitoring, hence boosting device reliability and life. …”
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PaveDistress: A comprehensive dataset of pavement distresses detectionMendeley Data by Zhen Liu, Wenxiu Wu, Xingyu Gu, Bingyan Cui

“…By providing high-quality, diverse images, the PaveDistress dataset offers significant potential for research in automated pavement condition monitoring and management systems.…”
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Machine Learning on Fault Diagnosis in Wind Turbines by Eddie Yin-Kwee Ng, Jian Tiong Lim

“…Therefore, SCADA-driven fault diagnosis in WT based on machine learning algorithms has been proposed in this study by comparing the performance of three different machine learning algorithms, namely k-nearest neighbors (kNN) with a bagging regressor, extreme gradient boosting (XGBoost) and an artificial neural network (ANN) on condition monitoring of gearbox oil sump temperature. Further, this study also compared the performance of two different feature selection methods, namely the Pearson correlation coefficient (PCC) and principal component analysis (PCA), and three hyperparameter optimization methods on optimizing the performance of the models, namely a grid search, a random search and Bayesian optimization. …”
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