Article
Green & Sustainable Science & Technology
Ziqi Wang, Changliang Liu, Feng Yan
Summary: This paper proposes a SCADA data-driven method for condition monitoring of wind turbines using incremental learning and multivariate state estimation technique. The method updates the monitoring model in real-time and improves computation efficiency through sample selection and dynamic downsampling. Experimental results show that the method maintains high accuracy and low false alarm rate in long-term operation, and detects potential faults in advance.
Article
Green & Sustainable Science & Technology
Xiaohang Jin, Zhuangwei Xu, Wei Qiao
Summary: This article proposes an ensemble approach to detect anomalies and diagnose faults in wind turbines, based on modeling and analyzing historical SCADA data from healthy wind turbines. The method can detect anomalies and diagnose faults before wind turbines have to be shut down for maintenance.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2021)
Article
Engineering, Multidisciplinary
Ling Xiang, Penghe Wang, Xin Yang, Aijun Hu, Hao Su
Summary: A new method for fault detection of wind turbines is proposed in this paper, which combines CNN and LSTM networks trained on SCADA data to enhance accuracy and predictive effectiveness.
Article
Computer Science, Information Systems
Lipeng Zhu, Yue Song
Summary: This article develops an automatic data label calibration method to improve the accuracy of WT fault information using time series data analysis. By analyzing temporal similarities and the diversity of multiple variables, the proposed approach reliably calibrates most of the data labels and determines the remaining labels using a new method.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Engineering, Electrical & Electronic
Jiarui Liu, Xinli Li, Chaojie Li, Guotian Yang, Yaqi Li, Jing Qiu, Zhao Yang Dong
Summary: The article proposes a generalized Siamese NBM scheme that explores the characteristics between anomalies and normal behavior. By considering fault samples and designing a parameter-shared backbone and auxiliary regularization terms, the proposed scheme improves the reliability and performance of anomaly detection. The use of a density-based clustering algorithm and label correction further enhances the trustworthiness of the scheme.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Automation & Control Systems
Xingchen Liu, Juan Du, Zhi-Sheng Ye
Summary: This article develops a novel condition monitoring and fault isolation system for wind turbines based on SCADA data. The article addresses challenges such as low sampling rate, time-varying working conditions, and lack of historical fault data. The system uses preprocessing and a global monitoring statistic to monitor the health status of the wind turbine and isolate faults without expert knowledge or historical data.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Engineering, Multidisciplinary
Jiarui Liu, Guotian Yang, Xinli Li, Shumin Hao, Yingming Guan, Yaqi Li
Summary: This paper introduces a novel deep generative method based on the convolutional neural network (CNN)-conditional variational auto-encoder (CVAE) for fault detection of wind turbines. The method combines the feature extraction ability of CVAE and CNN to improve the performance of fault detection through learning probability distribution models and conducting time-series feature extraction and reconstruction.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2023)
Article
Green & Sustainable Science & Technology
Zhihong Luo, Chengyue Fang, Changliang Liu, Shuai Liu
Summary: This paper proposes a complete set of procedures to identify and eliminate outliers in wind-power data based on classification processing. Different algorithms are proposed for different types of outliers. Through analysis of bottom stacked points, various operating modes of wind turbines at ultra-low wind speeds are discovered, and a mechanism-based intuitive rules method is proposed. Experimental results verify the effectiveness, superiority, and strong generalization of the proposed method.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2022)
Article
Engineering, Electrical & Electronic
Ping Wu, Yixuan Wang, Xujie Zhang, Jinfeng Gao, Lin Wang, Yichao Liu
Summary: This article proposes a Mogrifier long short-term memory autoencoder (MLSTM-AE) method to monitor blade breakage in wind turbines. The method calculates the Pearson correlation coefficient for variable selection and uses MLSTM layers to extract spatial-temporal information. By applying kernel density estimation, boundaries for blade breakage alerts are generated based on reconstruction errors, enabling effective monitoring of system dynamics.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Electrical & Electronic
Kai Zhang, Baoping Tang, Lei Deng, Xiaoxia Yu
Summary: The study presents a fault detection frame based on subspace reconstruction-based robust kernel principal component analysis (SR-RKPCA) model for wind turbines SCADA data. By utilizing RKPCA method, permutation entropy, and combined index, the stability, accuracy, and non-linear feature extraction capability of the wind turbine fault detection model are enhanced.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Green & Sustainable Science & Technology
Ran Ma, Wenyi Li, Yongsheng Qi
Summary: In this paper, the authors use the diffusion map algorithm for dimensionality reduction and propose a health state visualization method. By calculating the multivariate statistics of the SCADA data and reducing its dimensionality, typical manifold and clustering features are extracted. The authors construct a health indicator and analyze the embedding features through clustering. The efficiency, effectiveness, and reliability of the proposed method are confirmed through case studies and comparison with other methods. The extendibility of the method is also verified through comparison studies with other dimensionality reduction algorithms.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2022)
Article
Green & Sustainable Science & Technology
Wanqiu Chen, Yingning Qiu, Yanhui Feng, Ye Li, Andrew Kusiak
Summary: This study presents a framework for fault diagnosis of wind turbine faults using transfer learning algorithms Inception V3 and TrAdaBoost, and introduces a new evaluation index 'Comprehensive Index'. Traditional machine learning algorithms perform poorly for unbalanced and differently distributed datasets, while the novel transfer learning algorithm TrAdaBoost shows superior performance in dealing with such challenges.
Article
Green & Sustainable Science & Technology
Xinjian Bai, Tao Tao, Linyue Gao, Cheng Tao, Yongqian Liu
Summary: Wind turbine blade icing has a significant impact on power generation and fatigue life. Accurate diagnosis of blade icing is important for making timely adjustments. This study proposes a data processing method based on pseudo-sample processing to address the challenges of feature extraction and small sample size. The method uses Recursive Feature Elimination and Cross-Validation to select the most compelling feature set and implements a Transductive Support Vector Machine to regenerate unlabelled samples. The results show that the method improves the diagnostic accuracy of the model, especially for small sample data, with an average accuracy improvement of 10.06%.
Article
Energy & Fuels
Chenlong Feng, Chao Liu, Dongxiang Jiang, Detong Kong, Wei Zhang
Summary: A systematic early-stage anomaly detection framework is proposed in this study to utilize SCADA data for condition monitoring and anomaly detection of wind turbines. The framework includes a data cleaning algorithm, a multivariate power curve model, and a sequential probability ratio test. Case studies demonstrate the effectiveness of the framework.
JOURNAL OF ENERGY ENGINEERING
(2023)
Article
Green & Sustainable Science & Technology
Davide Astolfi, Ravi Pandit, Ludovica Celesti, Andrea Lombardi, Ludovico Terzi
Summary: The analysis of wind turbine performance is crucial for future operation, and it is necessary to determine the machine's operation based on historical data and control optimization.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2022)
Article
Computer Science, Artificial Intelligence
Jun Zhan, Ronglin Wang, Lingzhi Yi, Yaguo Wang, Zhengjuan Xie
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
(2019)
Article
Green & Sustainable Science & Technology
Jun Zhan, Chengkun Wu, Canqun Yang, Qiucheng Miao, Shilin Wang, Xiandong Ma
Summary: In this paper, a novel condition monitoring approach based on multidirectional spatial-temporal feature aggregation networks (MSTFAN) is proposed to accurately evaluate the performance and diagnose faults of wind turbines (WTs). The approach extracts spatial-temporal features of data using a spatial-temporal network constructed by combining a graph attention network (GAT) and a temporal convolutional network (TCN). It further studies the long-term spatial-temporal dependency of the features using a bidirectional long short-term memory (BiLSTM) neural network. Experimental results on real-world wind farm datasets demonstrate the effectiveness of the proposed approach in detecting early abnormal situations of WTs.
Article
Engineering, Mechanical
Xuanen Kan, Yanjun Lu, Fan Zhang, Weipeng Hu
Summary: A blade disk system is crucial for the energy conversion efficiency of turbomachinery, but differences between blades can result in localized vibration. This study develops an approximate symplectic method to simulate vibration localization in a mistuned bladed disk system and reveals the influences of initial positive pressure, contact angle, and surface roughness on the strength of vibration localization.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Zimeng Liu, Cheng Chang, Haodong Hu, Hui Ma, Kaigang Yuan, Xin Li, Xiaojian Zhao, Zhike Peng
Summary: Considering the calculation efficiency and accuracy of meshing characteristics of gear pair with tooth root crack fault, a parametric model of cracked spur gear is established by simplifying the crack propagation path. The LTCA method is used to calculate the time-varying meshing stiffness and transmission error, and the results are verified by finite element method. The study also proposes a crack area share index to measure the degree of crack fault and determines the application range of simplified crack propagation path.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Rongjian Sun, Conggan Ma, Nic Zhang, Chuyo Kaku, Yu Zhang, Qirui Hou
Summary: This paper proposes a novel forward calculation method (FCM) for calculating anisotropic material parameters (AMPs) of the motor stator assembly, considering structural discontinuities and composite material properties. The method is based on multi-scale theory and decouples the multi-scale equations to describe the equivalence and equivalence preconditions of AMPs of two scale models. The effectiveness of this method is verified by modal experiments.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Hao Zhang, Jiangcen Ke
Summary: This research introduces an intelligent scheduling system framework to optimize the ship lock schedule of the Three Gorges Hub. By analyzing navigational rules, operational characteristics, and existing problems, a mixed-integer nonlinear programming model is formulated with multiple objectives and constraints, and a hybrid intelligent algorithm is constructed for optimization.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Jingjing He, Xizhong Wu, Xuefei Guan
Summary: A sensitivity and reliability enhanced ultrasonic method has been developed in this study to monitor and predict stress loss in pre-stressed multi-layer structures. The method leverages the potential breathing effect of porous cushion materials in the structures to increase the sensitivity of the signal feature to stress loss. Experimental investigations show that the proposed method offers improved accuracy, reliability, and sensitivity to stress change.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Benyamin Hosseiny, Jalal Amini, Hossein Aghababaei
Summary: This paper presents a method for monitoring sub-second or sub-minute displacements using GBSAR signals, which employs spectral estimation to achieve multi-dimensional target detection. It improves the processing of MIMO radar data and enables high-resolution fast displacement monitoring from GBSAR signals.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Xianze Li, Hao Su, Ling Xiang, Qingtao Yao, Aijun Hu
Summary: This paper proposes a novel method for bearing fault identification, which can accurately identify faults with few samples under complex working conditions. The method is based on a Transformer meta-learning model, and the final result is determined by the weighted voting of multiple models.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Xiaomeng Li, Yi Wang, Guangyao Zhang, Baoping Tang, Yi Qin
Summary: Inspired by chaos fractal theory and slowly varying damage dynamics theory, this paper proposes a new health monitoring indicator for vibration signals of rotating machinery, which can effectively monitor the mechanical condition under both cyclo-stationary and variable operating conditions.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Hao Wang, Songye Zhu
Summary: This paper extends the latching mechanism to vibration control to improve energy dissipation efficiency. An innovative semi-active latched mass damper (LMD) is proposed, and different latching control strategies are tested and evaluated. The latching control can optimize the phase lag between control force and structural response, and provide an innovative solution to improve damper effectiveness and develop adaptive semi-active dampers.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Menghao Ping, Xinyu Jia, Costas Papadimitriou, Xu Han, Chao Jiang, Wang-Ji Yan
Summary: Identification of non-Gaussian processes is a challenging task in engineering problems. This article presents an improved orthogonal series expansion method to convert the identification of non-Gaussian processes into a finite number of non-Gaussian coefficients. The uncertainty of these coefficients is quantified using polynomial chaos expansion. The proposed method is applicable to both stationary and nonstationary non-Gaussian processes and has been validated through simulated data and real-world applications.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Lei Li, Wei Yang, Dongfa Li, Jianxin Han, Wenming Zhang
Summary: The frequency locking phenomenon induced by modal coupling can effectively overcome the dependence of peak frequency on driving strength in nonlinear resonant systems and improve the stability of peak frequency. This study proposes the double frequencies locking phenomenon in a three degrees of freedom (3-DOF) magnetic coupled resonant system driven by piezoelectricity. Experimental and theoretical investigations confirm the occurrence of first frequency locking and the subsequent switching to second frequency locking with the increase of driving force. Furthermore, a mass sensing scheme for double analytes is proposed based on the double frequencies locking phenomenon.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Kai Ma, Jingtao Du, Yang Liu, Ximing Chen
Summary: This study explores the feasibility of using nonlinear energy sinks (NES) as replacements for traditional linear tuned mass dampers (TMD) in practical engineering applications, specifically in diesel engine crankshafts. The results show that NES provides better vibration attenuation for the crankshaft compared to TMD under different operating conditions.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Wentao Xu, Li Cheng, Shuaihao Lei, Lei Yu, Weixuan Jiao
Summary: In this study, a high-precision hydraulic mechanical stand and a vertical mixed-flow pumping station device were used to conduct research on cavitation signals of mixed-flow pumps. By analyzing the water pressure pulsation signal, it was found that the power spectrum density method is more sensitive and capable of extracting characteristics compared to traditional time-frequency domain analysis. This has significant implications for the identification and prevention of cavitation in mixed-flow pump machinery.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Xiaodong Chen, Kang Tai, Huifeng Tan, Zhimin Xie
Summary: This paper addresses the issue of parasitic motion in microgripper jaws and its impact on clamping accuracy, and proposes a symmetrically stressed parallelogram mechanism as a solution. Through mechanical modeling and experimental validation, the effectiveness of this method is demonstrated.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Zhifeng Shi, Gang Zhang, Jing Liu, Xinbin Li, Yajun Xu, Changfeng Yan
Summary: This study provides useful guidance for early bearing fault detection and diagnosis by investigating the effects of crack inclination and propagation direction on the vibration characteristics of bearings.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)