Fault detection and diagnosis of a blade pitch system in a floating wind turbine based on Kalman filters and artificial neural networks

Cho, Seongpil; Choi, Minjoo; Gao, Zhen; Moan, Torgeir

doi:10.1016/j.renene.2020.12.116

Cited by 97 publications

(45 citation statements)

References 42 publications

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“…In this paper, six types of faults that occur in a pitch sensor and an actuator of a hydraulic blade pitch system are considered (Cho et al, 2021). Table 2 presents these different types of faults.…”

Section: Fault Classification Methodsmentioning

confidence: 99%

“…In particular, researchers have used use neural network theories based on multilayer perceptron (MLP) (Kusiak and Li, 2011;Wang et al, 2016;Zaher et al, 2009;Kusiak and Verma, 2012;Cho et al, 2021), convolutional neural networks (CNNs) (Bach-Andersen et al, 2015;Bach-Andersen et al, 2018), and autoencoders (AEs) (Dervilis et al, 2014;Jiang et al, 2017) in order to develop reliable fault diagnosis systems for the components of wind turbines including the main bearing, gearbox, and generator. In particular, Cho et al (2021) used an MLP-based ANN technique to perform fault diagnosis of a blade pitch system. This technique is, however, unsuitable for processing time-series data during training, and therefore, a recurrent neural network (RNN) that is apt for processing time-series data is required.…”

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confidence: 99%

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Fault Classification of a Blade Pitch System in a Floating Wind Turbine Based on a Recurrent Neural Network

Cho¹,

Park

Choi

2021

J. Ocean Eng. Technol.

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Offshore wind turbines are prone to unexpected faults as they are often operated in a harsher environments compared to turbines on land. Accordingly, there has been a growing interest toward developing technologies for detecting and diagnosing faults in advance for the effective maintenance and operation of these turbines. Unexpected faults influence system components, actuators, sensors, and controllers, which may result in the inoperability of a turbine in case of serious failures. Moreover, faults can lead to economic losses by inducing changes in the system characteristics, operation safety, and power efficiency of a wind turbine. Maintenance costs account for approximately 25%-30% of the life-cycle cost of an offshore wind power plant (Dinwoodie et al., 2013), and therefore, the reliability of such a wind turbine is critical. Among the components of an offshore turbine, the blade pitch system has the highest downtime and failure rate (Gayo, 2011;Carroll et al., 2016;NordzeeWind, 2010). A system fault affects the aerodynamic loads of a blade, the power generation output, and the behavior of substructures. Therefore, it is crucial to diagnose faults at the early stage in a blade pitch system to protect a turbine and prevent downtime of the entire system (Cho et al., 2018). Early fault diagnosis can reduce risks and prevent accidents by raising alarms at appropriate times, thus enabling wind power plant operators to effectively operate and maintain a turbine (Isermann, 2006). It can also prevent long-term damage to turbines and provide a reliable technological guarantee for the further development of the wind power generation industry. Up to recently, classical fault diagnosis methods with statistical classification, approximation methods, and density-based methods has been used. Deep learning methods, which are a type of machine learning algorithm, have emerged in recent years; they have been used for learning or training the complex structures of an actual large-scale dataset continuously collected by sensors in various applications including computer vision, object classification, voice recognition,

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Section: Fault Classification Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Fault Classification of a Blade Pitch System in a Floating Wind Turbine Based on a Recurrent Neural Network

Cho¹,

Park

Choi

2021

J. Ocean Eng. Technol.

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“…A Gaussian mixture model was used for outlier detection, this split the data into faults and performance curves were used to classify faults. Cho et al [19,20] have produced two papers looking at fault detection for a floating wind turbine pitch system using Kalman Filters. This used the input command to the actuator and the output of the sensor as inputs to the Kalman Filter, the residuals were compared and if they exceeded a threshold, then a fault was detected.…”

Section: Pitch System Condition Monitoringmentioning

confidence: 99%

“…In their first paper [19], the authors then isolated the fault being the cause of the sensor or the actuator through analysing the Nacelle Yaw. In the second paper [20], the fault was diagnosed with an Artificial Neural Network, to differentiate between 6 different fault cases. A paper from He et al [21] used electrical signature analysis of the pitch system to detect faults.…”

Section: Pitch System Condition Monitoringmentioning

confidence: 99%

Investigation of Isolation Forest for Wind Turbine Pitch System Condition Monitoring Using SCADA Data

et al. 2021

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Wind turbine pitch system condition monitoring is an active area of research, and this paper investigates the use of the Isolation Forest Machine Learning model and Supervisory Control and Data Acquisition system data for this task. This paper examines two case studies, turbines with hydraulic or electric pitch systems, and uses an Isolation Forest to predict failure ahead of time. This novel technique compared several models per turbine, each trained on a different number of months of data. An anomaly proportion for three different time-series window lengths was compared, to observe trends and peaks before failure. The two cases were compared, and it was found that this technique could detect abnormal activity roughly 12 to 18 months before failure for both the hydraulic and electric pitch systems for all unhealthy turbines, and a trend upwards in anomalies could be found in the immediate run up to failure. These peaks in anomalous behaviour could indicate a future failure and this would allow for on-site maintenance to be scheduled. Therefore, this method could improve scheduling planned maintenance activity for pitch systems, regardless of the pitch system employed.

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“…Zheng et al [27] proposed an improved uniform phase empirical mode decomposition method for machinery fault diagnosis. In addition, artificial intelligence techniques are increasingly wildly applied to the field of fault diagnosis, among which are Bayesian method [28], support vector machine method [29], and artificial neural network [30]. In recent years, deep learning [31] has become a new research hotspot in the field of fault diagnosis.…”

Section: B Knowledge Graph-assisted Fault Diagnosismentioning

confidence: 99%

CNC Machine Tool Fault Diagnosis Integrated Rescheduling Approach Supported by Digital Twin-Driven Interaction and Cooperation Framework

Liu

et al. 2021

IEEE Access

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The problems of CNC machine tool (CNCMT) fault diagnosis and production rescheduling have attracted continuous attention because of their great significance to the manufacturing industry. Digital twin is a supporting technology for achieving smart manufacturing and provides a new paradigm for solving these problems. This paper explores a digital twin-driven interaction and cooperation framework and proposes the architecture and implementation mechanism to enable the sharing of data, knowledge, and resource, to realize the fusion of physical space and cyber space, and to improve the accuracy of fault diagnosis. Under this framework, aiming at the influence of CNCMT failure on the initial production planning, a self-adaptation rescheduling method based on Monte Carlo Tree Search (MCTS) algorithm is proposed to provide support for developing more efficient production planning. Finally, the effectiveness of the proposed framework is validated by experimental study. The framework and integrated rescheduling approach can provide guidance for enterprises in implementing CNCMT maintenance and production scheduling to meet high accuracy and reliability requirements.

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Fault detection and diagnosis of a blade pitch system in a floating wind turbine based on Kalman filters and artificial neural networks

Cited by 97 publications

References 42 publications

Fault Classification of a Blade Pitch System in a Floating Wind Turbine Based on a Recurrent Neural Network

Fault Classification of a Blade Pitch System in a Floating Wind Turbine Based on a Recurrent Neural Network

Investigation of Isolation Forest for Wind Turbine Pitch System Condition Monitoring Using SCADA Data

CNC Machine Tool Fault Diagnosis Integrated Rescheduling Approach Supported by Digital Twin-Driven Interaction and Cooperation Framework

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