A data-driven approach for sensor fault diagnosis in gearbox of wind energy conversion system

Krüger, Minjia; Ding, Steven X.; Haghani, Adel; Engel, Peter; Jeinsch, Torsten

doi:10.1109/icca.2013.6565179

Cited by 10 publications

(6 citation statements)

References 15 publications

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“…For a comparison study, PCA [52], KPCA [36], dynamic PCA (DPCA) [53], dynamic KPCA (DKPCA) [54], and CVA [55] were employed. Among these methods, PCA and CVA are linear methods.…”

Section: Resultsmentioning

confidence: 99%

Data-driven fault detection of a 10 MW floating offshore wind turbine benchmark using kernel canonical variate analysis

Wang¹,

Wu²,

Huo³

et al. 2022

Meas. Sci. Technol.

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Floating offshore wind turbine (FOWT) can harvest more wind energy in deep waters. However, due to the complex mechanical structures and harsh working conditions, various sensors, actuators, and components of FOWT can malfunction and fail. To avoid serious accidents and reduce operation and maintenance costs, fault detection plays a critical role in wind energy engineering, particularly for offshore wind energy. Because of the complex characteristics such as dynamics and nonlinearity, an accurate mathematical model can not be easily obtained from first principles for FOWT. In this paper, a new data-driven fault detection method based on kernel canonical variable analysis (KCVA) is proposed for FOWTs. In the proposed method, the collected measurements are first augmented into time-lagged variables to capture the dynamics of FOWT. Then, the time-lagged variables are mapped to a high-dimensional feature space to extract nonlinear features. Specifically, canonical variable analysis (CVA) is carried out to explore the correlations in high-dimensional feature space. For fault detection, two monitoring indexes, including $T^2$ and $SPE$ statistics are established. To verify the performance of the proposed KCVA based fault detection method, experiments on a high-fidelity FOWT benchmark, which was created from the National Renewable Energy Laboratory (NREL) FAST v8.0 simulator were carried out. Results show the capability and efficiency of the proposed KCVA-based fault detection method in comparison with other related methods.

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“…For a comparison study, PCA [52], KPCA [36], dynamic PCA (DPCA) [53], dynamic KPCA (DKPCA) [54], and CVA [55] were employed. Among these methods, PCA and CVA are linear methods.…”

Section: Resultsmentioning

confidence: 99%

Data-driven fault detection of a 10 MW floating offshore wind turbine benchmark using kernel canonical variate analysis

Wang¹,

Wu²,

Huo³

et al. 2022

Meas. Sci. Technol.

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“…Focusing on previous classified techniques, within predictive models based on models, several approaches have been identified like linear models combined with artificial neural networks [12], the use of Model Predictive Control (MPC) method [13] or Extreme Learning Machine (ELM) algorithms [14]. Within data-driven techniques, there are also some remarkable researches like an anticipatory control based on MPC approach using time series model [15], a novel predictive maintenance method based on the Random Forest algorithm [16], a three phase based method: offline training process, online monitoring phase and online diagnosis phase [17], a new method using Artificial Neural Networks (ANN) [18] or one that uses fuzzy models [19]. Within case based techniques, there is a study that proposes a novel method which combines an Adaptative Neuro-Fuzzy Inference System (ANFIS) with a Big Data paradigm [11].…”

Section: State Of Artmentioning

confidence: 99%

Real-time predictive maintenance for wind turbines using Big Data frameworks

Canizo

Onieva

Conde

et al. 2017

2017 IEEE International Conference on Prognostics and Health Management (ICPHM)

144

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This work presents the evolution of a solution for predictive maintenance to a Big Data environment. The proposed adaptation aims for predicting failures on wind turbines using a data-driven solution deployed in the cloud and which is composed by three main modules. (i) A predictive model generator which generates predictive models for each monitored wind turbine by means of Random Forest algorithm. (ii) A monitoring agent that makes predictions every 10 minutes about failures in wind turbines during the next hour. Finally, (iii) a dashboard where given predictions can be visualized. To implement the solution Apache Spark, Apache Kafka, Apache Mesos and HDFS have been used. Therefore, we have improved the previous work in terms of data process speed, scalability and automation. In addition, we have provided fault-tolerant functionality with a centralized access point from where the status of all the wind turbines of a company localized all over the world can be monitored, reducing O&M costs.

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“…Finally, the performance of the proposed FDD techniques is assessed using Monte Carlo schemes. In [18], the authors developed a data-driven FDD approach for the gearbox of a WEC system. Moreover, in the paper [19], the authors proposed unknown input observer based scheme for detecting faults in a wind turbine converter.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fault Diagnosis of Uncertain Systems Based on Interval Gaussian Process Regression: Application to Wind Energy Conversion Systems

et al. 2020

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Fault detection and diagnosis (FDD) of wind energy conversion (WEC) systems play an important role in reducing the maintenance and operational costs and increase system reliability. Thus, this paper proposes a novel Interval Gaussian Process Regression (IGPR)-based Random Forest (RF) technique (IGPR-RF) for diagnosing uncertain WEC systems. In the proposed IGPR-RF technique, the effective interval-valued nonlinear statistical features are extracted and selected using the IGPR model and then fed to the RF algorithm for fault classification purposes. The proposed technique is characterized by a better handling of WEC system uncertainties such as wind variability, noise, measurement errors, which leads to an improved fault classification accuracy. The obtained results show that the proposed IGPR-RF technique is characterized by a high diagnosis accuracy (an average accuracy of 99.99%) compared to the conventional classifiers.

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A data-driven approach for sensor fault diagnosis in gearbox of wind energy conversion system

Cited by 10 publications

References 15 publications

Data-driven fault detection of a 10 MW floating offshore wind turbine benchmark using kernel canonical variate analysis

Data-driven fault detection of a 10 MW floating offshore wind turbine benchmark using kernel canonical variate analysis

Real-time predictive maintenance for wind turbines using Big Data frameworks

A Novel Fault Diagnosis of Uncertain Systems Based on Interval Gaussian Process Regression: Application to Wind Energy Conversion Systems

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