A Deep Learning Approach to Detect and Isolate Thruster Failures for Dynamically Positioned Vessels Using Motion Data

Han, Peihua; Li, Guoyuan; Skulstad, Robert; Skjong, Stian; Zhang, Houxiang

doi:10.1109/tim.2020.3016413

Cited by 25 publications

(10 citation statements)

References 27 publications

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“…The cross entropy loss function is used to optimize the model when the balanced data sets are used to train the model. For the loss-functionbased method, the imbalanced data sets are applied for model training directly and the focal loss function is leveraged for model optimization [32].…”

Section: E Loss Functionmentioning

confidence: 99%

A Multilevel Convolutional Recurrent Neural Network for Blade Icing Detection of Wind Turbine

Tian

Cheng

et al. 2021

IEEE Sensors J.

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Blade icing detection becomes increasingly significant as it can avoid revenue loss and power degradation. Conventional methods are usually limited by additional costs, and model-driven methods heavily depend on prior domain knowledge. Data-driven methods, especially deep learning approaches without needing the time-consuming handcraft feature engineering, offer a promising solution for blade icing detection. However, the monitoring signals normally have complex and diverse features as wind turbine operates in complex environments, thus effective model is needed for data analyzing. Additionally, the distribution of monitoring data is imbalanced, which causes the abnormal data mining inadequate. In this work, a multilevel convolutional recurrent neural network (MCRNN), is proposed for blade icing detection. Specifically, discrete wavelet decomposition is leveraged to obtain multilevel features both from the time domain and the frequency domain. A parallel structure combining an LSTM branch and a CNN branch is established in each level for feature extraction. To alleviate the severe data imbalance, two mechanisms, including data resampling algorithm and class-rebalanced loss function, are investigated. Furthermore, a multi-step accumulation strategy is proposed to enhance the accuracy of real-time detection. Extensive studies demonstrate that the proposed MCRNN can achieve up 38.8% and 42.9% higher F1-score over the best baseline on the balanced data sets processed by data resampling algorithm and 23.9% and 30.6% higher on imbalanced data sets with MCRNN optimized by the class-rebalanced loss function. The real-time detection verifies the applicability of the proposed method and indicates that the proposed multi-step accumulation strategy can improve the accuracy of icing detection.

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Section: E Loss Functionmentioning

confidence: 99%

A Multilevel Convolutional Recurrent Neural Network for Blade Icing Detection of Wind Turbine

Tian

Cheng

et al. 2021

IEEE Sensors J.

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“…Existing methods include feature-based machine learning approaches and deep learning approaches. This method has been utilized for rolling bearing fault diagnostics [17], power distribution system fault detection [18], thruster failure detection [19]. Despite of their effectiveness, supervised methods require the availability of labeled instances for normal as well as faulty classes, which might not be available in most cases.…”

Section: B Data-driven Fault Detectionmentioning

confidence: 99%

Fault Detection With LSTM-Based Variational Autoencoder for Maritime Components

Han

Ellefsen

et al. 2021

IEEE Sensors J.

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Maintenance routines on ships today follow either a reactive maintenance (RM) or preventive maintenance (PvM) approach. RM can be regarded as post-failure repair, which might create large costs. PvM uses predetermined maintenance intervals, which often involves unnecessary maintenance. Recently, prognostics and health management (PHM) has emerged as a potential way to develop an ideal maintenance policy. PHM aims to provide optimal maintenance schedule through the use of sensor measurement for fault detection and fault prognostics, among which fault detection is the first and fundamental action. In this paper, a longshort term memory based variational autoencoder (LSTM-VAE) is proposed for fault detection of maritime components onboard. It is a semi-supervised approach that requires only fault-free data for training. Therefore, it is widely applicable in the maritime industry since operational data in normal conditions already exists. Realworld operation data collected from a diesel engine on the research vessel (RV) Gunnerus is used to validate the method. Results show that the LSTM-VAE can detect the fault accurately.

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“…Deep learning technology has now been extensively researched in data-driven fault detection. Based on the historical data set, a deep CNN model was designed to realize thruster failure detection of dynamically positioned vessels [9]. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection for Motor Drive Control System of Industrial Robots Using CNN-LSTM-based Observers

Wang

Zhang

Wang

2023

Trans. Electr. Mach. Syst.

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The complex working conditions and nonlinear characteristics of the motor drive control system of industrial robots make it difficult to detect faults. In this paper, a deep learning-based observer, which combines the convolutional neural network (CNN) and the long short-term memory network (LSTM), is employed to approximate the nonlinear driving control system. CNN layers are introduced to extract dynamic features of the data, whereas LSTM layers perform time-sequential prediction of the target system. In terms of application, normal samples are fed into the observer to build an offline prediction model for the target system. The trained CNN-LSTM-based observer is then deployed along with the target system to estimate the system outputs. Online fault detection can be realized by analyzing the residuals. Finally, an application of the proposed fault detection method to a brushless DC motor drive system is given to verify the effectiveness of the proposed scheme. Simulation results indicate the impressive fault detection capability of the presented method for driving control systems of industrial robots.

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A Deep Learning Approach to Detect and Isolate Thruster Failures for Dynamically Positioned Vessels Using Motion Data

Cited by 25 publications

References 27 publications

A Multilevel Convolutional Recurrent Neural Network for Blade Icing Detection of Wind Turbine

A Multilevel Convolutional Recurrent Neural Network for Blade Icing Detection of Wind Turbine

Fault Detection With LSTM-Based Variational Autoencoder for Maritime Components

Fault Detection for Motor Drive Control System of Industrial Robots Using CNN-LSTM-based Observers

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