Enhanced Particle Filtering for Bearing Remaining Useful Life Prediction of Wind Turbine Drivetrain Gearboxes

Cheng, Fangzhou; Qu, Liyan; Wang, Qiao; Hao, Liwei

doi:10.1109/tie.2018.2866057

Cited by 83 publications

(32 citation statements)

References 57 publications

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“…4In the on-site scenarios, RMS exhibits favorable tendency when the health state of bearings in wind turbines are deteriorating. This is verified not only by the three study cases in this paper, but the published paper, such as [28]. For the high-speed bearings in wind turbines, the failure threshold of RMS is 16 m/s 2 .…”

Section: B Health Indicator and Failure Thresholdsupporting

confidence: 78%

“…Deutsch et al [27] integrated deep belief network and particle filter for RUL prediction of hybrid ceramic bearings. For the application of RUL prediction in wind turbine systems, Cheng et al developed enhanced particle filtering algorithm [28] to predict the remaining useful life of a bearing in a 2.5 MW wind turbine. They also defined noise-to-signal ratio as the fault-related feature for fault prognosis, and used adaptive neuro-fuzzy inference system and particle filtering to predict the RUL of wind turbine gearbox [29].…”

Section: Introductionmentioning

confidence: 99%

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A Robust Model-Based Approach for Bearing Remaining Useful Life Prognosis in Wind Turbines

Teng

Han

et al. 2020

IEEE Access

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Accurate remaining useful life prognosis of bearings in wind turbines can effectively help to schedule maintenance strategy and reduce operational costs at wind farms. Unscented particle filter is good at state tracking in nonlinear problem. A robust model-based approach based on improved unscented particle filter is presented to deal with bearing life prognosis in wind turbines, which involves: (1) The mean of sigma points after unscented Kalman transform is regarded as the particles in particle filter to guarantee the particles aggregation; (2) Several past measurements are utilized to estimate the likelihood function of current step; (3) Uniform distribution is adopted for resampling particles to make them diversity. The presented remaining useful life prognosis approach depends more on the measurement, rather than the initial parameters of degradation model, which makes it practicable for the on-site wind turbines. Three life-cycle bearings from wind turbine high-speed shafts demonstrate the effectiveness of the proposed approach. INDEX TERMS Remaining useful life, prognosis, bearings, wind turbines, improved unscented particle filter.

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Section: B Health Indicator and Failure Thresholdsupporting

confidence: 78%

Section: Introductionmentioning

confidence: 99%

A Robust Model-Based Approach for Bearing Remaining Useful Life Prognosis in Wind Turbines

Teng

Han

et al. 2020

IEEE Access

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“…The intelligent O&M of WTs based on current signal was developed and applied to real wind farms [83]. For the fault diagnosis of WT bearing, support vector machine (SVM) can be used to predict its remaining useful life [84]. Similarly, the k-means algorithm can also be used to predict the remaining useful life of WTs [85].…”

Section: Artificial Intelligence (Ai)mentioning

confidence: 99%

A Survey of Condition Monitoring and Fault Diagnosis toward Integrated O&M for Wind Turbines

Zhang

2019

Energies

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Wind power, as a renewable energy for coping with global climate change challenge, has achieved rapid development in recent years. The breakdown of wind turbines (WTs) not only leads to high repair expenses but also may threaten the stability of the whole power grid. How to reduce the operation and the maintenance (O&M) cost of wind farms is an obstacle to its further promotion and application. To provide reliable condition monitoring and fault diagnosis (CMFD) for WTs, this paper presents a comprehensive survey of the existing CMFD methods in the following three aspects: energy flow, information flow, and integrated O&M system. Energy flow mainly analyzes the characteristics of each component from the angle of energy conversion of WTs. Information flow is the carrier of fault and control information of WT. At the end of this paper, an integrated WT O&M system based on electrical signals is proposed.

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“…The data-driven method is designed to transform the raw monitoring data into relevant information related to system degradation process and the degradation models are derived without concerning about the physics of the system degradation processes [17]. The data-driven approach usually consists of two phases: the first phase learns the fault degradation process and then the second phase predicts the future state of the fault [18]. The data-driven methods mainly use artificial intelligent (AI) approaches and statistical approaches to learn the degradation patterns and estimate the remaining useful life of devices.…”

Section: Related Workmentioning

confidence: 99%

Empirical Mode Decomposition and Temporal Convolutional Networks for Remaining Useful Life Estimation

Yang

Yao

et al. 2019

Int J Parallel Prog

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Remaining useful life (RUL) prediction plays an important role in guaranteeing safe operation and reducing maintenance cost in modern industry. In this paper, we present a novel deep learning method for RUL estimation based on time empirical mode decomposition (EMD) and temporal convolutional networks (TCN). The proposed framework can effectively reveal the non-stationary characteristics of bearing degradation signals and acquire time-series degradation signals which namely intrinsic mode functions through empirical mode decomposition. Furthermore, the feature information is used as the input to convolution layer and trained by TCN to predict remaining useful life. The proposed EMD-TCN model structure maintains a superior result compared to several state-of-the-art convolutional algorithms on public data sets. Experimental results show that the average score of EMD-TCN model is improved by 10-20% than traditional convolutional algorithms.

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Enhanced Particle Filtering for Bearing Remaining Useful Life Prediction of Wind Turbine Drivetrain Gearboxes

Cited by 83 publications

References 57 publications

A Robust Model-Based Approach for Bearing Remaining Useful Life Prognosis in Wind Turbines

A Robust Model-Based Approach for Bearing Remaining Useful Life Prognosis in Wind Turbines

A Survey of Condition Monitoring and Fault Diagnosis toward Integrated O&M for Wind Turbines

Empirical Mode Decomposition and Temporal Convolutional Networks for Remaining Useful Life Estimation

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