Modeling and Prediction of Gearbox Faults With Data-Mining Algorithms

2013 ◽  
Vol 135 (3) ◽  
Author(s):  
Anoop Verma ◽  
Zijun Zhang ◽  
Andrew Kusiak
Keyword(s):  
Wind Turbine ◽  
Fast Fourier Transformation ◽  
Crest Factor ◽  
Wind Turbine Gearbox ◽  
Vibration Data ◽  
Data Mining Algorithms ◽  
Unseen Data ◽  
Data Driven Approach ◽  
Advance Time ◽  
Mining Algorithms

A data-driven approach for analyzing faults in wind turbine gearbox is developed and tested. More specifically, faults in a ring gear are predicted in advance. Time-domain statistical metrics, such as jerk, root mean square (RMS), crest factor (CF), and kurtosis, are investigated to identify faulty components of a wind turbine. The components identified are validated with the fast Fourier transformation (FFT) of vibration data. Fifty neural networks (NNs) with different parameter settings are trained to obtain the best performing model. Models based on original vibration data, and transformed jerk data are constructed. The jerk model based on multisensor data outperforms the other models and therefore is used for testing and validation of previously unseen data. Short-term predictions of up to 15 time intervals, each representing 0.1 s, are performed. The prediction accuracy varies from 91.68% to 94.78%.

A Data-Driven Approach to Detect Mechanical Faults in Wind Turbine Gearbox

2017 ◽  
Author(s):  
Ruoyu Li ◽  
Zeyi Sun
Keyword(s):  
Wind Turbine ◽  
Detection System ◽  
Wind Turbine Gearbox ◽  
Vibration Signals ◽  
Vibration Data ◽  
Data Driven Approach ◽  
Mechanical Faults ◽  
Condition Monitoring Systems ◽  
Learning Machine ◽  
Periodic Components

Online condition monitoring systems play an important role in preventing catastrophic failure, reducing maintenance costs, and improving the system reliability. In this paper, wind turbine gearbox mechanical fault detection system is developed. An adaptive filtering technique is applied to separate the impulsive components from the periodic components of the vibration signals. Then different features of the periodic components and impulsive components are extracted. An extreme learning machine based classifier is designed and trained by using the features extracted from simulated vibration data of wind turbine gearbox. Simulated vibration signals of wind turbines gearbox are used to demonstrate the effectiveness of the presented methodology.

Estimation of Wind Turbine Gearbox Loads for Online Fatigue Monitoring Using Inverse Methods

2021 ◽  
Author(s):  
Felix C. Mehlan ◽  
Amir R. Nejad ◽  
Zhen Gao
Keyword(s):  
Wind Turbine ◽  
Fatigue Damage ◽  
Wind Turbine Gearbox ◽  
Local Loads ◽  
Vibration Data ◽  
Novel Approach ◽  
Torque Measurements ◽  
Continuous Estimation ◽  
Dynamic States

Abstract In this article a novel approach for the estimation of wind turbine gearbox loads with the purpose of online fatigue damage monitoring is presented. The proposed method employs a Digital Twin framework and aims at continuous estimation of the dynamic states based on CMS vibration data and generator torque measurements from SCADA data. With knowledge of the dynamic states local loads at gearbox bearings are easily determined and fatigue models are be applied to track the accumulation of fatigue damage. A case study using simulation measurements from a high-fidelity gearbox model is conducted to evaluate the proposed method. Estimated loads at the considered IMS and HSS bearings show moderate to high correlation (R = 0.50–0.96) to measurements, as lower frequency internal dynamics are not fully captured. The estimated fatigue damage differs by 5–15 % from measurements.

Fault Monitoring of Wind Turbine Generator Brushes: A Data-Mining Approach

2012 ◽  
Vol 134 (2) ◽  
Author(s):  
Anoop Verma ◽  
Andrew Kusiak
Keyword(s):  
Data Mining ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Turbine Generator ◽  
Wind Turbine Generator ◽  
Data Mining Algorithms ◽  
Data Mining Approach ◽  
Carbon Brushes ◽  
Critical Components ◽  
Mining Algorithms

Components of wind turbines are subjected to asymmetric loads caused by variable wind conditions. Carbon brushes are critical components of the wind turbine generator. Adequately maintaining and detecting abnormalities in the carbon brushes early is essential for proper turbine performance. In this paper, data-mining algorithms are applied for early prediction of carbon brush faults. Predicting generator brush faults early enables timely maintenance or replacement of brushes. The results discussed in this paper are based on analyzing generator brush faults that occurred on 27 wind turbines. The datasets used to analyze faults were collected from the supervisory control and data acquisition (SCADA) systems installed at the wind turbines. Twenty-four data-mining models are constructed to predict faults up to 12 h before the actual fault occurs. To increase the prediction accuracy of the models discussed, a data balancing approach is used. Four data-mining algorithms were studied to evaluate the quality of the models for predicting generator brush faults. Among the selected data-mining algorithms, the boosting tree algorithm provided the best prediction results. Research limitations attributed to the available datasets are discussed.

scholarly journals Fault Diagnosis of Wind Turbine Gearbox Based on the Optimized LSTM Neural Network with Cosine Loss

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2339 ◽  
Author(s):  
Aijun Yin ◽  
Yinghua Yan ◽  
Zhiyu Zhang ◽  
Chuan Li ◽  
René-Vinicio Sánchez
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Short Term Memory ◽  
Wind Turbine Gearbox ◽  
Vibration Data ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Lstm Network ◽  
Wavelet Energy Entropy ◽  
Improve Cost

The gearbox is one of the most fragile parts of a wind turbine (WT). Fault diagnosis of the WT gearbox is of great importance to reduce operation and maintenance (O&M) costs and improve cost-effectiveness. At present, intelligent fault diagnosis methods based on long short-term memory (LSTM) networks have been widely adopted. As the traditional softmax loss of an LSTM network usually lacks the power of discrimination, this paper proposes a fault diagnosis method for wind turbine gearboxes based on optimized LSTM neural networks with cosine loss (Cos-LSTM). The loss can be converted from Euclid space to angular space by cosine loss, thus eliminating the effect of signal strength and improve the diagnosis accuracy. The energy sequence features and the wavelet energy entropy of the vibration signals are used to evaluate the Cos-LSTM networks. The effectiveness of the proposed method is verified with the fault vibration data collected on a gearbox fault diagnosis experimental platform. In addition, the Cos-LSTM method is also compared with other classic fault diagnosis techniques. The results demonstrate that the Cos-LSTM has better performance for gearbox fault diagnosis.

scholarly journals Prognosis of Wind Turbine Gearbox Bearing Failures using SCADA and Modeled Data

2020 ◽  
Vol 12 (1) ◽  
pp. 10
Author(s):  
Arch Desai ◽  
Yi Guo ◽  
Shawn Sheng ◽  
Shawn Sheng ◽  
Caleb Phillips ◽  
...  
Keyword(s):  
Machine Learning ◽  
Wind Turbine ◽  
Power Plants ◽  
Performance Enhancement ◽  
Low Frequency ◽  
Machine Learning Techniques ◽  
False Alarms ◽  
Bearing Failure ◽  
Wind Turbine Gearbox ◽  
Unseen Data

Predictive maintenance and condition monitoring systems for wind turbines have seen increased adoption to minimize downtime, reducing operation and maintenance costs. On today’s wind power plants, the integrated supervisory control and data acquisition (SCADA) system provides low- frequency operational data that can be leveraged to quantify a wind turbine’s health. The aim of this study is to utilize machine-learning techniques to predict axial cracking failures in wind turbine gearbox bearings up to 1 month ahead of time. The failures are assumed to have occurred when the investigated bearing was replaced. While current SCADA systems show the overall condition of a wind turbine, often they do not allow for the investigation of specific gearbox bearings’ health. To enrich bearing fault signatures, additional data are computed through physics-based models using gearbox design information. Based on SCADA data, modeled data, and bearing failure log data from an actual wind plant, the performances of different machine-learning models on unseen data are then evaluated using industry-standard metrics such as precision, recall, and F1 score. Results show the overall system performance enhancement in predicting bearing failure when modeled data are included with SCADA data. The reduction in terms of false alarms is about 50%, and improvement in terms of precision and F1 score is about 33% and 12% respectively, based on the best modeling case in this study.

Wind Turbine Gearbox Planetary Stage Gear Damage Detection Using Vibration Data

2014 ◽  
Author(s):  
Jesse Hanna ◽  
Huageng Luo
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Vibration Signal ◽  
Variable Speed ◽  
Wind Turbine Gearbox ◽  
Vibration Data ◽  
Planet Gear ◽  
Condition Monitoring System ◽  
The Many ◽  
Many Sources

Effective vibration based condition monitoring applied to the planetary stage of a wind turbine gearbox has been historically difficult. Numerous complications associated with the low speed and variable speed nature of a wind turbine gearbox as well as the many sources of vibration signal modulation and poor vibration transmission paths within the planetary stage itself have presented complex challenges around effectively monitoring the health of planetary stage components. The focus of this paper is the vibration behavior of planetary stage gear related damage and how this behavior can be accurately identified using vibration data. The theory behind this behavior and a case history showing the successful detection of planet gear damage and ring gear damage is presented. The damage detailed in this case is clearly identifiable in the data provided by the ADAPT.Wind condition monitoring system. Although this type of damage requires a gearbox replacement, prompt detection is important to avoid the risk of splitting the gearbox casing and damaging additional wind turbine components.

Wind Turbine Planetary Gear Fault Identification Using Statistical Condition Indicators and Machine Learning

2019 ◽  
Author(s):  
Cédric Peeters ◽  
Timothy Verstraeten ◽  
Ann Nowé ◽  
Jan Helsen
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Wind Turbine ◽  
Health Indicators ◽  
Planetary Gear ◽  
Wind Turbine Gearbox ◽  
Data Set ◽  
Vibration Data ◽  
Gearbox Vibration ◽  
Gear Fault

Abstract This work describes an automated condition monitoring framework to process and analyze vibration data measured on wind turbine gearboxes. The current state-of-the-art in signal processing often leads to a large quantity in health indicators thanks to the multiple potential pre-processing steps. Such large quantities of indicators become unfeasible to inspect manually when the data volume and the number of monitored turbines increases. Therefore, this paper proposes a hybrid analysis approach that combines advanced signal processing methods with machine learning and anomaly detection. This approach is investigated on an experimental wind turbine gearbox vibration data set. It is found that the combination of physics-based statistical indicators with machine learning is capable of detecting planetary gear stage damage and significantly simplifying the data analysis and inspection in the process.

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