scholarly journals Early Fault Detection of Wind Turbines Based on Operational Condition Clustering and Optimized Deep Belief Network Modeling

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 984 ◽  
Author(s):  
Hong Wang ◽  
Hongbin Wang ◽  
Guoqian Jiang ◽  
Jimeng li ◽  
Yueling Wang
Keyword(s):  
Fault Detection ◽  
Wind Turbines ◽  
Health Monitoring ◽  
Deep Belief Network ◽  
Operating Conditions ◽  
Behavior Modeling ◽  
Operation Condition ◽  
Belief Network ◽  
Operation Conditions ◽  
Early Fault Detection

Health monitoring and early fault detection of wind turbines have attracted considerable attention due to the benefits of improving reliability and reducing the operation and maintenance costs of the turbine. However, dynamic and constantly changing operating conditions of wind turbines still pose great challenges to effective and reliable fault detection. Most existing health monitoring approaches mainly focus on one single operating condition, so these methods cannot assess the health status of turbines accurately, leading to unsatisfactory detection performance. To this end, this paper proposes a novel general health monitoring framework for wind turbines based on supervisory control and data acquisition (SCADA) data. A key feature of the proposed framework is that it first partitions the turbine operation into multiple sub-operation conditions by the clustering approach and then builds a normal turbine behavior model for each sub-operation condition. For normal behavior modeling, an optimized deep belief network is proposed. This optimized modeling method can capture the sophisticated nonlinear correlations among different monitoring variables, which is helpful to enhance the prediction performance. A case study of main bearing fault detection using real SCADA data is used to validate the proposed approach, which demonstrates its effectiveness and advantages.

SCADA data as a powerful tool for early fault detection in wind turbine gearboxes

2020 ◽  
pp. 0309524X2096941
Author(s):  
Khaled Taha Abd-Elwahab ◽  
Ali Ahmed Hassan
Keyword(s):  
Wind Speed ◽  
Fault Detection ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Comparison Method ◽  
Operating Conditions ◽  
Tree Model ◽  
Aggregated Model ◽  
Early Fault Detection ◽  
Abnormal Performance

The different operating conditions of wind turbines pose great challenges for efficient and reliable fault detection. Therefore, a good analysis of wind turbine data is essential in assessing the state of the wind turbines, since the traditional threshold cannot provide a timely warning as it indicates that the malfunction has already occurred. This paper presents a new method for analyzing the actual data of the turbines, using aggregated model consisting of the neighborhood comparison method, K-means clustering and decision tree model to diagnose faults. The wind speed of the adjacent turbines is compared with each other, then other parameters of the same wind speed are also compared with each other. The purpose of comparison is that, the wind turbines which are similar in wind speed are similar in performance as well. This approach helps us to discover the abnormal data for turbine performance with in the normal operating range. The abnormal performance of any turbine destroys the similarity relationship between its data and the neighboring unit’s data. The main advantage of this approach is the possibility to detect the beginning of abnormal performance in real time, a case study using real SCADA data is used to validate this approach, which demonstrates its effectiveness and advantages.

scholarly journals Power efficiency estimation based health monitoring and fault detection of modular and reconfigurable robot

2021 ◽  
Author(s):  
Jing Yaun
Keyword(s):  
Fault Detection ◽  
Health Monitoring ◽  
Power Efficiency ◽  
Joint Torque ◽  
Robot System ◽  
Operation Conditions ◽  
Efficiency Estimation ◽  
Joint Torque Sensor ◽  
Reconfigurable Robot ◽  
Critical Components

Power efficiency degradation of machines often provides intrinsic indication of problems associated with their operation conditions. Inspired by this observation, in this thesis work, a simple yet effective power efficiency estimation base health monitoring and fault detection technique is proposed for modular and reconfigurable robot with joint torque sensor. The design of the Ryerson modular and reconfigurable robot system is first introduced, which aims to achieve modularity and compactness of the robot modules. Critical components, such as the joint motor, motor driver, harmonic drive, sensors, and joint brake, have been selected according to the requirement. Power efficiency coefficients of each joint module are obtained using sensor measurements and used directly for health monitoring and fault detection. The proposed method has been experimentally tested on the developed modular and reconfigurable robot with joint torque sensing and a distributed control system. Experimental results have demonstrated the effectiveness of the proposed method.

scholarly journals A Deep Belief Network-based Fault Detection Method for Nonlinear Processes

2018 ◽  
Vol 51 (24) ◽  
pp. 9-14 ◽  
Author(s):  
Peng Tang ◽  
Kaixiang Peng ◽  
Kai Zhang ◽  
Zhiwen Chen ◽  
Xu Yang ◽  
...  
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Deep Belief Network ◽  
Nonlinear Processes ◽  
Belief Network

Adaptive fault detection of the bearing in wind turbine generators using parameterless empirical wavelet transform and margin factor

2019 ◽  
Vol 25 (6) ◽  
pp. 1263-1278 ◽  
Author(s):  
Wei Teng ◽  
Wei Wang ◽  
Haixing Ma ◽  
Yibing Liu ◽  
Zhiyong Ma ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Fault Detection ◽  
Wind Turbines ◽  
Vibration Signal ◽  
Scale Space ◽  
Operating Conditions ◽  
Turbine Generators ◽  
Detection Approach ◽  
Empirical Wavelet Transform ◽  
Fault Information

Wind turbines revolve in difficult operating conditions due to stochastic loads and produce massive vibration signals, which cause obstacles in detecting potential fault information. To overcome this, an adaptive fault detection approach is presented in this paper on the basis of parameterless empirical wavelet transform (PEWT) and the margin factor. PEWT can decompose the vibration signal into a series of empirical modes (EMs) through splitting its Fourier spectrum, using the scale space method and adaptively constructing an orthogonal wavelet filter bank. The margin factor is utilized as a key metric for automatically selecting the EM which is sensitive to the potential faults. The method presented in this paper will improve the efficiency and accuracy of fault information for the condition monitoring of wind turbines.

scholarly journals Power efficiency estimation based health monitoring and fault detection of modular and reconfigurable robot

2021 ◽  
Author(s):  
Jing Yaun
Keyword(s):  
Fault Detection ◽  
Health Monitoring ◽  
Power Efficiency ◽  
Joint Torque ◽  
Robot System ◽  
Operation Conditions ◽  
Efficiency Estimation ◽  
Joint Torque Sensor ◽  
Reconfigurable Robot ◽  
Critical Components

Power efficiency degradation of machines often provides intrinsic indication of problems associated with their operation conditions. Inspired by this observation, in this thesis work, a simple yet effective power efficiency estimation base health monitoring and fault detection technique is proposed for modular and reconfigurable robot with joint torque sensor. The design of the Ryerson modular and reconfigurable robot system is first introduced, which aims to achieve modularity and compactness of the robot modules. Critical components, such as the joint motor, motor driver, harmonic drive, sensors, and joint brake, have been selected according to the requirement. Power efficiency coefficients of each joint module are obtained using sensor measurements and used directly for health monitoring and fault detection. The proposed method has been experimentally tested on the developed modular and reconfigurable robot with joint torque sensing and a distributed control system. Experimental results have demonstrated the effectiveness of the proposed method.

Real-Time Fault Detection Application for Natural Gas Pipelines

2012 ◽  
Author(s):  
Melitsa J. Torres ◽  
Jose D. Posada ◽  
Jaime R. Garcia ◽  
Marco E. Sanjuan
Keyword(s):  
Natural Gas ◽  
Fault Detection ◽  
Real Time ◽  
Principal Component ◽  
Simulation Software ◽  
Third Party ◽  
Normal Operation ◽  
Operation Condition ◽  
Detection Techniques ◽  
Operation Conditions

The implementation of fault detection techniques in industrial systems for process monitoring has proven to be a useful tool to process operators supervising the plant’s operation conditions. As plants become more instrumented, more data is available for fault detection applications, if they are capable of demonstrate anticipation and low false alarm rates. A regional Natural Gas transportation system deals with these types of drawbacks. While the improvements are carried out, some effort should be done in order to improve the safety in operations. In this paper a data-driven technique was used to detect fault conditions along the pipeline, sectioning it in five partitions to increase the detection sensibility. To overcome the lack of quality in data, simulation software intended to gas controllers training and pipeline operation was used to simulate leaks scenarios. Some historic data with high quality is also used to create normal operation condition models by means of Principal Component Analysis. All simulated faults were detected in a reduced time gap and recent events related to third-party actions showed the tool proficiency to detecting faults in real time. In addition, it considers a fault normalized index per section indicating the fault persistence and aggressiveness in a single plot.

Early fault detection of gas turbine hot components under different ambient and operating conditions

2021 ◽  
pp. 095441002098689
Author(s):  
Jiao Liu ◽  
Jinfu Liu ◽  
Daren Yu ◽  
Zhongqi Wang ◽  
Weizhong Yan ◽  
...  
Keyword(s):  
Fault Detection ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Detection Method ◽  
Operating Conditions ◽  
Exhaust Gas ◽  
Gas Temperature ◽  
Spatial Correlations ◽  
Early Fault Detection ◽  
Exhaust Gas Temperature

Failure of hot components in gas turbines often causes catastrophic results. Early fault detection can prevent serious incidents and improve the availability. A novel early fault detection method of hot components is proposed in this article. Exhaust gas temperature is usually used as the indicator to detect the fault in the hot components, which is measured by several exhaust thermocouples with uniform distribution at the turbine exhaust section. The healthy hot components cause uniform exhaust gas temperature (EGT) profile, whereas the hot component faults could cause the uneven EGT profile. However, the temperature differences between different thermocouple readings are also affected by different ambient and operating conditions, and it sometimes has a greater influence on EGT than the faults. In this article, an accurate EGT model is presented to eliminate the influence of different ambient and operating conditions on EGT. Especially, the EGT profile swirl under different ambient and operating conditions is also included by considering the information of the thermocouples’ spatial correlations and the EGT profile swirl angle. Based on the developed EGT model, the detection performance of early fault detection of hot components in gas turbine is improved. The accuracy and effectiveness of the developed early fault detection method are evaluated by the real-world gas turbine data.

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