Health Monitoring of Wind Turbine Blades through Vibration Signal Using Advanced Signal Processing Techniques

2020 ◽  
Author(s):  
Sudarsan Sahoo ◽  
Kuldeep Kushwah ◽  
Arun Kumar Sunaniya
Keyword(s):  
Signal Processing ◽  
Wind Turbine ◽  
Health Monitoring ◽  
Turbine Blades ◽  
Vibration Signal ◽  
Wind Turbine Blades ◽  
Processing Techniques ◽  
Signal Processing Techniques

An experimental investigation into passive acoustic damage detection for structural health monitoring of wind turbine blades

2020 ◽  
Vol 19 (6) ◽  
pp. 1711-1725 ◽  
Author(s):  
Jaclyn Solimine ◽  
Christopher Niezrecki ◽  
Murat Inalpolat
Keyword(s):  
Signal Processing ◽  
Structural Health Monitoring ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Health Monitoring ◽  
Fatigue Testing ◽  
Turbine Blades ◽  
Wind Turbine Blade ◽  
Wind Turbine Blades ◽  
Structural Health

This article details the implementation of a novel passive structural health monitoring approach for damage detection in wind turbine blades using airborne sound. The approach utilizes blade-internal microphones to detect trends, shifts, or spikes in the sound pressure level of the blade cavity using a limited network of internally distributed airborne acoustic sensors, naturally occurring passive system excitation, and periodic measurement windows. A test campaign was performed on a utility-scale wind turbine blade undergoing fatigue testing to demonstrate the ability of the method for structural health monitoring applications. The preliminary audio signal processing steps used in the study, which were heavily influenced by those methods commonly utilized in speech-processing applications, are discussed in detail. Principal component analysis and K-means clustering are applied to the feature-space representation of the data set to identify any outliers (synonymous with deviations from the normal operation of the wind turbine blade) in the measurements. The performance of the system is evaluated based on its ability to detect those structural events in the blade that are identified by making manual observations of the measurements. The signal processing methods proposed within the article are shown to be successful in detecting structural and acoustic aberrations experienced by a full-scale wind turbine blade undergoing fatigue testing. Following the assessment of the data, recommendations are given to address the future development of the approach in terms of physical limitations, signal processing techniques, and machine learning options.

Structural Health Monitoring of Research-Scale Wind Turbine Blades

2013 ◽  
Vol 558 ◽  
pp. 364-373 ◽  
Author(s):  
Stuart G. Taylor ◽  
Kevin M. Farinholt ◽  
Gyu Hae Park ◽  
Charles R. Farrar ◽  
Michael D. Todd ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Embedded Systems ◽  
Wind Turbine ◽  
Health Monitoring ◽  
Turbine Blades ◽  
Field Tests ◽  
Ultrasonic Waves ◽  
Wind Turbine Blades ◽  
Structural Health ◽  
Sensing System

This paper presents ongoing work by the authors to implement real-time structural health monitoring (SHM) systems for operational research-scale wind turbine blades. The authors have been investigating and assessing the performance of several techniques for SHM of wind turbine blades using piezoelectric active sensors. Following a series of laboratory vibration and fatigue tests, these techniques are being implemented using embedded systems developed by the authors. These embedded systems are being deployed on operating wind turbine platforms, including a 20-meter rotor diameter turbine, located in Bushland, TX, and a 4.5-meter rotor diameter turbine, located in Los Alamos, NM. The SHM approach includes measurements over multiple frequency ranges, in which diffuse ultrasonic waves are excited and recorded using an active sensing system, and the blades global ambient vibration response is recorded using a passive sensing system. These dual measurement types provide a means of correlating the effect of potential damage to changes in the global structural behavior of the blade. In order to provide a backdrop for the sensors and systems currently installed in the field, recent damage detection results for laboratory-based wind turbine blade experiments are reviewed. Our recent and ongoing experimental platforms for field tests are described, and experimental results from these field tests are presented. LA-UR-12-24691.

Radar-based structural health monitoring of wind turbine blades: The case of damage detection

2017 ◽  
Vol 17 (4) ◽  
pp. 815-822 ◽  
Author(s):  
Jochen Moll ◽  
Philip Arnold ◽  
Moritz Mälzer ◽  
Viktor Krozer ◽  
Dimitry Pozdniakov ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Wind Turbine ◽  
Health Monitoring ◽  
Turbine Blades ◽  
Heterogeneous Material ◽  
Material System ◽  
Wind Turbine Blades ◽  
Millimetre Wave ◽  
Structural Health

Structural health monitoring of wind turbine blades is challenging due to its large dimensions, as well as the complex and heterogeneous material system. In this article, we will introduce a radically new structural health monitoring approach that uses permanently installed radar sensors in the microwave and millimetre-wave frequency range for remote and in-service inspection of wind turbine blades. The radar sensor is placed at the tower of the wind turbine and irradiates the electromagnetic waves in the direction of the rotating blades. Experimental results for damage detection of complex structures will be presented in a laboratory environment for the case of a 10-mm-thick glass-fibre-reinforced plastic plate, as well as a real blade-tip sample.

Wind Turbine Blade Damage Detection Using Various Machine Learning Algorithms

2016 ◽  
Author(s):  
Taylor Regan ◽  
Rukiye Canturk ◽  
Elizabeth Slavkovsky ◽  
Christopher Niezrecki ◽  
Murat Inalpolat
Keyword(s):  
Machine Learning ◽  
Damage Detection ◽  
Wind Turbine ◽  
Health Monitoring ◽  
Turbine Blades ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Test Rig ◽  
Wind Turbine Blades ◽  
Baseline Characteristics

Wind turbine blades undergo high operational loads, experience variable environmental conditions, and are susceptible to failures due to defects, fatigue, and weather induced damage. These large-scale composite structures are essentially enclosed acoustic cavities and currently have limited, if any, structural health monitoring in practice. A novel acoustics-based structural sensing and health monitoring technique is developed, requiring efficient algorithms for operational damage detection of cavity structures. This paper describes a systematic approach used in the identification of a competent machine learning algorithm as well as a set of statistical features for acoustics-based damage detection of enclosed cavities, such as wind turbine blades. Logistic regression (LR) and support vector machine (SVM) methods are identified and used with optimal feature selection for decision making using binary classification. A laboratory-scale wind turbine with hollow composite blades was built for damage detection studies. This test rig allows for testing of stationary or rotating blades (each fit with an internally located speaker and microphone), of which time and frequency domain information can be collected to establish baseline characteristics. The test rig can then be used to observe any deviations from the baseline characteristics. An external microphone attached to the tower will also be utilized to monitor blade damage while blades are internally ensonified by wireless speakers. An initial test campaign with healthy and damaged blade specimens is carried out to arrive at certain conclusions on the detectability and feature extraction capabilities required for damage detection.

scholarly journals Structural health monitoring for delamination detection and location in wind turbine blades employing guided waves

Wind Energy ◽  
2019 ◽  
Vol 22 (5) ◽  
pp. 698-711 ◽  
Author(s):  
Carlos Quiterio Gómez Muñoz ◽  
Fausto Pedro García Marquez ◽  
Borja Hernandez Crespo ◽  
Kena Makaya
Keyword(s):  
Structural Health Monitoring ◽  
Wind Turbine ◽  
Health Monitoring ◽  
Guided Waves ◽  
Turbine Blades ◽  
Wind Turbine Blades ◽  
Structural Health ◽  
Delamination Detection

In Situ Blade Deflection Monitoring of a Wind Turbine Using a Wireless Laser Displacement Sensor Device within the Tower

2013 ◽  
Vol 558 ◽  
pp. 84-91 ◽  
Author(s):  
Paritosh Giri ◽  
Jung Ryul Lee
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Health Monitoring ◽  
Turbine Blades ◽  
Displacement Sensor ◽  
Laser Displacement Sensor ◽  
Wind Turbine Blades ◽  
Analog To Digital ◽  
Large Numbers ◽  
Transceiver Module

With commercially viable global wind power potential, wind energy penetration is further expected to rise, as will the related problems. One issue is the collision of wind turbine blades with the tower during operation. Structured health monitoring is required to improve operational safety, minimize the risk of sudden failure or total breakdown, ensure reliable power generation, and reduce wind turbine life cycle costs. Large numbers of sensors such as fiber Bragg grating and piezoelectric devices have been attached to the structure, a design that is uneconomical and impractical for use in large wind turbines. This study proposes a single laser displacement sensor (LDS) system in which all of the rotating blades could be cost-effectively evaluated. Contrary to the approach of blade sensor installation, the LDS system is installed in the tower to enable noncontact blade displacement monitoring. The concept of a noncontact sensor and actuator and their energy delivery device installation in the tower will enable various approaches for wind turbine structural health monitoring. Blade bolt loosening causes deflection in the affected blade. Similarly, nacelle tilt or mass loss damage in the blade will result in changes in blade deflection, but the proposed system can identify such problems with ease. With the need of more energy, the sizes of wind blades are getting bigger and bigger. Due to the large size of wind turbine, nowadays wind turbines are installed very high above the ground or water level. It is impractical to monitor the results from LDS through wired connection in these cases. Hence, the wired connection of LDS to base (monitoring) station must be replaced by a wireless solution. This wireless solution is achieved using Zigbee technology. Zigbee operates in the industrial, scientific and medical (ISM) radio bands, typically 2.4 GHz, 915 MHz and 868 MHz. The output from the LDS is fed to the microcontroller which acts as an analog to digital converter. The output from the microcontroller is connected to the Zigbee transceiver module, which transmits the data and at the other end, the zigbee reads the data and displays on the PC from where user can monitor the condition of wind blades.

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