Damage detection of wind turbine blade based on wavelet analysis

Zhao, Qian; Li, Wei; Shao, Yichuan; Yao, Xingjia; Tian, Haonan; Zhang, Jing

doi:10.1109/cisp.2015.7408103

Cited by 11 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One main limitation of the CWT is its computational delay; as the wavelets scan through the entire signal, much of the redundant information is analysed [ 70 ]. CWT finds application in TCM and structural health monitoring (SHM), in applications such as machining processes [ 84 , 85 , 86 ] and wind turbine blades [ 83 , 87 ] using acoustic, ultrasonic and accelerometer sensor signals.…”

Section: Signal Processingmentioning

confidence: 99%

See 1 more Smart Citation

Condition Monitoring of Additively Manufactured Injection Mould Tooling: A Review of Demands, Opportunities and Potential Strategies

Weinert

Tormey

O’Hara

et al. 2023

Sensors

View full text Add to dashboard Cite

Injection moulding (IM) is an important industrial process, known to be the most used plastic formation technique. Demand for faster cycle times and higher product customisation is driving interest in additive manufacturing (AM) as a new method for mould tool manufacturing. The use of AM offers advantages such as greater design flexibility and conformal cooling of components to reduce cycle times and increase product precision. However, shortcomings of metal additive manufacturing, such as porosity and residual stresses, introduce uncertainties about the reliability and longevity of AM tooling. The injection moulding process relies on high volumes of produced parts and a minimal amount of tool failures. This paper reviews the demands for tool condition monitoring systems for AM-manufactured mould tools; although tool failures in conventionally manufactured tooling are rare, they do occur, usually due to cracking, deflection, and channel blockages. However, due to the limitations of the AM process, metal 3D-printed mould tools are susceptible to failures due to cracking, delamination and deformation. Due to their success in other fields, acoustic emission, accelerometers and ultrasound sensors offer the greatest potential in mould tool condition monitoring. Due to the noisy machine environment, sophisticated signal processing and decision-making algorithms are required to prevent false alarms or the missing of warning signals. This review outlines the state of the art in signal decomposition and both data- and model-based approaches to determination of the current state of the tool, and how these can be employed for IM tool condition monitoring. The development of such a system would help to ensure greater industrial uptake of additive manufacturing of injection mould tooling, by increasing confidence in the technology, further improving the efficiency and productivity of the sector.

show abstract

Section: Signal Processingmentioning

confidence: 99%

“…Zhao et al [ 87 ] used a four-layer decomposition of an AE signal collected from AE sensors mounted on a wind turbine blade. The monitored frequency range was between 0 kHz and 250 kHz.…”

Section: Signal Processingmentioning

confidence: 99%

Condition Monitoring of Additively Manufactured Injection Mould Tooling: A Review of Demands, Opportunities and Potential Strategies

Weinert

Tormey

O’Hara

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Tsai et al (2009) proposed a continuous wavelet transform based entropy method to enhance the damage-detection capability of wind turbine blades, the approach can form a quantitative index systematically to detect the damage of blades, anticipating formulating a forewarning mechanism for wind power system. Zhao et al (2015) applied the basic theory and algorithm of wavelet analysis and support vector machine (SVM) to extract the feature vector of the fault vibration signal of the blade in wind turbine, which obtained a good effect on identification of the blade crack damage. The above methods are effective in blade fault detection, but additional devices are needed that will enhance the hardware cost.…”

Section: Introductionmentioning

confidence: 99%

Rotor imbalance detection and quantification in wind turbines via vibration analysis

Xu¹,

Ding²,

Gong³

et al. 2021

Wind Engineering

View full text Add to dashboard Cite

Rotor imbalance is a common fault in wind turbines, which may enhance radial loads that induce faults on the main bearing and gearbox. It usually results from the asymmetry of the air dynamics caused by blade crack, icing, etc. A simple and effective method on rotor imbalance detection and quantification is presented using the vibration signal collected from the accelerometer monitoring the wind turbine drive train. A vibration model describing the rotor imbalance under blade crack is proposed. The complex morlet wavelet transform is applied to the detection of the rotational frequency of rotor hub which represents the rotor hub. A health indicator that can quantify the degree of the rotor imbalance is designed. The proposed methods have successfully detected and quantified the rotor imbalance caused by blade crack in an on-site wind turbine.

show abstract

“…In [9] a multi objective optimization model with H ¥ / Hobserver is developed to identify the fault in the sensor and actuator. In the area of application of fault detection the SVM is an efficient technique in machine learning [10], [11], [12] and with this techniques many fault detections are done [13].…”

Section: Introductionmentioning

confidence: 99%

SVM and RVM Fault Model for Wind Energy Conversion Systm

2019

IJRTE

View full text Add to dashboard Cite

The paper present the fault identification technique for wind Energy conversion system based on SVM (support vector machines) and RVM. A Relevance Vector Machine based fault detection technique and support vector machine fault detection technique with the Benchmark Model of the WECS is carried out with Multi class classification... The proposed implementation would carry out simulation which would consider multiple faults occurring simultaneously with a comparison study of both techniques can be achieved. The algorithm is carried out and the results are found to be satisfactory. The results in MATLAB shows that effective memory usage of each technique.

show abstract

Damage detection of wind turbine blade based on wavelet analysis

Cited by 11 publications

References 0 publications

Condition Monitoring of Additively Manufactured Injection Mould Tooling: A Review of Demands, Opportunities and Potential Strategies

Condition Monitoring of Additively Manufactured Injection Mould Tooling: A Review of Demands, Opportunities and Potential Strategies

Rotor imbalance detection and quantification in wind turbines via vibration analysis

SVM and RVM Fault Model for Wind Energy Conversion Systm

Contact Info

Product

Resources

About