Online detection of crack damage in composite plates using embedded piezoelectric actuators/sensors and wavelet analysis

Yan, Yi; Yam, L.H.

doi:10.1016/s0263-8223(02)00043-0

Cited by 105 publications

(60 citation statements)

References 17 publications

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“…Those methods, such as wavelet analysis, empirical modes decomposition (EMD), and Hilbert-Huang Transform (HHT), are more universal and less dependent on the structural shape. The main concept of using wavelet analysis is to break down the dynamic signal of a structural response into a series of local basis function called wavelets, so as to detect the special characteristics of the structure using scaling and transformation property of wavelets [30][31][32][33]. The method based on the EMD and HHT is intended to extract damage spikes due to a sudden change of structural stiffness from the measured data thereby detecting the damage occurring time instants and locations, and determining the natural frequencies and damping ratios of the structure before and after damage [34].…”

Section: Modern Signal Processing Methodsmentioning

confidence: 99%

The State-of-the-Art on Framework of Vibration-Based Structural Damage Identification for Decision Making

2017

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Abstract:Research on detecting structural damage at the earliest possible stage has been an interesting topic for decades. Among them, the vibration-based damage detection method as a global technique is especially pervasive. The present study reviewed the state-of-the-art on the framework of vibration-based damage identification in different levels including the prediction of the remaining useful life of structures and the decision making for proper actions. This framework consists of several major parts including the detection of damage occurrence using response-based methods, building reasonable structural models, selecting damage parameters and constructing objective functions with sensitivity analysis, adopting optimization techniques to solve the problem, predicting the remaining useful life of structures, and making decisions for the next actions. For each part, the commonly used methods were reviewed and the merits and drawbacks were summarized to give recommendations. This framework is aimed to guide the researchers and engineers to implement step by step the structure damage identification using vibration measurements. Finally, the future research work in this field is recommended.

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Section: Modern Signal Processing Methodsmentioning

confidence: 99%

The State-of-the-Art on Framework of Vibration-Based Structural Damage Identification for Decision Making

2017

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“…The disadvantage of this technique is that is not applicable to in-service structures, since the equipment to get the images is very sensitive to external vibrations so the acquisition has to be performed in very controlled conditions. Wavelet Transform is another signal processing technique widely used in guided wave technology, mainly in a pitch-catch configuration (Kessler et al 2002;Yan and Yam 2002;Paget et al 2003). In composite materials, it has been proved that it provides good detectability results.…”

Section: Damage Detectionmentioning

confidence: 99%

Damage Sensing in Blades

Crespo

2016

Mare-Wint

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This chapter is divided into three parts; the problem, possible solutions and the chosen option to address the problem, which is my PhD topic within the project MAREWINT. So firstly, the chapter presents an overview of the typical damages that a wind turbine blade can suffer during its life operation. Then, a review of different Structural Health Monitoring (SHM) techniques which are currently being investigated for wind turbine blades is presented. Finally, the chapter provides the state-of-the-art of Guided Wave Technology in composite materials; where different aspects of this SHM technique are explained in more detail. IntroductionWind energy is an important renewable energy source which has gained high relevance during the last decades. Different countries have released plans to invest in wind energy in the future years; such as the USA that will generate 20 % of the country's electricity from wind power by 2030 or Denmark that have set the targets of producing 50 % of the power from the wind by 2020 and also of making Denmark completely free of dependence on fossil fuels by 2050. So, the use of wind power is not expected to decrease within the next decade (Márquez et al. 2012). The trend is to manufacture bigger wind turbines and deploy them offshore. These new wind turbines have around 6 MW power output, 120-metre height tower and 80-metre long blades. They are designed to be operating in rough conditions in difficult-to-reach areas. Therefore, the deployment of Structural Health Monitoring (SHM) techniques becomes essential in order to assess remotely the integrity of the structure. The advantages of using these techniques are many (Schulz and Sundaresan 2006), such as reducing expensive costs for periodic inspections of turbines which are hard to reach, prevention of unnecessary replacement of components based on time of use, or minimizing down time and frequency of sudden breakdowns.

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“…If the shape analyzing function x(t) is correlating well with the wavelet shape, the resulted coefficient will be larger compared to the wavelet that is not correlating well with the shape of the analyzing function. Due to the uniqueness and the flexibility of the CWT, it is also widely adopted in many other machine or civil structural health monitoring applications involving vibrational signature analysis [27][28][29].…”

Section: Continuous Wavelet Transform the Continuous Waveletmentioning

confidence: 99%

Vibration Signature Analysis and Parameter Extractions on Damages in Gears and Rolling Element Bearings

Shen

Wen

Arunyanart

et al. 2011

ISRN Mechanical Engineering

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This paper is to analyze and identify damage in gear teeth and rolling element bearings by establishing pattern feature parameters from vibration signatures. In the present work, different damage scenarios involving different combinations of gear tooth damage, bearing damage are considered. Each of the damage scenarios are studied and compared in the time domain, the frequency domain, and the joint time-frequency domain using the FM0 technique, the Fourier Transform, the Wigner-Ville Transform, and the Continuous Wavelet Transform, respectively. Results obtained from the three different signal domains are analyzed to develop indicative parameters and visual presentations that measure the integrity and wellness of the bearing and gear components. The joint time-frequency domain obtained from the continuous wavelet transform has shown to be a superior technique for providing clear visual examination solution for different types of component damages as well as for feature extractions used for computerbased machine health monitoring solution.

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Online detection of crack damage in composite plates using embedded piezoelectric actuators/sensors and wavelet analysis

Cited by 105 publications

References 17 publications

The State-of-the-Art on Framework of Vibration-Based Structural Damage Identification for Decision Making

The State-of-the-Art on Framework of Vibration-Based Structural Damage Identification for Decision Making

Damage Sensing in Blades

Vibration Signature Analysis and Parameter Extractions on Damages in Gears and Rolling Element Bearings

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