A Statistical Method for Crack Detection from Vibrothermography Inspection Data

Abstract

Nondestructive evaluation methods are widely used in quality control processes for critical components in systems such as aircraft engines and nuclear power plants. These same methods are also used in periodic field inspection to assure that the system continues to be reliable while it is in service. This paper describes a detection algorithm to automatically analyze vibrothermography sequence-of-image inspection data used to detect cracks in jet engine fan blades. Principal components analysis is used for dimension reduction. Then the fitted coefficients of the first principal component are processed by using robust regression to produce studentized residuals that are used in the crack-detection rules. We also show how to quantify the probability of detection for the algorithm. The detection algorithm is both computationally efficient and accurate. It correctly identified several cracks in our experimental data that were not detected by the standard human visual inspection method. AbstractNondestructive evaluation methods are widely used in quality control processes for critical components in systems such as aircraft engines and nuclear power plants. These same methods are also used in periodic field inspection to assure that the system continues to be reliable while it is in service. This paper describes a detection algorithm to automatically analyze vibrothermography sequence-of-image inspection data used to detect cracks in jet engine fan blades. Principal components analysis is used for dimension reduction. Then the fitted coefficients of the first principal component are processed by using robust regression to produce studentized residuals that are used in the crack-detection rules. We also show how to quantify the probability of detection for the algorithm. The detection algorithm is both computationally efficient and accurate. It correctly identified several cracks in 1 2 our experimental data that were not detected by the standard human visual inspection method.

Review of Recent Advances in the Application of the Wavelet Transform to Diagnose Cracked Rotors

“…Figure 5 shows the mean POD(a) curve together with the 95% confidence bound, and the 90/95 crack size (the probability of detection is 90% and the confidence bound is 95%) [72]. Recently, several works regarding crack detection in rotors have used POD curves to present diagnosis results, examples of these can be found in [33,[73][74][75].…”

Big data and reliability applications: The complexity dimension

“…For example, infrared cameras can be used to track the change of a thermal field. Although it is not directly related to degradation, vibrothermography data from Gao and Meeker (2012) in Figure 4 illustrate the evolution of a process over a twodimensional spatial region. From frame 8, one starts to see a signal in the middle of the region and the signal becomes evident in frame 16.…”

A Statistical Framework for Improved Automatic Flaw Detection in Nondestructive Evaluation Images

“…Although the vibrothermography technology is still in its infancy, especially when compared to NDE images obtained using ultrasonic or radiographic methods, a commonly-used pre-processing data reduction technique (essentially eliminating the temporal dimension) is to use the image frame with the largest contrast (highest signal) (Li et al, 2010(Li et al, , 2011Gao and Meeker, 2012) before analysis.…”

“…There is substantial and long-standing interest in the development of statistical methodology and algorithms for the analysis of NDE data (e.g., see Berens and Hovey, 1981, 1982, 1983, 1984Gray and Thompson, 1986;Annis and Erland, 1989;Burkel et al, 1996;Hovey and Berens, 1988;Perdijon, 1988aPerdijon, ,b, 1989Neal and Speckman, 1993;Howard and Gilmore, 1994;Sweeting, 1995;Spencer and Schurman, 1995;Olin and Meeker, 1996;Howard et al, 1998;Aoki and Suga, 1999;Zaki et al, 2001;Legendre et al, 2001;Meyer and Candy, 2002;Zavaljevski et al, 2005;Dogandzic and Zhang, 2007;Hasanzadeh et al, 2008;Li et al, 2010;Gao and Meeker, 2012;Ng et al, 2013). Much of the NDE literature has revolved around the issues of noise reduction and comcomitant increased signal-to-noise ratio (SNR) and development of methodologies (e.g., development of wavelet methods, expectation-maximization algorithm-type methods) for better estimating the extent and probability of detection (POD) of a flaw in different techniques.…”