Tariq Khan

A Recursive Bayesian Estimation Method for Solving Electromagnetic Nondestructive Evaluation Inverse Problems

Estimating flaw profiles from measurements is a typical inverse problem in electromagnetic nondestructive evaluation (NDE). This paper proposes a novel state-space approach for solving such inverse problems. The approach is robust in the presence of measurement noise. It formulates the inverse problem as a tracking problem with state and measurement equations. The state-space model resembles the classical discrete-time tracking problem. The model allows recursive Bayesian nonlinear filters based on sequential Monte Carlo methods to be applied in conjunction with numerical models that represent the measurement process (i.e., solution of the forward problem). We apply our approach to simulated eddy-current and magnetic flux leakage NDE measurements (with and without measurement noise) from known flaw shapes, and the results indicate the feasibility and robustness of the proposed method.

Particle-Filter-Based Multisensor Fusion for Solving Low-Frequency Electromagnetic NDE Inverse Problems

Flaw profile characterization from nondestructive evaluation (NDE) measurements is a typical inverse problem. A novel transformation of this inverse problem into a tracking problem and subsequent application of a sequential Monte Carlo method called particle filtering has been proposed by the authors in an earlier publication. In this paper, the problem of flaw characterization from multisensor data is considered. The NDE inverse problem is posed as a statistical inverse problem, and particle filtering is modified to handle data from multiple measurement modes. The measurement modes are assumed to be independent of each other with principal component analysis used to legitimize the assumption of independence. The proposed particle-filter-based data fusion algorithm is applied to experimental low-frequency NDE data to investigate its feasibility.

Sequential Monte Carlo Methods for Electromagnetic NDE Inverse Problems—Evaluation and Comparison of Measurement Models

Flaw profile estimation from measurements is a typical inverse problem in electromagnetic nondestructive evaluation (NDE). The application of recursive Bayesian nonlinear filters based on sequential Monte Carlo methods, in conjunction with measurement process models and a Markovian crack growth model, is a new approach for solving such inverse problems. The approach resembles the classical discrete-time tracking problem and is robust to the noisy measurement data. This paper reports a comparative study of the results of employing different measurement models in this Bayesian inversion framework. The results are evaluated on the basis of accuracy and computational cost.

PARTICLE FILTER BASED MULTISENSOR FUSION FOR SOLVING ELECTROMAGNETIC NDE INVERSE PROBLEMS

Flaw profile estimation from measurements is a typical inverse problem in electromagnetic nondestructive evaluation (NDE). The increasing availability of multiple measurement modes in NDE requires the development of multisensor data fusion algorithms to solve the NDE inverse problem. This paper proposes a multisensor data fusion algorithm for flaw profiling, based on a recursive state space approach. The problem of flaw profile estimation from given multisensor data is formulated using multiple measurement process models and a state transition model. This formulation enables the application of Bayesian non‐linear filters based on sequential Monte Carlo methods. The new approach is computationally efficient if computationally simple measurement models are employed. Moreover, the technique is robust to noisy measurement data. The initial results indicate significant improvement in the accuracy of inversion results when more than one type of measurement data is used for flaw profile estimation.

WIRELESS NDE SENSOR SYSTEM FOR CONTINUOUS MONITORING

For continuous monitoring of power‐plant components, the use of in‐situ sensors (i.e., sensors that are permanently mounted on the structure) is necessary. In‐situ wired sensors require an unrealistic amount of cabling for power and data transfer, which can drive up costs of installation and maintenance. In addition, the use of cabling in hostile environments (high temperature/pressure environments) is not a viable option. This paper presents a wireless system for continuous monitoring, identification of anomalous events, NDE data acquisition and data transfer. NDE sensors are integrated with a wireless radio unit such as a MICA mote. Measurements from the sensors are typically acquired at prescribed intervals, encoded and compressed, and transmitted to a central processing server, where appropriate signal processing techniques may be used to filter out noise in the measurements, enhance the desired signal and quantify the damage in terms of severity.

Assessment of nuclear fuel pellets using X-ray tomography

Fuel rods in nuclear power plants consist of uranium dioxide pellets enclosed in Zirconium alloy (Zircaloy) tubes. It is vitally important for the pellet surface to remain free fr om pits, cracks and chipping defects after it is loaded into t he tubes to prevent local hot spots during reactor operation. The inspection of the fuel rod presents several challenges. In this pa per, we investigate the feasibility of using X-ray tomography inspection to detect flaws on the surface of pellets contained wit hin the tubes using very limited number of projections.

Particle filter based multisensor fusion for flaw shape reconstruction in steam generator NDE

Determination of flaw profiles from electromagnetic nondestructive evaluation (NDE) measurements is a common inverse problem. Electromagnetic NDE methods are used extensively for the examination of critical components in a number of industries ranging from defense, to energy, petrochemical, and transportation industries. Steam generator tubing inspection is a typical NDE case study. Steam generator tubes in nuclear power plants are continuously exposed to harsh environmental conditions including high temperatures, pressures, fluid flow rates and material interactions resulting in various types of degradation mechanisms such as mechanical wear, stress corrosion cracking (SCC), pitting, and inter granular attack (IGA) [1]. These flaws typically result in tube thinning and development of multiple crack-like flaws, thereby providing a mechanism for contaminating fluids on the secondary side. Consequently steam generator tubes in nuclear power plants need to be inspected periodically for cracks or leaks. Historically, steam generator tube inspection has been a difficult problem. There have been numerous cases of unscheduled plant shutdowns in the past, which typically cost over

EXPERIMENTAL NOISE INJECTION IN SIMULATED MODEL SIGNALS

500,000 a day. Hence there is a strong economic incentive to develop reliable nondestructive evaluation (NDE) methods for steam generator tube inspection. Eddy current inspection [2] has proven to be both fast and effective in detecting and sizing most of the degradation mechanisms that occurred in the early generators. However, as the nations generators have aged over years, newer and much more subtle forms of degradation have appeared that require more intelligent application of eddy current tests such as use of multi-frequency eddy current data.

Near-field acoustic holography for acoustic noise source identification in turbomachinery

Nondestructive testing methods have been routinely, designed, evaluated and optimized using simulation models developed using various computational techniques. The simulated signal using computational model differs from the true signal in that the signal does not simulated experimental noise. In order to use the computational models more effectively for signal processing algorithm development, experimental noise should be injected in the simulated signals. Experimental noise PDF (probability density function) can be numerically calculated from measured noise. The experimental signal PDF can then be generated by combining the simulation signal and measurement noise PDF. Sampling from experimental signal distribution is not a straight forward task as the distribution is generally not a standard parametric distribution. This paper presents a method that approximates experimental signal PDF as a mixture of Gaussian densities. Maximum‐likelihood estimate of the parameters of Gaussian distributions from a given...

ONLINE BAYESIAN ESTIMATION FOR SOLVING ELECTROMAGNETIC NDE INVERSE PROBLEMS

A novel approach using basis expansions of acoustic sources is proposed to better condition the inverse problem in NAH. Results indicate that the basis expansion representation of sources provides better source reconstruction results through effectively combating ill-posedness of the inverse problem. The source reconstruction accuracy in direct inversion increases if the measurement data is smoothed prior to inversion [12]. The effects of employing basis expansion technique on denoised/ smoothed data will be studied in future