Gerges Dib

Rotating Field EC-GMR Sensor for Crack Detection at Fastener Site in Layered Structures

Eddy current-based techniques have been investigated for the inspection of embedded cracks under fastener heads in riveted structures. However, these techniques are limited in their ability to detect cracks that are not perpendicular to induced current flows. Further, the presence of a steel fastener of high permeability produces a strong signal that masks relatively smaller indication from a crack. In this paper, a rotating electromagnetic field is designed to rotate the applied magnetic fields and related eddy currents electrically so that the sensor shows uniform sensitivity in detecting cracks in all radial directions around fastener sites. Giant magnetoresistive sensors are employed to image the normal component of this rotating field, to detect different crack orientations at aluminum and ferromagnetic fastener sites. Numerical model-based studies and experimental validation are presented.

Nonlinear, non-stationary image processing technique for eddy current NDE

Automatic analysis of eddy current (EC) data has facilitated the analysis of large volumes of data generated in the inspection of steam generator tubes in nuclear power plants. The traditional procedure for analysis of EC data includes data calibration, pre-processing, region of interest (ROI) detection, feature extraction and classification. Accurate ROI detection has been enhanced by pre-processing, which involves reducing noise and other undesirable components as well as enhancing defect indications in the raw measurement. This paper presents the Hilbert-Huang Transform (HHT) for feature extraction and support vector machine (SVM) for classification. The performance is shown to significantly better than the existing rule based classification approach used in industry.

Design and performance of optimal detectors for guided wave structural health monitoring

Ultrasonic guided wave measurements in structural health monitoring systems are affected over a long term by measurement noise, environmental conditions, transducer aging, and malfunction. This results in measurement variability which affects detection performance, especially in complex structures where baseline data comparison is required. This article derives the optimal detector structure, within the framework of detection theory, based on reducing a guided wave signal at the sensor into a single feature value that can be used for comparison with a threshold. Three different types of detectors are derived depending on the underlying structure’s complexity: (a) simple structures where defect reflections can be identified without the need for baseline data; (b) simple structures that require baseline data due to overlap of defect scatter with scatter from structural features; and (c) complex structure with dense structural features that require baseline data. The detectors are derived by modeling the effects of variabilities and uncertainties as random processes. Analytical solutions for the performance of detectors in terms of the probability of detection and false alarm are derived. A finite element model that simulates guided wave inspection is used in a Monte-Carlo procedure to quantify the effects of environmental variability in terms of defect probability of detection. Results demonstrate that the problems of structural complexity and environmental variability introduce temporal diversity in the signals, which can be exploited to improve detection performance.

EC-GMR array with rotating current excitation for multilayered riveted structures inspection

The challenge in detecting crack under fastener heads (CUF) in a multi-layered aircraft structure poses the need for advanced NDE technology. Our previous work has presented the feasibility of eddy current (EC) technology using giant magnetoresistive (GMR) sensors in detecting 2nd layer hidden cracks in layered aircraft components. An EC-GMR inspection system has been developed to directly measure the normal component of magnetic flux density associated with eddy currents induced inside the specimen. However, a major limitation of current sensor system is in detecting cracks that are parallel to the direction of induced currents. This paper presents a new design using orthogonal excitation coils for generating a rotating uniform current, which provides uniform sensitivity to cracks emanating in all orientations around fastener sites. The design and inspection using the orthogonal coil probe and GMR sensor is presented using a simulation model. Several candidate designs for the orthogonal coil configuratio...

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.

Experimental validation of ultrasonic NDE simulation software

Computer modeling and simulation is becoming an essential tool for transducer design and insight into ultrasonic nondestructive evaluation (UT-NDE). As the popularity of simulation tools for UT-NDE increases, it becomes important to assess their reliability to model acoustic responses from defects in operating components and provide information that is consistent with in-field inspection data. This includes information about the detectability of different defect types for a given UT probe. Recently, a cooperative program between the Electrical Power Research Institute and the U.S. Nuclear Regulatory Commission was established to validate numerical modeling software commonly used for simulating UT-NDE of nuclear power plant components. In the first phase of this cooperative, extensive experimental UT measurements were conducted on machined notches with varying depth, length, and orientation in stainless steel plates. Then, the notches were modeled in CIVA, a semi-analytical NDE simulation platform develope...

Multitechnique monitoring of fatigue damage in adhesively bonded composite lap-joints

The requirement for reduced structural weight has driven the development of adhesively bonded joints. However, a major issue preventing their full acceptance is the initiation of premature failure in the form of a disbond between adherends, mainly due to fatigue, manufacturing flaws or impact damage. This work presents the integrated approach for in-situ monitoring of degradation of the adhesive bond in the GFRP composite lap-joint using ultrasonic guided waves and dynamic measurements from strategically embedded FBG sensors. Guided waves are actuated with surface mounted piezoelectric elements and mode tuning is used to provide high sensitivity to the degradation of the adhesive layer parameters. Composite lap-joints are subjected to fatigue loading, and data from piezoceramic transducers are collected at regular intervals to evaluate the progression of damage. Results demonstrate that quasi-static loading affects guided wave measurements considerably, but FBG sensors can be used to monitor the applied l...

Feasibility of PZT ceramics for impact damage detection in composite structures

Fiber reinforced plastic composites are becoming widely used in vehicles and airframe structures due to their high strength to weight ratio. However unlike metals, the multilayered composite structures are more susceptible to damage mechanisms such as disbonds and delaminations due to impacts. It is often difficult to visually detect the damage. Lead-Zirconate-Titanate (PZT) thin films are becoming popular for in-situ structural health monitoring due to their small size, high piezoelectric coupling coefficient, and ease of surface-mounting and/or embedding in composite structures. A network of such transducers could be utilized for damage detection using guided wave techniques, impedance techniques, or passive impact detection techniques. However, the PZT films are subject to the same impact probabilities that the structure encounters. If the transducers fail due to the subjected impacts, they can result in false readings and ultimately failing to correctly detect damage in the structure. This paper prese...

Image processing algorithms for automated analysis of GMR data from inspection of multilayer structures

Eddy current probes (EC) with Giant Magnetoresistive (GMR) sensors have recently emerged as a promising tool for rapid scanning of multilayer aircraft panels that helps detect cracks under fastener heads. However, analysis of GMR data is challenging due to the complexity of sensed magnetic fields. Further, probes that induce unidirectional currents are insensitive to cracks parallel to the current flow. In this paper, signal processing algorithms are developed for mixing data from two orthogonal EC-GMR scans in order to generate pseudo-rotating electromagnetic field images of fasteners with bottom layer cracks. Finite element simulations demonstrate that the normal component of numerically computed rotating field has uniform sensitivity to cracks emanating in all radial directions. The concept of pseudo-rotating field imaging is experimentally validated with the help of MAUS bilateral GMR array (Big-MR) designed by Boeing.

Summary Describing Integration of ERM Methodology into Supervisory Control Framework with Software Package Documentation; Advanced Reactor Technology Milestone: M4AT-16PN2301052

....................................................................................................................................................... iii Acknowledgments ......................................................................................................................................... v Acronyms and Abbreviations ..................................................................................................................... vii 1.0 Introduction ....................................................................................................................................... 1.1 1.1 Research Objectives .................................................................................................................. 1.1 1.2 Research Assumptions .............................................................................................................. 1.1 1.3 Organization of Report .............................................................................................................. 1.2 2.0 Background ........................................................................................................................................ 2.1 2.1 Overview and Operational Characteristics for Advanced Reactors .......................................... 2.1 2.2 Brief Overview of ERMs for Advanced Reactors ..................................................................... 2.3 3.0 Prototypic ERM Framework Evaluation ........................................................................................... 3.1 3.1 Economic Model ....................................................................................................................... 3.1 3.2 Component Aging ..................................................................................................................... 3.1 3.3 Evaluation of Prototypic ERM Framework ............................................................................... 3.2 3.3.1 Changes to Maintenance Strategy .................................................................................. 3.2 3.3.2 Prognostic Result ............................................................................................................ 3.9 3.3.3 Evaluation Summary .................................................................................................... 3.11 4.0 Integration of ERM Module with Supervisory Control System Framework ..................................... 4.1 4.1 Overview ................................................................................................................................... 4.1 4.1.1 ERM Software Design ................................................................................................... 4.1 4.1.2 Evaluation Scenarios ...................................................................................................... 4.1 4.2 Integration Specification ........................................................................................................... 4.2 4.2.1 Valve Prognostic Modules ............................................................................................. 4.3 4.3 Progress Summary..................................................................................................................... 4.3 4.3.1 Integration ...................................................................................................................... 4.4 4.3.2 Evaluation of Valve Prognostic Model .......................................................................... 4.5 5.0 Summary ............................................................................................................................................ 5.1 5.1 Envisioned Role of ERM in O&M for Advanced Reactors ...................................................... 5.1 5.2 Research Contributions towards Advanced Reactor O&M Optimization................................. 5.2 5.2.1 Overview of Achievements during this Project.............................................................. 5.2 5.2.2 Recommended Path Forward ......................................................................................... 5.2 6.0 References ......................................................................................................................................... 6.1 Appendix A – Specification for Integration of Diagnostics and Prognostics Module of Enhanced Risk Monitoring with Supervisory Control System ......................................................................... A.1