Shreekanth Mandayam

An architecture for intelligent systems based on smart sensors

Based on requirements for a next-generation rocket test facility, elements of a prototype IRTF have been implemented. A key component is distributed smart sensor elements integrated using a knowledgeware environment. One of the specific goals is to imbue sensors with the intelligence needed to perform self-diagnosis of health and to participate in a hierarchy of health determination at sensor, process, and system levels. The preliminary results provide the basis for future advanced development and validation using rocket test facilities at Stennis Space Center (SSC) 1. We have identified issues important to further development of health-enabled networks, which should be of interest to others working with smart sensors and intelligent health management systems.

Characterization of gas pipeline inspection signals using wavelet basis function neural networks

Magnetic flux leakage techniques are used extensively to detect and characterize defects in natural gas transmission pipelines. This paper presents a novel approach for training a multiresolution, hierarchical wavelet basis function (WBF) neural network for the three-dimensional characterization of defects from magnetic flux leakage signals. Gaussian radial basis functions and Mexican hat wavelet frames are used as scaling functions and wavelets respectively. The centers of the basis functions are calculated using a dyadic expansion scheme and a k-means clustering algorithm. The results indicate that significant advantages over other neural network based defect characterization schemes could be obtained, in that the accuracy of the predicted defect profile can be controlled by the resolution of the network. The feasibility of employing a WBF neural network is demonstrated by predicting defect profiles from both simulation data and experimental magnetic flux leakage signals.

Invariance transformations for magnetic flux leakage signals

Magnetic flux leakage (MFL) methods are used extensively for inspecting ferromagnetic materials. The analysis of the MFL signal is however fraught with problems associated with the sensitivity of the signal to a number of factors such as the MFL sensor velocity and variations in the permeability of the test specimen. The interpretation can be simplified if the signals can be processed to attain invariance to these conditions. This paper presents novel methods for obtaining permeability invariant and velocity invariant MFL signals.

Solution of inverse problems in electromagnetic NDE using finite element methods

This paper presents a technique for solving inverse problems in magnetostatic nondestructive evaluation (NDE), using finite element models. In-line inspection of ferromagnetic gas pipelines containing pipe-wall defects, is chosen as the candidate NDE process. The signal inversion technique consists of iteratively solving the forward problem by updating the finite element mesh, rather than the material properties of the finite elements. Preliminary simulation results obtained using a 2D finite element model are presented.

Wavelet-based permeability compensation technique for characterizing magnetic flux leakage images

The magnetic flux leakage method, used for nondestructive evaluation of ferromagnetic objects, generates greyscale images that are representative of the integrity of the specimen. Defective areas typically appear as bright regions in the image. Unfortunately, the task of defect characterization becomes more challenging due to the effects of variations in the test parameters associated with the experiment. One such test parameter is the permeability of the test object. Conventional invariant pattern recognition algorithms are not capable of performing invariance transformations to compensate for such variations. This paper describes a novel technique that uses wavelet basis functions to provide selective invariant features and eliminate image intensity variations from undesirable changes in operational variables. The performance of the invariance transformation is demonstrated by applying the method to magnetic flux leakage images obtained using a finite element simulation of in-line inspection of natural gas transmission pipelines.

Multiple classifier systems for multisensor data fusion

We have previously introduced Learn ++ , an en- semble of classifiers based algorithm capable of incre- mental learning from additional data, and pointed to its fea- sibility in data fusion applications. In this contribution, we provide additional details, updated results and insight on how such a system can be used in integrating complemen- tary knowledge provided by different data sources obtained from different sensors. Essentially, the algorithm generates an ensemble of classifiers using data from each source, and combines these classifiers using a weighted voting proce- dure. The weights are determined based on the individual classifiers training performance as well as the observed or predicted reliability of each data source.

Creating an Agile ECE Learning Environment Through Engineering Clinics

To keep up with rapidly advancing technology, numerous innovations to the electrical and computer engineering (ECE) curriculum, learning methods and pedagogy have been envisioned, tested, and implemented. It is safe to say that no single approach will work for all of the diverse ECE technologies and every type of learner. However, a few key innovations appear useful in keeping undergraduate students motivated to learn, resilient to technology evolution, and oriented amid the overload of new information and ECE applications. Engineering clinics, similar to their medical clinic counterparts, provide project-based experiences within the core of an ECE education that enable transformation of the entire curriculum toward an outcomes-oriented, student-centered, total-quality environment. Clinics and project-based learning approaches build skills that give the students confidence and motivation to continuously self-learn and adapt as the technologies around them give way to new, more effective paradigms. Perhaps more importantly, engineering clinic experiences provide numerous opportunities for students to experience the holism of true engineering problem-solving approaches and the ranges of potential technology solutions. This paper reviews the clinic innovations that will enable ECE education to become more effective in the midst of the present plethora of information and technology. Assessment results are provided and are very encouraging. This paper concludes that agile learning environments, created to graduate engineers who can be rapidly productive in the professional and research worlds, are enhanced by clinic and/or project-based learning experiences in the ECE curriculum.

Application of wavelet basis function neural networks to NDE

This paper presents a novel approach for training a multiresolution, hierarchical wavelet basis function neural network. Such a network can be employed for characterizing defects in gas pipelines which are inspected using the magnetic flux leakage method of nondestructive testing. The results indicate that significant advantages over other neural network based defect characterization schemes could be obtained, in that the accuracy of the predicted defect profile can be controlled by the resolution of the network. The centers of the basis functions are calculated using a dyadic expansion scheme and a hybrid learning method. The performance of the network is demonstrated by predicting defect profiles from experimental magnetic flux leakage signals.

The “Intelligent” Valve: A Diagnostic Framework for Integrated System-Health Management of a Rocket-Engine Test Stand

Valves play a critical role in rocket-engine test stands because they are essential for the cryogen transport mechanisms that are vital to test operations. Sensors that are placed on valves monitor the pressure, temperature, flow rate, valve position, and any other features that are required for diagnosing their functionality. Integrated system-health management (ISHM) algorithms have been used to identify and evaluate anomalous operating conditions of systems and subsystems (e.g., valves and valve components) on complex structures, such as rocket test stands. In order for such algorithms to be useful, there is a need to develop realistic models for the most common and problem-prone elements. Furthermore, the user needs to be provided with efficient tools to explore the nature of the anomaly and its possible effects on the element, as well as its relationship to the overall system state. This paper presents the development of an intelligent-valve framework that is capable of tracking and visualizing events of the large linear actuator valve (LLAV) in order to detect anomalous conditions. The framework employs a combination of technologies, including a dynamic data exchange data-transfer protocol, autoassociative neural networks, empirical and physical models, and virtual-reality environments. The diagnostic procedure that is developed has the ability to be integrated into existing ISHM systems and can be used for assessing the integrity of rocket-engine test-stand components.

Signal Processing for In-Line Inspection of Gas Transmission Pipelines

Gas transmission pipelines in the United States are primarily inspected using the magnetic flux leakage (MFL) nondestructive evaluation (NDE) technique. However, accurate analysis of the NDE signals in terms of the underlying defects requires a thorough knowledge of various operational parameters such as B-H characteristics of the pipe wall, the velocity of the scanning tool, etc. In certain situations, information about such operational parameters is either absent or hard to obtain. Appropriate signal processing techniques can be applied to the raw MFL signals to ensure that defect characterization is possible in spite of local variations in the test situation. This paper presents two such signal processing methods—one, to compensate the MFL signal for variations in pipe-material grade, and the other to remove the effects of signal distortion that occur due to the velocity of the scanning device.