Structural Data Acquisition Using Sensor Network

The development cost of any civil infrastructure is very high; during its life span, the civil structure undergoes a lot of physical loads and environmental effects which damage the structure. Failing to identify this damage at an early stage may result in severe property loss and may become a potential threat to people and the environment. Thus, there is a need to develop effective damage detection techniques to ensure the safety and integrity of the structure. One of the Structural Health Monitoring methods to evaluate a structure is by using statistical analysis. In this study, a civil structure measuring 8 feet in length, 3 feet in diameter, embedded with thermocouple sensors at 4 different levels is analyzed under controlled and variable conditions. With the help of statistical analysis, possible damage to the structure was analyzed. The analysis could detect the structural defects at various levels of the structure.

Prototype and proof of concept for a display system in retail stores

Selling devices on retail stores comes with the big challenge of grabbing the customer’s attention. Nowadays people have a lot of offers at their disposal and new marketing techniques must emerge to differentiate the products. When it comes to smartphones and tablets, those devices can make the difference by themselves, if we use their computing power and capabilities to create something unique and interactive. With that in mind, three prototypes were developed during an internship: a face recognition based Customer Detection, a face tracking solution with an Avatar and interactive cross-app Guides. All three revealed to have potential to be differentiating solutions in a retail store, not only raising the chance of a customer taking notice of the device but also of interacting with them to learn more about their features. The results were meant to be only proof of concepts and therefore were not tested in the real world.

Hardware-software design of embedded systems for intelligent sensing applications

This Thesis wants to highlight the importance of ad-hoc designed and developed embedded systems in the implementation of intelligent sensor networks. As evidence four areas of application are presented: Precision Agriculture, Bioengineering, Automotive and Structural Health Monitoring. For each field is reported one, or more, smart device design and developing, in addition to on-board elaborations, experimental validation and in field tests. In particular, it is presented the design and development of a fruit meter. In the bioengineering field, three different projects are reported, detailing the architectures implemented and the validation tests conducted. Two prototype realizations of an inner temperature measurement system in electric motors for an automotive application are then discussed. Lastly, the HW/SW design of a Smart Sensor Network is analyzed: the network features on-board data management and processing, integration in an IoT toolchain, Wireless Sensor Network developments and an AI framework for vibration-based structural assessment.

Detection of Internal Defects in Concrete Members Using Global Vibration Characteristics

Rock-pocket and honeycomb defects impair overall stiffness, accelerate aging, reduce service life, and cause structural problems in hardened concrete members. Traditional methods for detecting such deficient volumes involve visual observations or localized nondestructive methods, which are labor-intensive, time-consuming, highly sensitive to test conditions, and require knowledge of and accessibility to defect locations. The authors propose a vibration response-based nondestructive technique that combines experimental and numerical methodologies for use in identifying the location and severity of internal defects of concrete members. The experimental component entails collecting mode shape curvatures from laboratory beam specimens with size-controlled rock pocket and honeycomb defects, and the numerical component entails simulating beam vibration response through a finite element (FE) model parameterized with three defect-identifying variables indicating location (x, coordinate along the beam length) and severity of damage (alpha, stiffness reduction and beta, mass reduction). Defects are detected by comparing the FE model predictions to experimental measurements and inferring the low number of defect-identifying variables. This method is particularly well-suited for rapid and cost-effective quality assurance for precast concrete members and for inspecting concrete members with simple geometric forms.

Early detection of idiopathic Parkinson's disease based on magnetic resonance imaging

The diagnosis of idiopathic Parkinson's disease (IPD) is entirely clinical. The fact that neuronal damage begins 5-10 years before occurrence of sub-clinical signs, underlines the importance of preclinical diagnosis. A new approach for in-vivo pathophysiological assessment of IPD-related neurodegeneration was implemented based on recently developed neuroimaging methods. It is based on non- invasive magnetic resonance data sensitive to brain tissue property changes that precede macroscopic atrophy in the early stages of IPD. This research aims to determine the brain tissue property changes induced by neurodegeneration that can be linked to clinical phenotypes which will allow us to create a predictive model for early diagnosis in IPD. We hypothesized that the degree of disease progression in IPD patients will have a differential and specific impact on brain tissue properties used to create a predictive model of motor and non-motor impairment in IPD. We studied the potential of in-vivo quantitative imaging sensitive to neurodegeneration- related brain tissue characteristics to detect changes in patients with IPD. We carried out methodological work within the well established SPM8 framework to estimate the sensitivity of tissue probability maps for automated tissue classification for detection of early IPD. We performed whole-brain multi parameter mapping at high resolution followed by voxel-based morphometric (VBM) analysis and voxel-based quantification (VBQ) comparing healthy subjects to IPD patients. We found a trend demonstrating non-significant tissue property changes in the olfactory bulb area using the MT and R1 parameter with p<0.001. Comparing to the IPD patients, the healthy group presented a bilateral higher MT and R1 intensity in this specific functional region. These results did not correlate with age, severity or duration of disease. We failed to demonstrate any changes with the R2* parameter. We interpreted our findings as demyelination of the olfactory tract, which is clinically represented as anosmia. However, the lack of correlation with duration or severity complicates its implications in the creation of a predictive model of impairment in IPD.

Detection of beetle damage in forests by X-ray CT image processing

Some beetle species can have devastating economic impacts on forest and nursery industries. A recent example is Anophophora glabripennis, a species of beetle known in the United States as the ''Asian Longhorrned beetle'', which has damaged many American forests, and is a threat which can unintentionally reach south American countries, including Brazil. This work presents a new method based on X-ray computerized tomography (CT) and image processing for beetle injury detection in forests. Its results show a set of images with correct identification of the location of beetles in living trees as well as damage evaluation with time.

Permutation entropy based real-time chatter detection using audio signal in turning process

Machine tool chatter is an unfavorable phenomenon during metal cutting, which results in heavy vibration of cutting tool. With increase in depth of cut, the cutting regime changes from chatter-free cutting to one with chatter. In this paper, we propose the use of permutation entropy (PE), a conceptually simple and computationally fast measurement to detect the onset of chatter from the time series using sound signal recorded with a unidirectional microphone. PE can efficiently distinguish the regular and complex nature of any signal and extract information about the dynamics of the process by indicating sudden change in its value. Under situations where the data sets are huge and there is no time for preprocessing and fine-tuning, PE can effectively detect dynamical changes of the system. This makes PE an ideal choice for online detection of chatter, which is not possible with other conventional nonlinear methods. In the present study, the variation of PE under two cutting conditions is analyzed. Abrupt variation in the value of PE with increase in depth of cut indicates the onset of chatter vibrations. The results are verified using frequency spectra of the signals and the nonlinear measure, normalized coarse-grained information rate (NCIR).

A New Approach for Real Time Fault Diagnosis in Induction Motors Based on Vibration Measurement

Condition monitoring is used to increase machinery availability and machinery performance, reducing consequential damage, increasing machine life, reducing spare parts inventories, and reducing breakdown maintenance. An efficient real time vibration measurement and analysis instruments is capable of providing warning and predicting faults at early stages. In this paper, a new methodology for the implementation of vibration measurement and analysis instruments in real time based on circuit architecture mapped from a MATLAB/Simulink model is presented. In this study, signal processing applications such as FIR filters and fast Fourier transform are treated as systems, which are implemented in hardware using a system generator toolbox, which translates a Simulink model in a hardware description language - HDL for FPGA implementations.

Automation in fault detection using neural network and model updating

In this article, an implementation of structural health monitoring process automation based on vibration measurements is proposed. The work presents an alternative approach which intent is to exploit the capability of model updating techniques associated to neural networks to be used in a process of automation of fault detection. The updating procedure supplies a reliable model which permits to simulate any damage condition in order to establish direct correlation between faults and deviation in the response of the model. The ability of the neural networks to recognize, at known signature, changes in the actual data of a model in real time are explored to investigate changes of the actual operation conditions of the system. The learning of the network is performed using a compressed spectrum signal created for each specific type of fault. Different fault conditions for a frame structure are evaluated using simulated data as well as measured experimental data.

Fault detection in rotating machines by vibration signal processing techniques

Machines with moving parts give rise to vibrations and consequently noise. The setting up and the status of each machine yield to a peculiar vibration signature. Therefore, a change in the vibration signature, due to a change in the machine state, can be used to detect incipient defects before they become critical. This is the goal of condition monitoring, in which the informations obtained from a machine signature are used in order to detect faults at an early stage. There are a large number of signal processing techniques that can be used in order to extract interesting information from a measured vibration signal. This study seeks to detect rotating machine defects using a range of techniques including synchronous time averaging, Hilbert transform-based demodulation, continuous wavelet transform, Wigner-Ville distribution and spectral correlation density function. The detection and the diagnostic capability of these techniques are discussed and compared on the basis of experimental results concerning gear tooth faults, i.e. fatigue crack at the tooth root and tooth spalls of different sizes, as well as assembly faults in diesel engine. Moreover, the sensitivity to fault severity is assessed by the application of these signal processing techniques to gear tooth faults of different sizes.

Diagnosis and Fault detection in Electrical Machines and Drives based on Advanced Signal Processing Techniques

In the present thesis, a new methodology of diagnosis based on advanced use of time-frequency technique analysis is presented. More precisely, a new fault index that allows tracking individual fault components in a single frequency band is defined. More in detail, a frequency sliding is applied to the signals being analyzed (currents, voltages, vibration signals), so that each single fault frequency component is shifted into a prefixed single frequency band. Then, the discrete Wavelet Transform is applied to the resulting signal to extract the fault signature in the frequency band that has been chosen. Once the state of the machine has been qualitatively diagnosed, a quantitative evaluation of the fault degree is necessary. For this purpose, a fault index based on the energy calculation of approximation and/or detail signals resulting from wavelet decomposition has been introduced to quantify the fault extend. The main advantages of the developed new method over existing Diagnosis techniques are the following: - Capability of monitoring the fault evolution continuously over time under any transient operating condition; - Speed/slip measurement or estimation is not required; - Higher accuracy in filtering frequency components around the fundamental in case of rotor faults; - Reduction in the likelihood of false indications by avoiding confusion with other fault harmonics (the contribution of the most relevant fault frequency components under speed-varying conditions are clamped in a single frequency band); - Low memory requirement due to low sampling frequency; - Reduction in the latency of time processing (no requirement of repeated sampling operation).

Experimental study on bridge damage identification based on wavelet packet energy curvature difference method

Among those damage identification methods, the Wavelet Packet Energy Curvature Difference (WPECD) Method is an effective one. However, most of the existing methods rely on numerical simulation and are unverified via experiment, and very few of them have been applied to practice. In this paper, the validity of WPECD in structural damage identification is verified by a numerical example. A damage simulation experiment is taken on a real replaced girder at the Ziya River New Bridge in Cangzhou. Two damage cases are applied and the acceleration responses at the measuring points are obtained, based on which the damages are identified with the WPECD Method, and the influence of wavelet function and decomposition level is studied. The results show that the WPECD Method can identify structure damage efficiently and can be put into practice.

Damage location in structures by monitoring vibration characteristics

The aim of this work was to investigate the feasibility of detecting and locating damage in large frame structures where visual inspection would be difficult or impossible. This method is based on a vibration technique for non-destructively assessing the integrity of structures by using measurements of changes in the natural frequencies. Such measurements can be made at a single point in the structure. The method requires that initially a comprehensive theoretical vibration analysis of the structure is undertaken and from it predictions are made of changes in dynamic characteristics that will occur if each member of the structure is damaged in turn. The natural frequencies of the undamaged structure are measured, and then routinely remeasured at intervals . If a change in the natural frequencies is detected a statistical method. is used to make the best match between the measured changes in frequency and the family of theoretical predictions. This predicts the most likely damage site. The theoretical analysis was based on the finite element method. Many structures were extensively studied and a computer model was used to simulate the effect of the extent and location of the damage on natural frequencies. Only one such analysis is required for each structure to be investigated. The experimental study was conducted on small structures In the laboratory. Frequency changes were found from inertance measurements on various plane and space frames. The computational requirements of the location analysis are small and a desk-top micro computer was used. Results of this work showed that the method was successful in detecting and locating damage in the test structures.

A behavior based approach to virus detection

Fast spreading unknown viruses have caused major damage on computer systems upon their initial release. Current detection methods have lacked capabilities to detect unknown viruses quickly enough to avoid mass spreading and damage. This dissertation has presented a behavior based approach to detecting known and unknown viruses based on their attempt to replicate. Replication is the qualifying fundamental characteristic of a virus and is consistently present in all viruses making this approach applicable to viruses belonging to many classes and executing under several conditions. A form of replication called self-reference replication, (SR-replication), has been formalized as one main type of replication which specifically replicates by modifying or creating other files on a system to include the virus itself. This replication type was used to detect viruses attempting replication by referencing themselves which is a necessary step to successfully replicate files. The approach does not require a priori knowledge about known viruses. Detection was accomplished at runtime by monitoring currently executing processes attempting to replicate. Two implementation prototypes of the detection approach called SRRAT were created and tested on the Microsoft Windows operating systems focusing on the tracking of user mode Win32 API system calls and Kernel mode system services. The research results showed SR-replication capable of distinguishing between file infecting viruses and benign processes with little or no false positives and false negatives. ^

A Behavior Based Approach to Virus Detection

Fast spreading unknown viruses have caused major damage on computer systems upon their initial release. Current detection methods have lacked capabilities to detect unknown virus quickly enough to avoid mass spreading and damage. This dissertation has presented a behavior based approach to detecting known and unknown viruses based on their attempt to replicate. Replication is the qualifying fundamental characteristic of a virus and is consistently present in all viruses making this approach applicable to viruses belonging to many classes and executing under several conditions. A form of replication called self-reference replication, (SR-replication), has been formalized as one main type of replication which specifically replicates by modifying or creating other files on a system to include the virus itself. This replication type was used to detect viruses attempting replication by referencing themselves which is a necessary step to successfully replicate files. The approach does not require a priori knowledge about known viruses. Detection was accomplished at runtime by monitoring currently executing processes attempting to replicate. Two implementation prototypes of the detection approach called SRRAT were created and tested on the Microsoft Windows operating systems focusing on the tracking of user mode Win32 API system calls and Kernel mode system services. The research results showed SR-replication capable of distinguishing between file infecting viruses and benign processes with little or no false positives and false negatives.