An experimental interface of a microcomputer with a Vega Universal Testing Machine to retrieve data on test specimens

The purpose of this study was to develop an inexpensive interface between the Vega universal testing machine and a Commodore-64 microcomputer. Also, to develop a computer program that will store and retrieve pertinent information about the metallurgical properties of tensile specimens (i.e., modulus of elasticity, Brinell Hardness number, yield point, reduction of area, tempering temperature, etc.). Also, compare the interfacing with the conventional method;It is theorized that by interfacing the Vega universal testing machine with the Commodore-64 microcomputer, the metallurgical data calculated by the computer will be equal to the metallurgical data calculated by the conventional method;Out of eleven hypotheses, there were nine hypotheses with significant difference at the ninety-five percent confidence level. Two hypotheses had no differences. These were the percent elongation and unit deformation. The percent elongation is a multiple of the unit deformation by 100 times;The computer was faster overall than the conventional method. However, the regression predictive equation used in the computer program to calculate the metallurgical data produced on the average higher values than the conventional method. The predictive equation can be adjusted to calculate values that are equivalent to the correct values for any given specimen

On-line health monitoring of passive electronic components using digitally controlled power converter

This thesis presents System Identification based On-Line Health Monitoring to analyse the dynamic behaviour of the Switch-Mode Power Converter (SMPC), detect, and diagnose anomalies in passive electronic components. The anomaly detection in this research is determined by examining the change in passive component values due to degradation. Degradation, which is a long-term process, however, is characterised by inserting different component values in the power converter. The novel health-monitoring capability enables accurate detection of passive electronic components despite component variations and uncertainties and is valid for different topologies of the switch-mode power converter. The need for a novel on-line health-monitoring capability is driven by the need to improve unscheduled in-service, logistics, and engineering costs, including the requirement of Integrated Vehicle Health Management (IVHM) for electronic systems and components. The detection and diagnosis of degradations and failures within power converters is of great importance for aircraft electronic manufacturers, such as Thales, where component failures result in equipment downtime and large maintenance costs. The fact that existing techniques, including built-in-self test, use of dedicated sensors, physics-of-failure, and data-driven based health-monitoring, have yet to deliver extensive application in IVHM, provides the motivation for this research ... [cont.]

Advances in Bioengineering

The technological approach and the high level of innovation make bioengineering extremely dynamic and this forces researchers to continuous updating. It involves the publication of the results of the latest scientific research. This book covers a wide range of aspects and issues related to advances in bioengineering research with a particular focus on innovative technologies and applications. The book consists of 13 scientific contributions divided in four sections: Materials Science; Biosensors. Electronics and Telemetry; Light Therapy; Computing and Analysis Techniques

Impedance Estimation Using Randomized Pulse Width Modulation and Power Converters

With the adoption of technologies such as alternative energy production, DC power grids, and electric vehicles, the use of high power switching converters has seen a dramatic increase. These power converters serve many rolls such as grid-tied inverters in solar farms, high power charging for electric vehicles, motor drives for industrial applications, and DC links in transmission systems. With the increased prevalence of such devices, it is only natural to attempt to optimize their operation. As with any level of converter, it is desirable to have accurate control over the generated voltages and currents. Often, these controllers implement some form of predictive control which requires knowledge of system parameter values to operate properly. Due to several factors, including temperature and component non-linearity, these component values can vary during normal operation. This can lead to degradation of closed loop control and system instabilities. If one is able to measure system parameters while the converter is operating, control parameters can be updated in real time to optimize the system performance. A significant percentage of the size and cost of switching converters are filter elements meant to reduce the amount of noise injected into other attached circuits, or in the case of grid-tied converters, noise injected into the grid. As power levels increase, the size, cost, and power lost in the filter becomes greater. To minimize these negative effects, methods have been developed that reduce harmonic injections, thus allowing for smaller filter elements. One such technique is Randomized Pulse Width Modulation which removes the large harmonic spikes present in standard switching systems, and replaces them with a wide frequency energy spectrum. The objective of this research is to examine the feasibility of online impedance identification by combining and modifying existing technologies. Specifically, Randomized Pulse Width Modulation and Wideband System Identification techniques are used to simultaneously reduce system noise and create an estimation of system filter element impedances. This allows for the reduction of the filter size while simultaneously providing a real-time estimate of the filter impedance with the goal of better feedback control performance

Fault-based Analysis of Industrial Cyber-Physical Systems

The fourth industrial revolution called Industry 4.0 tries to bridge the gap between traditional Electronic Design Automation (EDA) technologies and the necessity of innovating in many indus- trial fields, e.g., automotive, avionic, and manufacturing. This complex digitalization process in- volves every industrial facility and comprises the transformation of methodologies, techniques, and tools to improve the efficiency of every industrial process. The enhancement of functional safety in Industry 4.0 applications needs to exploit the studies related to model-based and data-driven anal- yses of the deployed Industrial Cyber-Physical System (ICPS). Modeling an ICPS is possible at different abstraction levels, relying on the physical details included in the model and necessary to describe specific system behaviors. However, it is extremely complicated because an ICPS is com- posed of heterogeneous components related to different physical domains, e.g., digital, electrical, and mechanical. In addition, it is also necessary to consider not only nominal behaviors but even faulty behaviors to perform more specific analyses, e.g., predictive maintenance of specific assets. Nevertheless, these faulty data are usually not present or not available directly from the industrial machinery. To overcome these limitations, constructing a virtual model of an ICPS extended with different classes of faults enables the characterization of faulty behaviors of the system influenced by different faults. In literature, these topics are addressed with non-uniformly approaches and with the absence of standardized and automatic methodologies for describing and simulating faults in the different domains composing an ICPS. This thesis attempts to overcome these state-of-the-art gaps by proposing novel methodologies, techniques, and tools to: model and simulate analog and multi-domain systems; abstract low-level models to higher-level behavioral models; and monitor industrial systems based on the Industrial Internet of Things (IIOT) paradigm. Specifically, the proposed contributions involve the exten- sion of state-of-the-art fault injection practices to improve the ICPSs safety, the development of frameworks for safety operations automatization, and the definition of a monitoring framework for ICPSs. Overall, fault injection in analog and digital models is the state of the practice to en- sure functional safety, as mentioned in the ISO 26262 standard specific for the automotive field. Starting from state-of-the-art defects defined for analog descriptions, new defects are proposed to enhance the IEEE P2427 draft standard for analog defect modeling and coverage. Moreover, dif- ferent techniques to abstract a transistor-level model to a behavioral model are proposed to speed up the simulation of faulty circuits. Therefore, unlike the electrical domain, there is no extensive use of fault injection techniques in the mechanical one. Thus, extending the fault injection to the mechanical and thermal fields allows for supporting the definition and evaluation of more reliable safety mechanisms. Hence, a taxonomy of mechanical faults is derived from the electrical domain by exploiting the physical analogies. Furthermore, specific tools are built for automatically instru- menting different descriptions with multi-domain faults. The entire work is proposed as a basis for supporting the creation of increasingly resilient and secure ICPS that need to preserve functional safety in any operating context

Variability analysis of engine idle vibration

Vibration in motor vehicles is largely influenced by the engine and thus has become the focus of much automotive testing. Engine idle vibration is focused on since deviations in the vibration signature are prevalent at this operating condition. The objective of this thesis was to derive a best-practice method for the analysis of engine idle vibration. Variability of the engine vibration signatures was calculated through the implementation of multiple analysis techniques. These methods included: angle domain analysis, the fast Fourier transform, the discrete cosine transform, the moving average model, and the auto-regressive moving average model. Also included in the investigation were examinations of data normalization, detrending, and filtration. The results of the analyses were then evaluated with reference to the correlation between similar engines and the identification of outliers. It was found that the fast Fourier transform analysis technique provided the best overall results. The moving average model and the auto-regressive moving average models were also identified as methods that have great potential in vibration analysis but are limited by their computational intensity

A Literature Review on the Application of Acoustic Emission to Machine Condition Monitoring

Acoustic emission (AE) is a common physical phenomenon, in which the strain energy is released in the form of elastic wave when a material is deformed or cracked during the stress process. The condition monitoring based on AE is a relatively new method that aims to use noise/vibration anomalies to detect machine failures. However, some challenges lie ahead of its application. This thesis aims to analyze the literature in the field of AE applications to machine condition monitoring. The principles of AE technology, relevant instruments, machine monitoring and AE signal analysis, and practical examples of AE monitoring applications will be presented. More specifically, challenges, solutions and future direction in solving signal noise and attenuation challenges will be discussed. Through the example of rotating machinery, the characteristics of AE will be explained in detail. This thesis lays the foundation for the actual use of AE to monitor and analyze the state of machinery and provides guideline for future data collection and analysis of AE signals