123 research outputs found
Simulation of Impedance Discontinuities Resulting from Degradation of Interconnections on Printed Circuit Boards
No full textNumerical simulation of impedance discontinuities resulting from degradation of interconnections on printed circuit board
Ieee Access Special Section Editorial:Complex System Health Management Based on Condition Monitoring and Test Data
No full textHealth monitoring of solenoid valve electromagnetic coil insulation under thermal deterioration
No full textSolenoid-operated valves (SOVs) are widely used components in a variety of industries and systems. Though they are small components, their failure can lead to catastrophic failure of the system in which they are placed. Past studies have revealed the electromagnetic coil insulation to be a weakness, and prior work in the AC motor community on twisted pairs has shown that insulation capacitance measurements can reveal useful diagnostic information. Yet, no studies have tracked the impedance spectrum over an aging/degradation period, no methods are available to identify frequencies in the impedance spectrum to be used, there discussion about how to employ coil impedance to determine a degradation mechanism, nor has there been any quantifications of the mechanical properties due to degradation. This paper develops a method of detection of the aging of insulation in low-voltage electromagnetic coils when subjected to elevated temperature environmental conditions by assessing changes in the impedance spectrum. In this experiment, a solenoid valve was powered and subjected to an operating environment equal to its maximum operating temperature. The valve was periodically removed from the temperature chamber and the impedance spectrum measured. The complex impedance was split into its real and imaginary parts and the Spearman correlation coefficient was used to find regions of interest within the impedance spectrum. The results indicate that resistance and reactance provide information that can assist in condition-based maintenance procedures for electromagnetic coils. Furthermore, mechanical properties of the insulation are investigated and reveal differences in degradation due to environmental conditions.<br type="_moz" /
Health monitoring of solenoid valve electromagnetic coil insulation under thermal deterioration
No full textSolenoid-operated valves (SOVs) are widely used components in a variety of industries and systems. Though they are small components, their failure can lead to catastrophic failure of the system in which they are placed. Past studies have revealed the electromagnetic coil insulation to be a weakness, and prior work in the AC motor community on twisted pairs has shown that insulation capacitance measurements can reveal useful diagnostic information. Yet, no studies have tracked the impedance spectrum over an aging/degradation period, no methods are available to identify frequencies in the impedance spectrum to be used, there discussion about how to employ coil impedance to determine a degradation mechanism, nor has there been any quantifications of the mechanical properties due to degradation. This paper develops a method of detection of the aging of insulation in low-voltage electromagnetic coils when subjected to elevated temperature environmental conditions by assessing changes in the impedance spectrum. In this experiment, a solenoid valve was powered and subjected to an operating environment equal to its maximum operating temperature. The valve was periodically removed from the temperature chamber and the impedance spectrum measured. The complex impedance was split into its real and imaginary parts and the Spearman correlation coefficient was used to find regions of interest within the impedance spectrum. The results indicate that resistance and reactance provide information that can assist in condition-based maintenance procedures for electromagnetic coils. Furthermore, mechanical properties of the insulation are investigated and reveal differences in degradation due to environmental conditions.<br type="_moz" /
Reliability of Copper-Filled Stacked Microvias in High Density Interconnect Circuit Boards
No full textThe electronics industry strives to produce affordable, lightweight, and reliable products with higher performance. At the electronic component level, this translates to components with increased I/Os and reduced footprints, and on the package substrate and printed circuit board (PCB) level, to the use of high density interconnects (HDIs). HDI technology makes use of microvias as interconnects between different conductor layers. According to IPC standards, microvias are blind or buried vias that are equal to or less than 150 μm in diameter. Advances in miniaturized electronic devices have led to the evolution of microvias from single-level to stacked structures that intersect multiple HDI layers. A stacked microvia is usually filled with electroplated copper to make electrical interconnections and provide structural support.
A challenge for HDI circuit board processing is to fabricate microvias without generating defects in the deposited copper structures. Firstly, the copper plating process can easily generate voids in microvias. When voids are present, localized stress concentrations within the electrodeposited copper structure can degrade the reliability of microvias. Secondly, poor quality of electroless copper (a process step following microvia hole drilling and prior to electrolytic copper plating, that makes the microvia hole conductive) results in inferior bonding between the base of the microvia and the target pad underneath the microvia. Microvia base and target pad interface separation is a common failure observed in HDI circuit boards.
The objectives of this dissertation are to determine the effects of voids on the lifetime of copper-filled stacked microvias, and to develop an analytical model that the electronics industry can use to predict microvia fatigue life and assess risks associated with production and use of the latest generation of HDI circuit boards. The dissertation also aims to quantitatively address the factors that affect microvia interface separation.
A parametric study was conducted to investigate the effects of voids on the thermo-mechanical reliability of copper-filled stacked microvias using 3-D finite element analysis and strain-based fatigue life estimation. It was found that microvia fatigue life is affected by geometrical void characteristics, such as shape, size, and location; microvia aspect ratio; and material properties of dielectric layers. Large voids decrease the lifetime of microvias—for example, a 16% conical void results in a microvia fatigue life that is only 1.4% of that of a non-voided microvia. Moreover, microvia aspect ratio and z-axis coefficient of thermal expansion (CTE) of the HDI dielectric material are critical parameters for the lifetime. The fatigue life of a voided microvia of 0.25 aspect ratio is more than two orders of magnitude longer than the fatigue life of a voided microvia of 0.75 aspect ratio with the same void size. An increase of the z-axis CTE by 40% (from 50 ppm/°C to 70 ppm/°C) decreases the microvia fatigue life by 95%. As an outgrowth of this study, a microvia virtual qualification method was proposed. Using the combination of finite element analysis and fatigue life estimation, the required amount of HDI board reliability testing will be reduced, cutting overall development time and cost.
The factors that affect microvia fatigue life were examined, and a design of experiment (finite element simulation) was performed to quantify the effects of those factors on microvia lifetime in terms of cycles to failure. A second-order regression life prediction model was developed using response surface mothed (RSM) to predict cycles to failure of copper-filled stacked microvias under thermal loading. The life prediction model accounts for not only the microvia design parameters and material properties, but also voiding defects introduced during the manufacturing process. The model can predict cycles-to-failure of microvias without voids and with voids of different sizes. The electronics industry can use this model as a convenient and inexpensive tool for HDI design and process validation. This is the first known regression model for copper-filled stacked microvia life prediction.
Finally, the factors that affect microvia interface separation were quantitatively addressed. Finite element modeling was used to simulate microvias with imperfect electroless copper layers. This study revealed how thermal loadings and structure flaws (in terms of initial crack length) affect the chance of microvia interface separation
ANALYSIS AND IMPEDANCE-BASED DETECTION OF ELECTROMAGNETIC COIL INSULATION DEGRADATION
No full textElectromagnetic induction coils are widely used in a variety of applications, such as motors, solenoid valves, and relays. Many of these applications are safety-critical. Failure of the insulation that protects the windings in electromagnetic coils is a significant cause of coil failure and can have severe implications for system reliability. An effective insulation health monitoring program can reduce maintenance and replacement costs, predict the useful lifetime of the coil, and improve the operational availability of the system in which the coil is used. Impedance monitoring of coils has emerged as a promising approach for non-invasive, in-situ insulation health assessments of electromagnetic coils. Yet, little was understood about the relationship between coil impedance and traditional insulation health metrics, such as insulation capacitance and insulation resistance. Furthermore, relating the impedance measurements to chemical and mechanical characteristics of the insulation material is important to understanding the relationship between impedance measurements and the state of the insulation at failure. This study describes the development an improved method of electromagnetic coil insulation health monitoring and shows the uncovered relationships between coil impedance and the insulation electrical, chemical, and mechanical properties
INTERPRETABLE AND SPEED ADAPTIVE CONVOLUTIONAL NEURAL NETWORK FOR PROGNOSTICS AND HEALTH MANAGEMENT OF ROTATING MACHINERY
No full textFaulty rotating machines exhibit vibrational characteristics that can be distinguished from healthy machines using prognostics and health management methods. These characteristics can be extracted using signal processing techniques. However, these techniques require certain inputs, or parameters, before the desired characteristics can be extracted. Setting the parameters requires skill and knowledge, as they should reflect the component geometries and the operational conditions. Using convolutional neural networks for diagnosing faults on a rotating machine eliminates the need for parameter setting by replacing signal processing with mathematical operations in the networks. The parameters that affect the outcomes of the operations are learned from data during the training of the neural networks. The networks can capture characteristics that are related to the health state of a machine, but their operations are not interpretable. Unlike signal processing, the internal operations of the networks have no constraints that guide the networks to transform vibrations into certain information, that is, vibrational characteristics. Without the constraints, there is no basis for understanding the characteristics in terms that can be associated with the physics of failure. The lack of interpretability impedes the physical validation of vibrational characteristics captured by the networks.This dissertation presents a method for changing the internal operations of a convolutional neural network to emulate a specific type of signal processing known as envelope analysis. Envelope analysis demodulates vibrations to extract vibrational signatures associated with mechanical impact on a defective rolling component. An understanding of envelope analysis, along with knowledge of the geometries of machine components and operational speeds, allows for a physical interpretation of the signatures. The dissertation develops speed adaptive convolutional layers and a rotational speed estimation algorithm to identify defect signatures whose frequency components change as the speed changes. The characteristics that are captured by the developed convolutional neural network are verified through a feature selection process that is designed to filter out physically implausible features. Case studies on three different systems demonstrate the feasibility of using the developed convolutional neural network for the diagnosis
Nondestructive Sensing of Interconnect Failure Mechanisms Using Time-Domain Reflectometry
No full textThis paper presents time-domain reflectometry (TDR) as a nondestructive sensing method for interconnect failure mechanisms. Two competing interconnect failure mechanisms of electronics were considered: solder joint cracking and solder pad cratering. A simple theoretical analysis is presented to explain the effect of each failure mechanism on the TDR reflection coefficient. Mechanical fatigue tests have been conducted to confirm the theoretical analysis. The test results consistently demonstrated that the TDR reflection coefficient gradually decreased as the solder pad separated from the circuit board, whereas it increased during solder joint cracking. Traditional test methods based on electrical resistance monitoring cannot distinguish between failure mechanisms and do not detect degradation until an open circuit has been created. In contrast, the TDR reflection coefficient can be used as a sensing method for the determination of interconnect failure mechanisms as well as for early detection of the degradation associated with those mechanisms.close
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