Search

The failure behavior and the corresponding physical mechanism of the AlGaN/GaN high electron mobility transistors (HEMTs) under transmission line pulse (TLP) stress were investigated in this paper. The result shows that the output and transfer characteristics of the AlGaN/GaN HEMTs after 90 cycles begin to degrade by comparing with the fresh ones under 40 V TLP voltage, and the gate leakage current of the devices slightly increases. When the TLP voltage of 52 V was applied, a catastrophic failure occurs for the AlGaN/GaN HEMTs. Furthermore, the failure of the AlGaN/GaN HEMTs was located, and the micro-morphology of the abnormal spot was observed. The result shows that the gate metal was damaged due to the large TLP stress. The failure mechanism may be mainly attributed to the Joule heat that causes the high lattice temperature of 1160 K as well as the electric field, and it is higher than the melting point of Au (1064 K) in the gate metal (Au/Ti/Mo). The results may be useful in the design and application of electrostatic discharge (ESD) for AlGaN/GaN HEMTs.

The most common pre-breakdown phenomenon in high voltage (HV) equipment is partial discharge (PD). The presence of PD activity is a warning indicator that the insulation deterioration would worsen over time, leading to catastrophic failure if the associated defects are not addressed early. Ultra-high frequency (UHF) has been proposed as a non-conventional method for detecting PD because of its resilience to external noise, high sensitivity, and online measurement potential. The UHF detection approach uses sensors to detect electromagnetic waves emitted by the PD source. The sensor is thus one of the most important parts of UHF PD detection. Microstrip patch antenna is widely used as a UHF sensor due to its merits of simple structure, compact profile, low cost, and ease of fabrication. This project deals with the development of the microstrip antenna for PD detection. To evaluate the antennas' performance, five antennas are built and simulated using Computer Simulation Technology (CST) Microwave Studio software. Return loss, voltage standing wave ratio (VSWR), and surface current are among the simulated results. These five antennas operate in the range of at least 80% of UHF and have the acceptable size that can be installed on the dielectric window or mounted on the flange.

The short circuit performance of silicon carbide MOSFETs has been evaluated in a 270 V system designed to mimic the characteristics of a commercial airliner power system. The devices demonstrated the ability to withstand being short–circuited for a period of 8 µs before suffering a catastrophic failure when that coincided with the turn–off transient for a 9 µs pulse. At the point of turn off the junction temperature in the transistor is calculated to have risen to approximately 950 °C. The withstand period for a transistor held at a case temperature of 150 °C was 7 µs, indicating the failure is of a thermal origin and this reduction needs to be addressed for devices operating in high temperature environments, such as those found in aerospace.

In the construction of machinery and equipment, the way with the widest spread to realization of the parts is the cutting of metals. The cutting process takes place in a MUDPS - O system (tool-machine, machine tool, device, semi manufactured article, edged tool, human factor). The important key success factors for upgrading availability and preventing catastrophic failure in the process industries like construction of machinery and equipment are safety and reliability. One of the most important technique for evaluating the safety and reliability is Failure Mode and Effects Analysis (FMEA) based the Risk Priority Number (RPN) to establish the risk priority order of failure modes. But, traditional FMEA, based RPN, has been subject of severe criticism in the literature because of limitations and significant improvements have been made to overcome the drawbacks of the RPN. This paper aimed to provide the risk analysis through an improved Fuzzy Inference System (FIS) - FMEA model, based on Fuzzy Logic and compare with the traditional FMEA, for MUDPS-O technological system. As shown in the results, the solution proposed here reduce duplication of the RPN numbers and generate a more precise, sensible risk assessment. Based on the results of the applications of this approach, appropriate corrective actions can be taken into account to improve the safety and reliability of process industry, such us the construction of machinery and equipment.

Damage in the structure may lead to significant reduction in local strength. It is of great significance to localize the damage in the early stage for integrity of the structure so as to prevent catastrophic failure. The traditional damage detection techniques is achieved through comparing the current data with the baseline data obtained from the healthy structure. However, the baseline data is not available as the structure is often subjected to operational and environmental variations that may adversely affect the measurement signals. In this paper, model identification method is adopted for crane damage detection. It only requires the data after the structure is damaged. An improved nonlinear model is proposed based on the traditional models. It is denoted as RBF-BL (RBF Network based State Dependent Bilinear) model. Different damage cases are considered in the girder of the crane. The girder is subjected to transient shock excitation. The nonlinear model is constructed based on the vibration response signals and parameter estimation is performed. The anomalous region is initially determined through the model characteristic parameter. Beamforming algorithm is then performed for precise position of the crack. Numerical simulation and experimental validation are implemented to prove the effectiveness of the proposed model for crane damage detection without baseline data.

In this paper, a physics of failure-based prediction method is combined with statistical models to consider the impact of current crowding and current droop effects on the reliability of LED arrays. Electronic-thermal models of LEDs are utilized to obtain the operation conditions under the influences of current crowding and current droop. A Markov chain-based model is used to calculate the probability distribution of each failure mode, including the lumen decay and catastrophic failure. Two types of LEDs were selected for a numerical study. The proposed prediction method provides the realistic reliability prediction results. It is found that the properties of LEDs have a great impact on their hazard rates of LED arrays. The equivalent resistance, third-order non-radiative coefficient, and radiative coefficient of LEDs are critical to the reliability of an LED array.

This paper explores the indirect risks of lightning strikes to electric aircraft. We present background on potential impacts, field testing, strike zones, electrical subsystems, and mitigation techniques involved in the analysis of impact of lightning strikes. An explanation of the theory behind the Transmission Line Matrix method is also presented. A commercial tool, CST Studio Suite, was used to model a lightning strike to an aircraft-like structure. Electric field probes are used to analyze the field changes within and outside the aircraft. Additionally, the surface currents on the aircraft are analyzed. Simulations are developed to explore the impact of skin thickness on the level of penetration of electric field on the aircraft subsystems. The results suggests that design based on appropriate parameters for metal skin aircraft can reduce the likelihood of subsystem interference or catastrophic failure of aircraft electrical systems.

This paper presents the modeling, design, and Multiphysics analysis of a 2000-W, 500000-rpm ultra-high-speed (UHS) machine for a mechanical-based antenna (AMEBA) application. The proposed machine will be utilized as a mechanical transmitter for extremely/very low frequency (0.3-3 kHz) communication, which will immediately enable the bidirectional communication between the earth surface to underground or undersea facilities. The design of a UHS machine for AMEBA application presents several special challenges because it requires a high shaft torque at UHS operation. Also, the UHS machine necessitates a high design-safety-margin to avoid any catastrophic failure at the UHS operation. However, a conventional 3-phase UHS machine cannot meet the torque requirement, thermal limit, structural integrity, and fails to provide enough safety margin at UHS operation. To overcome this limitation, this paper presents the design of a high-power UHS machine, which utilizes a multi- phase winding configuration and special materials to improve the torque density and the design-safety-margin. The machine geometry and design parameters are optimized using a Multiphysics loss minimization approach. The proposed design and its performance are analyzed using extensive finite element analysis (FEA). It is observed that the proposed design meets the electromagnetic, thermal, structural, and Rotordynamic performance with a greater design-safety-margin. Finally, a prototype of the proposed machine is developed and its performances (back-EMF and natural frequencies) are experimentally validated.

As the renewable energy market expands recently, safety of the EES application is becoming a major focus. Smart monitoring systems have allowed mitigating catastrophic failure of EES installations however fuses and contactor remain one of the safest protection solutions once everything else has failed. Today’s EES installers face the challenge of operating voltages of up to 1500 VDC with available fault currents larger than few 100 kA. Toward reaching the level of power/energy per container, paralleling battery strings will bring container fault currents to large levels. While this level of fault currents is not an issue for fuse, nonetheless, the risk is that the other battery will start to feed energy to the arcing inside the fuse and over-stresses it. The safety and growth of EES applications are now started to happen thanks to introduction of fuses specially designed for EES applications along with variety of applications notes on proper fuse selection complimented by test labs simulating actual EES fault currents. and development of new IEC standards for battery usage.

Reliability is an essential scientific and technological domain intrinsically linked with system integration. Nowadays, semiconductor industries are confronted with ever-increasing design complexity, dramatically decreasing design margins, increasing chances for and consequences of failures, shortening of product development and qualification time, and increasing difficulties to meet quality, robustness, and reliability requirements. The scientific successes of many micro/nano-related technology developments cannot lead to business success without innovation and breakthroughs in the way that we address reliability through the whole value chain. The aim of reliability is to predict, optimize and design upfront the reliability of micro/nanoelectronics and systems, an area denoted as ‘Design for Reliability (DfR)’. While virtual schemes based on numerical simulation are widely used for functional design, they lack a systematic approach when used for reliability assessments. Besides this, lifetime predictions are still based on old standards assuming a constant failure rate behavior. In this paper, we will present the reliability and failures found in solid-state lighting systems. It includes both degradation and catastrophic failure modes from observation towards a full description of its mechanism obtained by extensive use of acceleration tests using knowledge-based qualification methods. A use case will be presented in more details.

Data loss in high speed, high importance and critical data carrying networks like Defence Networks due to Link failures may lead to disastrous consequences. Link-flapping, a major contributor to Link failure is a very prominent occurrence in Traditional/Legacy networks which use routing protocols like OSPF and results in loss of data. Link-flapping occurring in critical networks where every bit of data is of extreme value and importance, affects communication reliability and can result in catastrophic failure of services. Even though mechanisms are present for preventing Link-flapping in traditional network devices like routers, still the data loss is prevalent. This paper proposes a method using SDN (Software Defined Networking) capabilities by which the effect of link-flapping occurrence in the network can be nullified while ensuring the uninterrupted flow of traffic and also providing a window for troubleshooting of the affected link simultaneously.

The resiliency of the nation's electric grid is under continuous stress. Impacts of weather, wear and tear, and the age of the grid all contribute to a decline in reliability and an increase in power outages. Aged and deteriorated overhead electrical equipment produce characteristic radio frequency (RF) emissions. Fixed sensors to monitor the condition of all grid equipment would be expensive and create an enormous data-handling problem. Mobile Edge Computing sensor (MECS) technology that can discriminate and locate the equipment that produce pre-failure RF emission failure signatures would enable equipment replacement before catastrophic failure causes an outage. Rebuilding or replacing the 5 million miles of the U.S. transmission and distribution grid is not an economical alternative. Maintaining the grid through a strategy of predictive, conditions-based maintenance results in improved grid performance, fewer outages, and addresses a worldwide problem. This paper and presentation detail a pilot project where MECS are installed on sanitation trucks. The vehicle mounted MECS evaluate changes in the state of electrical equipment on an entire city grid each week. Machine learning algorithms correlate a variety of grid impacts such as weather, load, season, and protective equipment operations to develop grid resilience forecast models. The forecasts allow financial impact evaluation of grid rebuilds, undergrounding programs, and maintenance strategies. The technology, data examples, and the results of the pilot project in a 65 square-mile Midwest city will be presented.

Homemade equipment - to save money Non-performing craft - because management doesn't want to have the hard discussion Unqualified personnel - because we need the bodies on the job A lack of formal procedures - because they take a lot of time and effort to write A deviation from lockout/tagout protocol - because it was quicker Any one of these can be the prelude to a serious electrical incident. Mix them all together and you have a recipe for a catastrophic failure which resulted in a near fatality and a cost to the company of millions of dollars.

Cryogenic operation of power electronic converters promises higher density, better efficiency, lower thermal management requirement and higher reliability. However, the wide variation in power semiconductor device performance at deep cryogenic temperatures might cause catastrophic failure if not considered accordingly during the design phase. The work presented in this paper explores a simple cryogenic characterization method for insulated-gate bipolar transistors (IGBT). In addition, an open access physics-based IGBT compact model has been modified with cryo-compatible temperature scaling equations in order to investigate the merit of the chosen device in a power electronic system. A commercial field stop trench IGBT (50A/650V) has been used for the electrical characterization. Furthermore, additional complicated converter circuits (e.g. a buck converter and a 5-level modular multilevel inverter) has been simulated, and loss calculation has been conducted to provide a means for designers to select best devices for appropriate low temperature applications.

Bearings are one of the most crucial components of rotating machinery. The condition of bearing contributes to overall machine performance. It is thus important to analyze the faults in the bearing in earlier stage in order avoid catastrophic failure. The condition monitoring based on vibration measurement can be used to identify defects in bearing. The present study is carried out to identify the effect on vibration spectrum of a ball bearing having defect at inner race using FFT analyzer. An experimental setup is developed. The fault is created at the inner race of the bearing by using electrical discharge machining (EDM). The frequency spectrum is acquired for faulty as well as healthy bearing using FFT analyzer. It can be concluded that high Peak in amplitude of vibration observed at BPFI in frequency spectrum indicates that fault is present at inner race of ball bearing. Only experimental observation methods depend on human knowledge and experience, which shows error. Therefore, it is necessary to detect fault without human intervention automatically using a computer. This study gives overall review for fault diagnosis using Artificial Intelligence. The experimental data is used for training and testing the Artificial Neural Network (ANN) to detect the fault automatically in MATLAB environment. The accuracy of ANN is found to be 94.27%. The results are in close agreement for the similar condition available in literature.

Failure of mechanical bearings is extremely problematic with respect to the reliability of electric and rotating machines for various applications and technologies, in transport, energy systems and advanced robotic systems (industry 4.0). The occurrence of catastrophic failure modes gives rise to abrupt unscheduled down time and maintenance, machine overheating (burn-out), as well as significant reliability and health and safety considerations for various mission critical end-applications. This paper proposes a robust method of remaining useful life (RUL) estimation using Support Vector Machines (SVMs) to classify the wear states of the rolling element bearings. Different types of feature sets are derived from the Short-time Fourier Transform (STFT) as well as Envelope Analysis; which are compared for both linear and non-linear wear state models. The methods were optimised for different types of SVM kernelling functions and achieved classification accuracy of up to 67.6%. Medium and coarse Gaussian kernelling functions achieved the highest level of accuracy. This proposed method proves to be a valuable non-invasive predictive CbM approach for electric and rotating machines, using accelerometer based vibration signals mounted on the external races of rolling element bearings under test.

In recent years, large-scale blackouts have occurred in several countries due to the lack of power system analysis and catastrophic failure defensive, which caused a worldwide concern. In comparison, Chinese power grid has a richer set of predicted faults and a more complete fault defense strategy. This article focuses on the Brazilian power grid. Firstly, the similarities and differences between the Brazilian power grid and the Chinese power grid in terms of grid structure, power supply composition, stability characteristics, and control strategy are studied. Then, combined with the requirement of the Brazilian power grid, some excellent experiences of the Chinese power grid are summarized. Point out the weakness part in Brazilian power system security and stability guidelines, which will give reference to Brazilian power grid. At last, some preliminary suggestions on the safety and stability analysis and control of the Brazilian power grid are put forward in the paper.

Electrochemical migration has attracted more attention from researchers due to its high risk of electronics reliability. It is a multistage process that may result in catastrophic failure of electronics. The growing interest of this phenomenon requires precise modeling to predict the time-to-failure, which consists of two main stages, incubation and growth of dendrites. This paper presents a short overview regarding the ECM process, main factors affecting the process., the main historical numerical models for electrochemical deposition in electrochemistry., electrochemical migration on electronics., and the importance of machine learning in building up more precise lifetime models compared to physics of failure models.

The performance of a field-enhanced emitter (FEE) array in an enclosed and ex-situ operated device has long been known to deviate from the ideal characteristics observed in laboratory settings. Here we report on the performance and failure mechanisms measured in FEE arrays operating in high-voltage sealed x-ray generators. The emitters are fabricated from etched silicon forming pyramids which are coated with metal. Unlike many previously reported arrays, here the pitch is large (1cm) and affords easy measurement and diagnosis of individual emitters. We report on emitter performance and likely causes of deviations from ideal, including the impact of anode treatment, emitter geometry and catastrophic failure mechanisms.

The 650 V and 1200 V SiC MOSFETs have gained significant market acceptance in the recent years, mainly, due to the promise of higher power conversion efficiency and power density. However, the lack of field-proven long-term reliability data of SiC MOSFETs is still a major concern, particularly in applications where the devices are exposed to harsh thermal and electrical cycles. This paper discusses a methodology to monitor health status of SiC MOSFETs to enable aging prognostics and remaining useful life estimation. The methodology is based on measurements of ohmic resistance between source and drain terminals. In order to desensitize the method from the well-known temperature dependency of the R dson ; it is proposed to measure the resistance of the MOSFET’s body-diode. It will be shown that body-diode presents a much lower dependency to changes in temperature. The measurements can be obtained in real-time, without interrupting the converter normal operation, a highly appreciated feature in next generation of power electronics converters. By proper management of failure precursor data, it will be possible to forecast converter failures so that necessary actions can be taken before a catastrophic failure occurs. The methodology is applicable for SiC MOSFETs of any voltage class from 650 V to 15 kV.

Ensuring the reliability and safety of power converters based on wide band gap (WBG) semiconductors is a critical issue. The former exhibit high critical fields, consequently, and unlike silicon devices, when the devices are stressed a catastrophic failure may erupt before avalanche takes place. For this reasons, short current protection circuit must exhibit nanosecond level detection time in order not to exceed the safe switching operation area of the device and has to ensure smooth turn-off. In this paper, an original derivative pulsed-current sensor in conjunction with the fast response 2-stage shutdown circuit is evaluated for various short-circuits. The protection circuit is developed and tested with the half-bridge converter evaluation board for 650 V / 80A GaN devices in various short circuit conditions.

Rotor blade damage and/or failure is a common occurrence in wind turbines that is very costly and can lead to substantial downtime. If a remote monitoring technology is developed, it can give an early warning about blade anomalies before catastrophic failures occur, maintenance process can be largely improved, and downtime and losses can be minimized. In this paper, an condition-based blade health remote monitoring and diagnostics (RM&D) solution using Electrical Signature Analysis (ESA) is proposed, where an electrical signature, pitch motor current obtained from existing pitch control platform, is used, and a natural-frequency-based blade health index is introduced. This solution can be directly applied to both existing and new wind turbine fleets and give wind farm operators an early warning before catastrophic failure occurs. It is hardware-free, zero added-cost, and has minimum impacts on turbine normal operation, and has been demonstrated by both simulation and field data from North America MW-scale wind farms. This approach turns out to be the first ESA-based effort to remotely monitor and diagnose blade defects for commercial MW-scale wind turbines.

This paper presents a theoretical study useful for the estimate of ride through capability of phase control thyristors submitted to high single- and multi-cycle AC current surges. The development of a calculation technique is presented, which is based exclusively on data sheet parameters. It allows for the previous determination of the usefulness of a given phase control thyristor to any application in which a high amplitude, short duration current waveform is to be handled. Furthermore, an alternative, simple method for the calculation of instantaneous virtual junction temperature of the device is presented. It is compared to the traditional equivalent power pulse technique. The results are experimentally verified by the direct application of pre-stablished current waveforms to a commercially available model of thyristor.

With the rapid increase in the connection of distributed generation and anticipated new load (electric vehicles particularly), a well-designed power system topology is critical to maintain the system's resilience against catastrophic failure. In order to understand the correlation of network topology and power system resilience, the author uses power tracing to investigate the contributions of the generations to the loads with the analysis of the network topologies. A new approach (neural networks) is then used to learn the inter-related indices between the generations and the loads under the network resilient scenarios. The results will be useful to determine the network's topology and improve power system recovery under emergencies.

The requirement to use low carbon-emitting power plants promotes increased utilization of renewable energy source. Photovoltaic (PV) as a modular generation is relatively easy to implement compared to the other renewable energy plants. In 2017, PV usage hits 100GW, globally. The safety of photovoltaic modules appears to be an issue with the growing usage of photovoltaic, as PV modules will face various modes of faults during the operation. Nearly 50 % of the total fault is the hot spot that is very difficult to locate in a largescale PV field. The latest method requires up to 105 days to detect hotspot in a 15 MW PV generation with an area of 30 hectares and made of 63000 modules (contains millions of cells). Those methods which cannot quickly and continuously detect the fault can degrade and burn the module. Therefore, a fast detection method is needed to prevent the catastrophic failure of PV modules. Thermal imaging using fish eye lens is promised to face this problem. It has wide field of view so that the wide area PV farm could be monitored simultaneously. However, fish eye lens has non linear projections which affect the image shape. Therefore, in this paper, the simulation to identified PV image characteristic that created by fish eye lens has been performed. The results show that there are some parameter combinations which can create a clear image without any overlapping. Also, the result show the length characteristic of PV image which can be used to defined the requirement of thermal sensor sensitivity.

A typical approach to minimizing the early failures (“infant mortality”) of the modern metallized film capacitors (MFCs) is to conduct a burn-in test at applied voltage slightly more (at 30 – 50%) than nominal value. During the test, an external capacitor bank is used as a capacitive storage device with an energy significantly exceeding the energy level of the capacitor under the test. Thus, the amount of energy in the self-healing (SH) process is practically unlimited, which may lead to catastrophic failure of the capacitor. Recently, the soft training test for application of such capacitors in overload modes has been proposed. This can significantly improve the MFCs technical characteristics. We present the results of experimental and theoretical study on SH processes of MFCs in soft training test, allowing the capacitors to operate at voltages 3 – 5 times higher than nominal value. The MFC’s degradation is associated with the equivalent parallel resistance (EPR) decreasing due to numerous SH events. On the base of the developed physical model, the relationship between critical value of testing voltage and EPR as a diagnostic parameter is offered.

Gear wear is inevitable during the service life of gearboxes and may lead to catastrophic failure. As an important micro-level wear feature, tooth surface roughness directly affects the gear wear progression (lubrication regimes, wear mechanisms and wear rates) and lifespan of a gearbox. Therefore, it is important to monitor surface roughness changes. The tooth surface roughness induces random vibration signals with cyclic amplitude modulation. Reported works used an indicator of second-order cyclostationarity (ICS2) to assess such signals. However, the ICS2 gives a poor correlation with surface roughness. This paper presents the use of an Autoregressive Conditional Heteroskedasticity (ARCH) model to represent the random vibration signals with cyclic amplitude modulation. ARCH model parameter serves as an indicator to assess the changes in gear tooth surface roughness. A laboratory dataset was used to validate the effectiveness of the ARCH model in assessing surface roughness level. Results have shown that using the ARCH model returns a more accurate assessment result than the ICS2.

The resiliency of the nation’s electric grid is under continuous stress. Impacts of weather, wear and tear, and the age of the grid all contribute to a decline in reliability and an increase in power outages. Power outages result in annual economic impact to energy consumers and present a security impact to critical infrastructure and the general public. Aged and deteriorated overhead electrical equipment produce characteristic radio frequency (RF) emissions. Fixed sensors to monitor the condition of all grid equipment would be expensive and create an enormous data-handling problem. Mobile Edge Computing Sensors (MECS) that can discriminate and locate the equipment that produce pre-failure RF emission failure signatures enable equipment replacement before catastrophic failure causes an outage. Rebuilding or replacing the 5 million miles of the U.S. transmission and distribution grid to reduce power outages is not an economical alternative. Maintaining the grid through a strategy of predictive, conditions-based maintenance results in improved grid performance, fewer outages, and addresses a worldwide problem. This paper discusses the technical challenges to design autonomous, low-power MECS for fleet deployment. The technology, and the results of a pilot project in a 65 square-mile Midwest city are presented.

A Type-C power delivery (PD) chip using a Type-C connector with Integrated V CONN power switch suffered catastrophic failure during application board power up and/or Type-C cable plug in. Failed devices showed signs of minor to severe damage on integrated V CONN power switch, ESD diodes, ESD rails of CC 1 and CC 2 and V CONN connector facing pins and bond wires of the V CONN pin.Accurate study, simulations and analysis of the V CONN switch, its layout, related parasitic BJT and potential SCR showed robustness of the design, layout and implementation. Also, different experiments with multiple failure analysis including emission and de-processing on multiple damaged parts provided a direction to understand the root cause. More investigation showed the off-chip boost converter on the application board was unstable and caused the problem. It over stressed the V CONN pin with a voltage much higher than its absolute maximum tolerance level which either killed the die immediately or wounded ESD structure and made it vulnerable against cable discharges.

Compared with steam/hydro/gas turbines used in traditional power plants, wind turbines (WTs) are usually operated in harsher environments and, therefore, have relatively higher failure rates. DFIGs are commonly used in WT systems and are prone to electromechanical incipient faults, which gradually degrades their operating characteristics and causes major damage in the system. The most common faults in the DFIGs reported in the literature are the inter-turn winding faults in the rotor and stator windings of the DFIG. If these faults are not detected and rectified at early stages, they tend to be catastrophic leading to unexpected outages which are associated with high operating and maintenance costs and significant loss of revenue along with interruption to the business relying mainly on wind energy. Given the projected growth of WTs in the energy market worldwide, further effort needs to be made towards implementing new or improving existing condition monitoring and fault detection strategies to allow the fault diagnosis process to be performed to detect, locate and mitigate the incipient faults before a catastrophic failure occurs. This paper focuses on the review of the techniques used in condition monitoring and early fault diagnosis of the interturn winding faults in the rotor and stator of the DFIGs while in operation. The review of the literature made it evident that the diagnosis of stator interturn winding faults in DFIGs has been researched extensively, although not under transient conditions. Whereas the diagnosis of rotor interturn winding faults of the same machines has remained largely unexplored.

Single-Event Burnout (SEB) is a catastrophic failure in the high voltage devices that is initiated by the passage of particles during turn-off state. Previous papers reported that SEB failure rate increases sharply when applied voltage exceeds a certain threshold voltage. On the other hand, the high voltage devices for the artificial satellite have been increasing. In space, due to increase flux of particle, it is predicted that SEB failure rate will be higher. In this paper, we proposed the failure rate calculation method for high voltage devices based on SEB cross section and flux of particles. This formula can calculate the failure rate at space level and terrestrial level depending on the applied voltage of the high voltage devices., 2017 29th International Symposium on Power Semiconductor Devices and IC's (ISPSD), May 28 2017-June 1 2017, Sapporo, Japan

After studying almost 20 years in past operational data, it was found that partial discharge leading to insulation failure in resin epoxy cast bushings is very frequent. The main cause for these failures is bad design which lead to high electrical stress causing insulation breakdown. Flaws in the manufacturing process under the influence of continuous operation cause insulation failure. Severe operating environment in the field lead to catastrophic failure. In this paper, to understand the root cause of above problems special techniques are discussed including simulation using 3D Electric Field Modelling by Finite Element Method. A case study is showcased where dissection analysis on bushings was implemented on the bushings having high PD values. Finally, partial discharge test on full scale prototypes are validated with 3D field computation results.

When a system involves storage, control, usage and transformation of large amounts of energy, failures can have catastrophic consequences. However, ultimate failure is almost always preceded by a chain of events involving multiple factors, including system configuration, coupling behavior, interactions, unexpected feedback loops, weak links, operator errors, random events, common mode failures, and the like. In this paper, the position is taken that no matter how well a system has been designed, failure is inevitable. By thinking in terms of energy and process physics, a new tool to assist in this analysis, the Energy Expansion Tree, is employed to look for opportunities to short circuit the chain of events in such a way as to achieve relatively fail-safe behavior.

In this paper we present a thermal conductivity gas sensor with improved sensitivity by adding holes in the thin-film membrane. A numerical model is created and validated against the reference CMOS MEMS thermal conductivity sensor. The numerical model is used to investigate the advantages of having isolating holes through the membrane, located on either side of the heating resistor. These holes increase robustness and minimise catastrophic failure caused by pressure difference whilst simultaneously increasing sensitivity by enhancing convective interaction. The electro-thermal efficiency is shown to increase by 16% whilst the sensitivity to measuring percentage CO2 increases by 39.2%. It is also shown that increasing the width of the holes does not have significant effect on these sensitivities; thus, small holes can be incorporated, leaving room for multiple resistors across the membrane for different measuring techniques. This paper serves as proof that membrane holes can be used to optimise thermal conductivity sensors and will serve as a reference when these designs are fabricated and tested, leading to a new low-power, high-sensitivity gas sensor.

Diagnosis of gearbox is crucial to prevent catastrophic failure and reduce financial losses. In this study, we introduce a novel fault diagnosis technique using the infrared thermography (IRT). The IRT-based techniques have merits of non-contact measurement and high-scalability. Since the convolutional neural network (CNN) is proven to be powerful in image processing, a fault diagnosis strategy is designed by combining the IRT and CNN. Then, the pattern identification is achieved by using softmax regression (SR) classifier. One experimental data is used to validate the effectiveness of the proposed method. Results demonstrate that this diagnosis strategy can recognize gearbox with various oil-level faults. Furthermore, some important distinguishable areas of IRT images are marked for further focused research field.

An apropos supply of grid resiliency materials has been found to be scarce within certain regions located within member countries of the Association of Southeast Asian Nations (ASEAN). This has led to numerous ad hoc paradigms. As an exemplar, the non-ideal positioning of a normally grounded, isolated indoor climate-controlled paradigm of key distribution feeders to temporary outdoor yard positions constitutes a potentially hazardous situation. In fact, there is a distinct possibility that the excessive ambient temperature conditions (i.e. non-climate-controlled outdoor environment) of the distribution substation’s yard, where the temporary outdoor feeder now temporarily resides, can cause the temperature within a recloser control cabinet to dramatically increase; the ensuing thermal stress can affect the insulation of both the enclosed equipment and the involved cables, thereby potentially leading to a possible explosion of the involved recloser control cabinet. Apropos telemetry sensors and the instantiation of grid resiliency reserve depots (to supply the necessary wire/control cables) can assist in mitigating against these situations, which can lead to catastrophic failure.

Power electronics devices are widely used especially in critical applications. Their failure affects the reliability and the functionality of the whole system. Understanding their failure mechanisms and degradation for prognostics and health management is becoming more and more interesting in the last few years. Due to environmental operation conditions, these devices are subject to degradation process that leads to complete failure of the system. However, before a catastrophic failure occurs some early signs can be observed to indicate the level of degradation known as fault precursors. For the Metal Oxide Semiconductor Field Effect Transistor (MOSFET), the ON-resistance $(\mathbf{R}_{\text{ds},(\text{ON})})$ is considered as the main precursor parameter for die-attached failure. Hence, the latter occurs when the MOSFET faces thermal over-stress which can be simulated by accelerated aging experiments. In this article, the degradation models of the MOSFET component of a DC-DC converter are presented based on experimental data, and the deterioration is estimated by the state-parameter joint estimation using Extended Kalman Filter (EKF).

ABSL builds batteries using commercial off the shelf (COTS) lithium-ion cells of the 18650 form factor. The commercial applications of 18650 lithium-ion cells have grown tremendously over the last 20 years, and cell manufacturers continue to improve the state-of-the-art by increasing energy density (Wh/L) and specific energy (Wh/kg) of their products. The increasing mass efficiency of these cells has facilitated more widespread and cost-effective adoption of lithium-ion as a power source in portable applications, which in turn has inspired investment in the development of new cells with ever increasing energy density. For space applications, the benefits of using cells with the highest possible mass efficiency are well established. But for a variety of reasons - the most significant being the increased amount of stored electrochemical potential energy - high energy density cells pose additional challenges to safely integrate into a battery solution. This is particularly true for the risk of catastrophic failure related to a thermal runaway event, and specific requirements are typically levied against battery designs in human spaceflight applications through JSC 20793, “Crewed Space Vehicle Battery Safety Requirements”. In order to characterize the thermal runaway behavior of a high energy density cell selected for a human spaceflight program, ABSL carried out a progressive development testing campaign. Starting from loose single cells, the test campaign incrementally included flight-like design features intended to mitigate the observed thermal runaway behavior. The results of the testing were subsequently incorporated into the flight design and the next level of development testing. The test campaign culminated in summer 2019 with successful verification of the JSC 20793 requirements on the CDR battery design.

Induction motors are widely used in transportation, mining, petrochemical, manufacturing and in almost every other field dealing with electrical power. These motors are simple, efficient, highly robust and rugged thus offering a very high degree of reliability. But like any other machine, they are vulnerable to faults, which if left unmonitored and detected, might lead to catastrophic failure of the machine in the long run. Early detection and diagnosis of faults that may occur are desirable to ensure that, operational effectiveness of an induction motor is improved. In this work, faults detection and classification using fuzzy logic controller is presented. A series of simulations using the models of 3-phase squirrel cage induction motor was performed using MATLAB/Simulink under different faults conditions, such as, break in stator windings, turn-to-turn short-circuit, 3-phase to ground and unbalance in input voltage. Models were designed on the basis of characteristics and parameters of a real motor. The results that were obtained showed that the fuzzy logic controller was able to detect the various faults of the 3-phase induction motor with high accuracy.