Assessment Methodology of Junction Temperature of Light-Emitting Diodes (LEDs)

Chang, Moon-Hwan,Pecht, Michael The Korean Microelectronics and Packaging Society 2016 마이크로전자 및 패키징학회지 Vol.23 No.3

High junction temperature directly or indirectly affects the optical performance and reliability of high power LEDs in many ways. This paper is focused on junction temperature characterization of LEDs. High power LEDs (3W) were tested in temperature steps to reach a thermal equilibrium condition between the chamber and the LEDs. The LEDs were generated by pulsed currents with duty ratios (0.091% and 0.061%) in multiple steps from 0mA and 700mA. The diode forward voltages corresponding to the short pulsed currents were monitored to correlate junction temperatures with the forward voltage responses for calibration measurement. In junction temperature measurement, forward voltage responses at different current levels were used to estimate junction temperatures. Finally junction temperatures in multiple steps of currents were estimated in effectively controlled conditions for designing the reliability of LEDs.

Autonomous health management for PMSM rail vehicles through demagnetization monitoring and prognosis control

Niu, Gang,Jiang, Junjie,Youn, Byeng D.,Pecht, Michael Elsevier 2018 ISA transactions Vol.72 No.-

<P><B>Abstract</B></P> <P>Autonomous vehicles are playing an increasingly importance in support of a wide variety of critical events. This paper presents a novel autonomous health management scheme on rail vehicles driven by permanent magnet synchronous motors (PMSMs). Firstly, the PMSMs are modeled based on first principle to deduce the initial profile of pneumatic braking (p-braking) force, then which is utilized for real-time demagnetization monitoring and degradation prognosis through similarity-based theory and generate prognosis-enhanced p-braking force strategy for final optimal control. A case study is conducted to demonstrate the feasibility and benefit of using the real-time prognostics and health management (PHM) information in vehicle ‘drive-brake’ control automatically. The results show that accurate demagnetization monitoring, degradation prognosis, and real-time capability for control optimization can be obtained, which can effectively relieve brake shoe wear.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A novel autonomous health management scheme for PMSM Rail Vehicles. </LI> <LI> Accurate demagnetization monitoring and degradation prognosis. </LI> <LI> Real-time prognosis-enhanced optimal control capability. </LI> <LI> Integrated control characteristic for ‘drive-brake’ systems. </LI> <LI> Advanced prognosis control enabled maintenance optimization. </LI> </UL> </P>

Remaining-Life Prediction of Solder Joints Using RF Impedance Analysis and Gaussian Process Regression

Daeil Kwon,Azarian, Michael H.,Pecht, Michael IEEE 2015 IEEE transactions on components, packaging, and ma Vol.5 No.11

<P>Solder joints are among the most common failure sites in electronic assemblies. This paper presents a prognostic approach that allows for the remaining useful life prediction of solder joints using an RF impedance analysis and the Gaussian process (GP) regression. While the solder joints were exposed to a mechanical stress condition to generate fatigue failures, the RF impedance of the solder joint was continuously monitored. The RF impedance provided an early indication of the impending solder-joint failure in the form of a gradual increase prior to the end of life. A GP model was applied to the RF impedance obtained from the fatigue tests in order to estimate the remaining life of the solder joint in real time. It was demonstrated that the GP model successfully predicted the time to failure of the solder joint with high accuracy prior to failure. The prediction performance was also evaluated using prognostic metrics.</P>

A probabilistic description scheme for rotating machinery health evaluation

Qiang Miao,Dong Wang,Michael Pecht 대한기계학회 2010 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.24 No.12

Condition-based maintenance has become more popular in recent years because of its advantages in terms of minimizing downtime,extending lifetime, and reducing cost. This kind of maintenance strategy is based on condition monitoring of machinery in operation. Ccondition monitoring is a key step in maintenance decision analysis. Numerous non-stationary signal processing methods have been developed to reveal fault characteristics in rotating machinery. In this study, an adaptive signal analysis method called empirical mode decomposition is employed for gearbox vibration signal preprocessing. Considering a modulation phenomenon that appeared in a faulty gear, the Hilbert Transform is applied to obtain an envelope signature, which usually contains abundant fault-related signatures. Being different from other failure classification problems, this paper is concerned with determining the probability of normal condition based on current observations describing the condition of a gearbox. Moreover, according to Bayes rule, this problem can be translated to estimate the conditional probability of current observations given normal gearbox condition using a Hidden Markov Model. From this point, a novel probabilistic health description index called Average Probability Index is proposed for gearbox health evaluation. For automatic detection, a semi-dynamic threshold is presented to detect an early fault in a gear. At last, validation and comparative studies are performed using two sets of gearbox lifetime accelerated testing vibration data. The results show the advantages of the proposed method for gearbox condition monitoring.

비선형모델링을 통한 온습도 바이어스 시험 중의 다층 세라믹축전기 수명 예측

Kwon, Daeil,Azarian, Michael H.,Pecht, Michael 한국마이크로전자및패키징학회 2013 마이크로전자 및 패키징학회지 Vol.20 No.3

This study presents an approach to predict insulation resistance failure of multilayer ceramic capacitors (MLCCs) using non-linear modeling. A capacitance aging model created by non-linear modeling allowed for the prediction of insulation resistance failure. The MLCC data tested under temperature-humidity-bias testing conditions showed that a change in capacitance, when measured against a capacitance aging model, was able to provide a prediction of insulation resistance failure.

Deep Residual Networks With Dynamically Weighted Wavelet Coefficients for Fault Diagnosis of Planetary Gearboxes

Zhao, Minghang,Kang, Myeongsu,Tang, Baoping,Pecht, Michael Institute of Electrical and Electronics Engineers 2018 IEEE transactions on industrial electronics Vol.65 No.5

<P>One of the significant tasks in data-driven fault diagnosis methods is to configure a good feature set involving statistical parameters. However, statistical parameters are often incapable of representing the dynamic behavior of planetary gearboxes under variable operating conditions. Although the use of deep learning algorithms to find a good set of features for fault diagnosis has somewhat improved diagnostic performance, the lack of domain knowledge incorporated into deep learning algorithms has limited further improvement. Accordingly, this paper developed a variant of deep residual networks (DRNs), the so-called deep residual networks with dynamically weighted wavelet coefficients (DRN+DWWC) to improve diagnostic performance, which takes a series of sets of wavelet packet coefficients on various frequency bands as an input. Further, the fact that no general consensus has been reached as to which frequency band contains the most intrinsic information about a planetary gearbox's health status calls for “dynamic weighting layers” in the DRN+DWWC and the role of the layers is to dynamically adjust a weight applied to each set of wavelet packet coefficients to find a discriminative set of features that will be further used for planetary gearbox fault diagnosis.</P>

Two-level fault diagnosis RBF networks for auto-transformer rectifier units using multi-source features

Lin, Yi,Ge, Hongjuan,Chen, Shuwen,Pecht, Michael The Korean Institute of Power Electronics 2020 JOURNAL OF POWER ELECTRONICS Vol.20 No.3

The auto-transformer rectifier unit (ATRU) is one of the most widely used avionic secondary power supplies. Timely fault identification and location of the ATRU is significant in terms of system reliability. A two-level fault diagnosis method for the ATRU using multi-source features (MSF) is proposed in this paper. Based on the topology of the ATRU, three key electrical signals are selected and analyzed to extract appropriate features for fault diagnosis. Mathematic expressions and simulation results of the feature signals under different fault modes are presented in the paper. Therefore, a unique MSF system is developed and a two-level fault diagnosis method based on radial basis function network groups is proposed. On the first level, the overall fault set is classified into three subsets and then on the second level, three radial basis function neural networks are constructed and trained to realize accurate fault localization. To verify the diagnosis performance of the proposed method, several comparative tests are implemented on a 12-pulse ATRU system, which shows that this method has a lower computational cost, better diagnostic accuracy and increased stability when compared with alternative methods.

A Massively Parallel Approach to Real-Time Bearing Fault Detection Using Sub-Band Analysis on an FPGA-Based Multicore System

Kang, Myeongsu,Kim, Jaeyoung,Jeong, In-Kyu,Kim, Jong-Myon,Pecht, Michael Institute of Electrical and Electronics Engineers 2016 IEEE transactions on industrial electronics Vol. No.

<P>The fact that rolling element bearing faults have an amplitude-modulating effect on their characteristic frequencies calls for sub-band analysis to determine an optimal sub-band signal that contains intrinsic information about bearing faults. In this regard, it is significant to accurately assess the presence of a bearing's abnormal symptoms. Hence, a bearing abnormality index (BAI) that properly quantifies how much information a sub-band signal contains about bearing faults is presented. Additionally, to facilitate real-time sub-band analysis based on the BAI, a massively parallel approach is introduced, where the approach involves the use of the multicore system. Likewise, the multicore system supports high-performance computing by exploiting 128 processing elements operating at 200 MHz in a Xilinx Virtex-7 field-programmable gate array (FPGA) device.</P>

A Life Model for Supercapacitors

Williard, Nick,Dongcheon Baek,Jong Won Park,Byung-Oh Choi,Osterman, Michael,Pecht, Michael IEEE 2015 IEEE TRANSACTIONS ON DEVICE AND MATERIALS RELIABIL Vol.15 No.4

<P>Supercapacitors provide high-power energy storage for electrical systems. The expected useful life of a supercapacitor is related to the oxidation of functional groups on the graphite electrode surface during usage, and it is highly dependent on operational voltage and temperature. In this paper, a life model is developed for commercial supercapacitors. The model incorporates a new voltage multiplier to describe the combined effects of temperature and voltage on supercapacitor life. Accelerated testing was conducted to obtain the time to failure of supercapacitors over a range of voltage and temperature conditions, validate the life model, and compare the model with two previously established capacitor life models. Failure was defined by a 30% decrease in capacitance or a 100% increase in equivalent series resistance.</P>

A Hybrid Feature Selection Scheme for Reducing Diagnostic Performance Deterioration Caused by Outliers in Data-Driven Diagnostics

Myeongsu Kang,Islam, Md Rashedul,Jaeyoung Kim,Jong-Myon Kim,Pecht, Michael IEEE 2016 IEEE transactions on industrial electronics Vol.63 No.5

<P>In practice, outliers, defined as data points that are distant from the other agglomerated data points in the same class, can seriously degrade diagnostic performance. To reduce diagnostic performance deterioration caused by outliers in data-driven diagnostics, an outlier-insensitive hybrid feature selection (OIHFS) methodology is developed to assess feature subset quality. In addition, a new feature evaluation metric is created as the ratio of the intraclass compactness to the interclass separability estimated by understanding the relationship between data points and outliers. The efficacy of the developed methodology is verified with a fault diagnosis application by identifying defect-free and defective rolling element bearings under various conditions.</P>