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PCの電源が故障する原因や症状は？対処法やチェックの手順も紹介
https://www.4900.co.jp/smarticle/12153/

電源ユニット（PSU）の故障原因とその症状まとめ
https://net-shitsuji.jp/pc/product/content/psu.html

パソコン故障の切り分け方法に電源があれば良い理由
https://btopc.jp/repair/repair-pc-2019.html

トラブル発生時の原因特定方法
https://akiba-pc.watch.impress.co.jp/docs/dosv/642993.html

電源投入時のトラブル対策編1 ～電源が入らない場合～
【保存版自作PC「トラブル」の原因と対策(2)】（2015/6/1 12:05）
https://akiba-pc.watch.impress.co.jp/docs/dosv/704684.html

参考資料(reference) @ arXiv

Electrical fault

arXiv:2003.10375 [pdf, other] eess.SP cs.LG stat.ML
FTT-NAS: Discovering Fault-Tolerant Neural Architecture

Authors: Xuefei Ning, Guangjun Ge, Wenshuo Li, Zhenhua Zhu, Yin Zheng, Xiaoming Chen, Zhen Gao, Yu Wang, Huazhong Yang

Abstract: …by deep learning are moving from the cloud to the edge. When deploying neural networks (NNs) onto the devices under complex environments, there are various types of possible faults: soft errors caused by cosmic radiation and radioactive impurities, voltage instability, aging, temperature variations, and malicious attackers. Thus the safety risk of deploying… ▽ More
Submitted 20 March, 2020; originally announced March 2020.

Comments: 13 pages, 9 figures

arXiv:2003.10145 [pdf, other] eess.SY
Identifying DC Faults in HVDC-VSC Systems for Integrating Large Offshore Wind -- A Localized Protection Scheme

Authors: Vaibhav Nougain, Sukumar Mishra, George S. Misyris, Spyros Chatzivasileiadis

Abstract: We propose a localized protection scheme based on rate of change of Running Autoregressive Smoothing Average (RASA) of fault limiting reactor voltage in VSC-HVDC systems. The paper addresses the issues of DC-… ▽ More
Submitted 23 March, 2020; originally announced March 2020.

arXiv:2003.09802 [pdf, other] cs.CV eess.IV
Review of data analysis in vision inspection of power lines with an in-depth discussion of deep learning technology

Authors: Xinyu Liu, Xiren Miao, Hao Jiang, Jing Chen

Abstract: …identifies the challenges for future research. Following the typical procedure of inspection data analysis, we categorize current works in this area into component detection and fault diagnosis. For each aspect, the techniques and methodologies adopted in the literature are summarized. Some valuable information is also included such as data description and m… ▽ More
Submitted 22 March, 2020; originally announced March 2020.

arXiv:2003.09710 [pdf] eess.SY
doi
10.1080/00207217.2017.1378380
Reliability Analysis of Component Level Redundant Topologies for solid state Fault Current Limiter

Authors: Masoud Farhadi, Mehdi Abapour, Behnam Mohammadi-Ivatloo

Abstract: …for the redundant design in component-level. This paper presents a comparative reliability analysis between different component-level redundant designs for solid state fault current limiter (SSFCL). The aim of the proposed analysis is to determine the more reliable component-level redundant configuration. The mean time to failure (MTTF) is used as the reliab… ▽ More
Submitted 21 March, 2020; originally announced March 2020.

arXiv:2003.09609 [pdf, ps, other] quant-ph eess.SY
Fault-tolerant Coherent H-infinity Control for Linear Quantum Systems

Authors: Yanan Liu, Daoyi Dong, Ian R. Petersen, Qing Gao, Steven X. Ding, Shota Yokoyama, Hidehiro Yonezawa

Abstract: …practical quantum control systems. The purpose of this paper is to design a coherent feedback controller for a class of linear quantum systems suffering from Markovian jumping faults so that the closed-loop quantum system has both… ▽ More
Submitted 21 March, 2020; originally announced March 2020.

Comments: 12 pages, 3 figures

arXiv:2003.08483 [pdf, other] eess.SY cs.LG math.NA
Fault Handling in Large Water Networks with Online Dictionary Learning

Authors: Paul Irofti, Florin Stoican, Vicenç Puig

Abstract: Fault detection and isolation in water distribution networks is an active topic due to its model's mathematical complexity and increased data availability through sensor placement. Here we simplify the model by offering a data driven alternative that takes the network topology into account when performing sensor placement and then proceeds to build a net… ▽ More
Submitted 18 March, 2020; originally announced March 2020.

Comments: Submitted to Journal of Process Control

arXiv:2003.04891 [pdf] eess.SP
Data-mining for Fault-Zone Detection of Distance Relay in FACTS-Based Transmission

Authors: Nima Salek Gilani, Mohammad Tavakoli Bina, Fatemeh Rahmani, Mahmood Hosseini Imani

Abstract: In this study, the problem of fault zone detection of distance relaying in FACTS-based transmission lines is analyzed. Existence of FACTS devices on the transmission line, when they are included in the… ▽ More
Submitted 9 March, 2020; originally announced March 2020.

Comments: 6 pages, 1 figure, 11 tables, To appear in the XIV, 2020 IEEE Texas Power and Energy Conference (TPEC), 6-7 February, 2020, Texas, U.S.A

arXiv:2003.03674 [pdf, other] cs.NI cs.DC eess.SP
Error Correction with Systematic RLNC in Multi-Channel THz Communication Systems

Authors: Cao Vien Phung, Anna Engelmann, Admela Jukan

Abstract: …a code rate, transmission rate of auxiliary channels, the number of THz channels, the modulation format and transmission distance as required system configurations for a fault tolerant THz transmission. ▽ More
Submitted 7 March, 2020; originally announced March 2020.

Comments: 6 pages, 5 figures

arXiv:2003.02671 [pdf, other] eess.SP cs.LG math.DS math.OC stat.ML
Hybrid modeling: Applications in real-time diagnosis

Authors: Ion Matei, Johan de Kleer, Alexander Feldman, Rahul Rai, Souma Chowdhury

Abstract: …we use optimization platforms featuring automatic differentiation. Training data is generated by simulating the high-fidelity model. We showcase our approach in the context of fault diagnosis of a rail switch system. Three new model abstractions whose complexities are two orders of magnitude smaller than the complexity of the high fidelity model, both in the… ▽ More
Submitted 3 March, 2020; originally announced March 2020.

arXiv:2003.02649 [pdf, other] eess.SP
An Audio-Based Fault Diagnosis Method for Quadrotors Using Convolutional Neural Network and Transfer Learning

Authors: Wansong Liu, Zhu Chen, Minghui Zheng

Abstract: Quadrotor unmanned aerial vehicles (UAVs) have been developed and applied into several types of workplaces, such as warehouses, which usually involve human workers. The co-existence of human and UAVs brings new challenges to UAVs: potential failure of UAVs may cause risk and danger to surrounding human. Effective and efficient detection of such failure may provide early warning to the surrounding… ▽ More
Submitted 2 March, 2020; originally announced March 2020.

Comments: ACC 2020 Draft

arXiv:2003.02422 [pdf, other] eess.SY
Deep Reinforcement Learning-Based Robust Protection in Electric Distribution Grids

Authors: Dongqi Wu, Dileep Kalathil, Le Xie

Abstract: …based control architecture for protective relay control in power distribution systems. The key challenge in protective relay control is to quickly and accurately detect faults from other disturbances in the system. The performance of widely-used traditional overcurrent protection scheme is limited by factors including distributed generations, power electroni… ▽ More
Submitted 4 March, 2020; originally announced March 2020.

Comments: Submitted to IEEE Transactions of Power Delivery, under review

arXiv:2003.01502 [pdf, ps, other] math.OC eess.SY math.DS
Fault detection and isolation for linear structured systems

Authors: Jiajia Jia, Harry L. Trentelman, M. Kanat Camlibel

Abstract: This paper deals with the fault detection and isolation (FDI) problem for linear structured systems in which the system matrices are given by zero/nonzero/arbitrary pattern matrices. In this paper, we follow a geometric approach to verify solvability of the FDI problem for such systems. To do so, we first develop a necessary and sufficient condition under wh… ▽ More
Submitted 3 March, 2020; originally announced March 2020.

Comments: 6 pages, 1 figure, 1 table

arXiv:2003.00264 [pdf] quant-ph cs.LG eess.SY math.OC stat.ML
Quantum Computing Assisted Deep Learning for Fault Detection and Diagnosis in Industrial Process Systems

Authors: Akshay Ajagekar, Fengqi You

Abstract: Quantum computing (QC) and deep learning techniques have attracted widespread attention in the recent years. This paper proposes QC-based deep learning methods for fault diagnosis that exploit their unique capabilities to overcome the computational challenges faced by conventional data-driven approaches performed on classical computers. Deep belief networks… ▽ More
Submitted 29 February, 2020; originally announced March 2020.

arXiv:2002.12217 [pdf, ps, other] cs.MA
Multi-agent maintenance scheduling based on the coordination between central operator and decentralized producers in an electricity market

Authors: Pegah Rokhforoz, Blazhe Gjorgiev, Giovanni Sansavini, Olga Fink

Abstract: Condition-based and predictive maintenance enable early detection of critical system conditions and thereby enable decision makers to forestall faults and mitigate them. However, decision makers also need to take the operational and production needs into consideration for optimal decision-making when scheduling maintenance activities. Particularly in network… ▽ More
Submitted 27 February, 2020; originally announced February 2020.

Comments: 17 pages, 7 figures

arXiv:2002.11564 [pdf, other] cs.RO cs.LG eess.SY
Mid-flight Propeller Failure Detection and Control of Propeller-deficient Quadcopter using Reinforcement Learning

Authors: Rohitkumar Arasanipalai, Aakriti Agrawal, Debasish Ghose

Abstract: …system is adaptive, unlike traditional control system based controllers. In order to develop an end-to-end system, the paper also proposes a novel neural network based propeller fault detection system to detect propeller loss and switch to the appropriate controller. Our simulation results demonstrate a stable quadcopter with efficient waypoint tracking for… ▽ More
Submitted 26 February, 2020; originally announced February 2020.

Comments: 8 pages, 34 images (11 figures)

arXiv:2002.11146 [pdf, ps, other] quant-ph hep-lat nucl-th
Quantum Algorithms for Simulating the Lattice Schwinger Model

Authors: Alexander F. Shaw, Pavel Lougovski, Jesse R. Stryker, Nathan Wiebe

Abstract: …is a testbed for the study of quantum gauge field theories. We give scalable, explicit digital quantum algorithms to simulate the lattice Schwinger model in both NISQ and fault-tolerant settings. In particular, we perform a tight analysis of low-order Trotter formula simulations of the Schwinger model, using recently derived commutator bounds, and give upper… ▽ More
Submitted 25 February, 2020; originally announced February 2020.

Report number: INT-PUB-20-008

arXiv:2002.10974 [pdf, other] cs.CV cs.LG eess.IV
doi
10.1109/TIE.2019.2931220
Fault Diagnosis in Microelectronics Attachment via Deep Learning Analysis of 3D Laser Scans

Authors: Nikolaos Dimitriou, Lampros Leontaris, Thanasis Vafeiadis, Dimosthenis Ioannidis, Tracy Wotherspoon, Gregory Tinker, Dimitrios Tzovaras

Abstract: …operator a process that is both time consuming and inefficient especially in preproduction runs where the error rate is high. In this paper we propose a system that automates fault diagnosis by accurately estimating the volume of glue deposits before and even after die attachment. To this end a modular scanning system is deployed that produces high resolutio… ▽ More
Submitted 25 February, 2020; originally announced February 2020.

Comments: 10 pages, 12 figures. in IEEE Transactions on Industrial Electronics, 2019 (early access)

arXiv:2002.10966 [pdf] eess.SY
Graph-based Faulted Line Identification Using Micro-PMU Data in Distribution Systems

Authors: Ying Zhang, Jianhui Wang, Mohammad Khodayar

Abstract: Motivated by increasing penetration of distributed generators (DGs) and fast development of micro-phasor measurement units (μPMUs), this paper proposes a novel graph-based faulted line identification algorithm using a limited number of μPMUs in distribution networks. The core of the proposed method is to apply advanced distribution system state estimation (D… ▽ More
Submitted 25 February, 2020; originally announced February 2020.

arXiv:2002.09945 [pdf, other] cs.LG eess.SP
doi
10.1109/DSN-S.2019.00021
On the Estimation of Complex Circuits Functional Failure Rate by Machine Learning Techniques

Authors: Thomas Lange, Aneesh Balakrishnan, Maximilien Glorieux, Dan Alexandrescu, Luca Sterpone

Abstract: …efforts mandated by today's Functional Safety requirements. Determining the Functional De-Rating of sequential logic cells typically requires computationally intensive fault-injection simulation campaigns. In this paper a new approach is proposed which uses Machine Learning to estimate the Functional De-Rating of individual flip-flops and thus, optimisin… ▽ More
Submitted 18 February, 2020; originally announced February 2020.

Comments: arXiv admin note: text overlap with arXiv:2002.08882

arXiv:2002.08999 [pdf] eess.SY
Towards Critical Clearing Time Sensitivity for DAE Systems with Singularity

Authors: Chetan Mishra, Chen Wang, Xin Xu, Virgilio A. Centeno

Abstract: …a bifurcation of the transient load flow solutions which is marked by the system trajectory reaching a singular surface in state space where the voltage causality is lost. If a fault is expected to cause voltage collapse, preventive control decisions such as changes in AVR settings need to be taken in order to get enhance the system stability. In this regard… ▽ More
Submitted 20 February, 2020; originally announced February 2020.

Comments: To be presented in IEEE PES General Meeting 2020 in Montreal

arXiv:2002.08882 [pdf, ps, other] eess.SP cs.LG
doi
10.1109/IOLTS.2019.8854423
Machine Learning to Tackle the Challenges of Transient and Soft Errors in Complex Circuits

Authors: Thomas Lange, Aneesh Balakrishnan, Maximilien Glorieux, Dan Alexandrescu, Luca Sterpone

Abstract: …processing resources and tool licenses. Thereby, de-rating or vulnerability factors are a major instrument of failure analysis efforts. Usually computationally intensive fault-injection simulation campaigns are required to obtain a fine-grained reliability metrics for the functional level. Therefore, the use of machine learning algorithms to assist this pro… ▽ More
Submitted 18 February, 2020; originally announced February 2020.

arXiv:2002.07997 [pdf, other] cs.LG eess.SP
Neural Architecture Search For Fault Diagnosis

Authors: Xudong Li, Yang Hu, Jianhua Zheng, Mingtao Li

Abstract: Data-driven methods have made great progress in fault diagnosis, especially deep learning method. Deep learning is suitable for processing big data, and has a strong feature extraction ability to realize end-to-end… ▽ More
Submitted 18 February, 2020; originally announced February 2020.

arXiv:2002.07605 [pdf] eess.SP cs.LG stat.ML
A comprehensive review on convolutional neural network in machine fault diagnosis

Authors: Jinyang Jiao, Ming Zhao, Jing Lin, Kaixuan Liang

Abstract: With the rapid development of manufacturing industry, machine fault diagnosis has become increasingly significant to ensure safe equipment operation and production. Consequently, multifarious approaches have been explored and developed in the past years, of which intelligent algorithms develop particularly rapidly. Convolutional neural network, as a typical… ▽ More
Submitted 13 February, 2020; originally announced February 2020.

arXiv:2002.06695 [pdf] eess.SY
Electrical Machines Fault Detection through Frequency Response Analysis (FRA) -- Part I: Stator

Authors: Reza Khalilisenobari, Javad Sadeh

Abstract: Remarkable performance of the Frequency Response Analysis (FRA) technique in transformers' fault detection, structural similarity between transformers and… ▽ More
Submitted 16 February, 2020; originally announced February 2020.

arXiv:2002.06316 [pdf] eess.SP
An Effective EMTR-Based High-Impedance Fault Location Method for Transmission Lines

Authors: Jianwei An, Chijie Zhuang, Farhad Rachidi, Rong Zeng

Abstract: This paper summarizes first the electromagnetic time reversal (EMTR) technique for fault location, and further numerically validates its effectiveness when the… ▽ More
Submitted 14 February, 2020; originally announced February 2020.

arXiv:2002.03639 [pdf, ps, other] cs.AI cs.IT eess.SP
iDCR: Improved Dempster Combination Rule for Multisensor Fault Diagnosis

Authors: Nimisha Ghosh, Sayantan Saha, Rourab Paul

Abstract: …be effectively fused for accurate monitoring of many engineering applications. In the last few years, one of the most sought after applications for multi sensor fusion has been fault diagnosis. Dempster-Shafer Theory of Evidence along with Dempsters Combination Rule is a very popular method for multi sensor fusion which can be successfully applied to… ▽ More
Submitted 10 February, 2020; originally announced February 2020.

arXiv:2002.03596 [pdf] cs.IT eess.SP
doi
10.1109/PTC.2009.5282257
Performance of Distance Relays in Presence of IPFC

Authors: M. Pouyan, F. Razavi, M. RashidiNejad

Abstract: …Firstly a detailed model of the IPFC and its control is proposed and then it is integrated into the 8-bus transmission system for the purposes of accurately simulating the fault transients. The simulation results show the impact of different operational mode of IPFC on the performance of a distance protection relays. ▽ More
Submitted 10 February, 2020; originally announced February 2020.

Comments: 6 pages, 13 figures

arXiv:2002.03411 [pdf, other] eess.SY math.DS
doi
10.1016/j.isatra.2018.03.014
Adaptive super-twisting observer for fault reconstruction in electro-hydraulic systems

Authors: Mohammad Bahrami, Mahyar Naraghi, Mohammad Zareinejad

Abstract: An adaptive-gain super-twisting sliding mode observer is proposed for fault reconstruction in electro-hydraulic servo systems (EHSS) receiving bounded perturbations with unknown bounds. The objective is to address challenging problems in classic sliding mode observers: chattering effect, conservatism of observer gains, strong condition on the distribution of… ▽ More
Submitted 9 February, 2020; originally announced February 2020.

Comments: Final version

Journal ref: ISA Transactions Volume 76, May 2018, Pages 235-245

arXiv:2002.03207 [pdf, other] eess.SP eess.SY
Design and Selection of Additional Residuals To Enhance Fault Isolation of A Turbocharged Spark Ignited Engine System

Authors: K. Y. Ng, E. Frisk, M. Krysander

Abstract: This paper presents a method to enhance fault isolation without adding physical sensors on a turbocharged spark ignited petrol engine system by designing additional residuals from an initial observer-based residuals setup. The best candidates from all potential additional residuals are selected using the concept of sequential residual generation to ensure be… ▽ More
Submitted 8 February, 2020; originally announced February 2020.

Comments: 6 pages, 11 figures, Submitted to CoDIT 2020

arXiv:2002.03201 [pdf, other] eess.SY eess.SP
doi
10.1109/MCS.2019.2961793
A Realistic Simulation Testbed of A Turbocharged Spark-Ignited Engine System: A Platform for the Evaluation of Fault Diagnosis Algorithms and Strategies

Authors: K. Y. Ng, E. Frisk, M. Krysander, L. Eriksson

Abstract: Research on fault diagnosis on highly nonlinear dynamic systems such as the engine of a vehicle have garnered huge interest in recent years, especially with the automotive industry heading towards self-driving technologies. This article presents a novel opensource simulation testbed of a turbocharged spark ignited (TCSI) petrol engine system for testing and… ▽ More
Submitted 8 February, 2020; originally announced February 2020.

Comments: 64 pages, 23 figures, To appear in IEEE Control Systems

Journal ref: IEEE Control Systems Magazine 40 (2020) 56-83

arXiv:2001.10098 [pdf, other] cs.LG eess.SP stat.ML
Multi-label Prediction in Time Series Data using Deep Neural Networks

Authors: Wenyu Zhang, Devesh K. Jha, Emil Laftchiev, Daniel Nikovski

Abstract: This paper addresses a multi-label predictive fault classification problem for multidimensional time-series data. While… ▽ More
Submitted 27 January, 2020; originally announced January 2020.

Comments: Accepted by IJPHM. Presented at PHM19

arXiv:2001.04729 [pdf, ps, other] math.OC cs.PL eess.SY
A unified method to decentralized state inference and fault diagnosis/prediction of discrete-event systems

Authors: Kuize Zhang

Abstract: The state inference problem and fault diagnosis/prediction problem are fundamental topics in many areas. In this paper, we consider discrete-event systems (DESs) modeled by finite-state automata (FSAs). There exist results for decentralized versions of the latter problem but there is almost no result for a decentralized version of the former problem. We prop… ▽ More
Submitted 13 February, 2020; v1 submitted 14 January, 2020; originally announced January 2020.

Comments: 30 pages,12 figures, 2 tables

MSC Class: 68Q45; 93B07

arXiv:2001.03612 [pdf, other] cs.LG eess.SP
A Deep Learning Approach Towards Prediction of Faults in Wind Turbines

Authors: Joyjit Chatterjee, Nina Dethlefs

Abstract: With the rising costs of conventional sources of energy, the world is moving towards sustainable energy sources including wind energy. Wind turbines consist of several electrical and mechanical components and experience an enormous amount of irregular loads, making their operational behaviour at times inconsistent. Operations and Maintenance (O&M) is a k… ▽ More
Submitted 11 December, 2019; originally announced January 2020.

Comments: Presented at the Northern Lights Deep Learning Workshop (NLDL), Tromso, Norway in January 2019. The workshop program can be found at http://nldl2019.org/program.html page

arXiv:2001.03321 [pdf, ps, other] cs.NI eess.SY
Fault Tolerance for Service Function Chains

Authors: Milad Ghaznavi, Elaheh Jalalpour, Bernard Wong, Raouf Boutaba, Ali Jose Mashtizadeh

Abstract: …function chain, or simply a chain. Tolerating failures when they occur along chains is imperative to the availability and reliability of enterprise applications. Making a chain fault-tolerant is challenging since, in the event of failures, the state of faulty middleboxes must be correctly and quickly recovered while providing high throughput and low latency.… ▽ More
Submitted 25 February, 2020; v1 submitted 10 January, 2020; originally announced January 2020.

arXiv:2001.02015 [pdf, other] eess.SP cs.LG eess.IV
doi
10.1109/TIE.2019.2962438
Missing-Class-Robust Domain Adaptation by Unilateral Alignment for Fault Diagnosis

Authors: Qin Wang, Gabriel Michau, Olga Fink

Abstract: …MNIST-M adaptation task. The proposed methodology is also evaluated on a fault diagnosis task, where the problem of missing fault types in the target training dataset is common in practice. Both experiments demonstrate the effectiveness of the proposed methodology. ▽ More
Submitted 7 January, 2020; originally announced January 2020.

arXiv:1912.12941 [pdf, other] cs.LG eess.SP stat.ML
doi
10.1016/j.ymssp.2019.106585
A general anomaly detection framework for fleet-based condition monitoring of machines

Authors: Kilian Hendrickx, Wannes Meert, Yves Mollet, Johan Gyselinck, Bram Cornelis, Konstantinos Gryllias, Jesse Davis

Abstract: …machines. In most of these applications, it is safe to assume healthy conditions for the majority of machines. Deviating machine behavior is then an indicator for a machine fault. This work proposes an unsupervised, generic, anomaly detection framework for fleet-based condition monitoring. It uses generic building blocks and offers three key advantages. Firs… ▽ More
Submitted 7 January, 2020; v1 submitted 30 December, 2019; originally announced December 2019.

Comments: Accepted in Mechanical Systems and Signal Processing, SI: Machine Diagnostics by AI

MSC Class: I.5.3; I.5.4; J.2; I.2.1 ACM Class: I.5.3; I.5.4; J.2; I.2.1

arXiv:1912.12528 [pdf, other] eess.SP cs.LG
Unsupervised Deep Transfer Learning for Intelligent Fault Diagnosis: An Open Source and Comparative Study

Authors: Zhibin Zhao, Qiyang Zhang, Xiaolei Yu, Chuang Sun, Shibin Wang, Ruqiang Yan, Xuefeng Chen

Abstract: Recent progress on intelligent fault diagnosis has greatly depended on the deep learning and plenty of labeled data. However, the machine often operates with various working conditions or the target task has different distributions with the collected data used for training (we called the domain shift problem). This leads to the deep transfer learning based (… ▽ More
Submitted 28 December, 2019; originally announced December 2019.

Comments: 35 pages, 20 figures, 12 Tables, and a journal paper

arXiv:1912.12326 [pdf] physics.app-ph cond-mat.mtrl-sci
doi
10.1002/aelm.201901171
Impact of stacking faults and domain boundaries on the electronic transport in cubic silicon carbide probed by conductive atomic force microscopy

Authors: F. Giannazzo, G. Greco, S. Di Franco, P. Fiorenza, I. Deretzis, A. La Magna, C. Bongiorno, M. Zimbone, F. La Via, M. Zielinski, F. Roccaforte

Abstract: …this work, a macro- and nano-scale characterization of Schottky contacts on 3C-SiC/Si was carried out, to elucidate the impact of the anti-phase-boundaries (APBs) and stacking-faults (SFs) on the forward and reverse current-voltage characteristics of these devices. Current mapping of 3C-SiC by conductive atomic force microscopy (CAFM) directly showed the rol… ▽ More
Submitted 16 January, 2020; v1 submitted 27 December, 2019; originally announced December 2019.

Comments: 20 pages, 7 figures

Journal ref: Adv. Electron. Mater. 2019, 1901171

arXiv:1912.09080 [pdf, other] eess.SY
doi
10.1016/j.jprocont.2019.11.006
Real-Time Estimation of a Multi-Stage Centrifugal Compressor Performance Map Considering Real-Gas Processes and Flexible Operation

Authors: Maik Gentsch, Rudibert King

Abstract: …monitoring: The first indicates the level of confidence in the local estimate, and the second points to drastic performance map alterations, which may be further exploited in fault detection. A modified reference simulation of a two-stage supercritical carbon dioxide compressor with known state trajectories, performance maps, and alterations demonstrates the… ▽ More
Submitted 19 December, 2019; originally announced December 2019.

Comments: to be published in Journal of Process Control, Volume 85

arXiv:1912.07582 [pdf, other] eess.SY
A Nonlinear Regression Method for Composite Protection Modeling of Induction Motor Loads

Authors: Soumya Kundu, Zhigang Chu, Yuan Liu, Yingying Tang, Qiuhua Huang, Daniel James, Yu Zhang, Pavel Etingov, David P. Chassin

Abstract: Protection equipment is used to prevent damage to induction motor loads by isolating them from power systems in the event of severe faults. Modeling the response of induction motor loads and their protection is vital for power system planning and operation, especially in understanding system's dynamic performance and stability after a… ▽ More
Submitted 16 December, 2019; originally announced December 2019.

Comments: accepted for presentation at The Eleventh Conference on Innovative Smart Grid Technologies (ISGT 2020)

Report number: PNNL-SA-146910

arXiv:1912.07394 [pdf, ps, other] eess.SP cs.CV cs.LG
doi
10.1109/DFT.2019.8875314
Efficient Error-Tolerant Quantized Neural Network Accelerators

Authors: Giulio Gambardella, Johannes Kappauf, Michaela Blott, Christoph Doehring, Martin Kumm, Peter Zipf, Kees Vissers

Abstract: …to meet throughput, latency and power requirements. Functional safety and error tolerance need to be considered as additional requirement in safety critical systems. In general, fault tolerant operation can be achieved by adding redundancy to the system, which is further exacerbating the computational demands. Furthermore, the question arises whether pruning… ▽ More
Submitted 16 December, 2019; originally announced December 2019.

Comments: 6 pages, 5 figures

Journal ref: 2019 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT)

arXiv:1912.07383 [pdf, ps, other] eess.SP eess.SY
A Survey of Predictive Maintenance: Systems, Purposes and Approaches

Authors: Yongyi Ran, Xin Zhou, Pengfeng Lin, Yonggang Wen, Ruilong Deng

Abstract: …which mainly comprise cost minimization, availability/reliability maximization and multi-objective optimization. Furthermore, we provide a review of the existing approaches for fault diagnosis and prognosis in PdM systems that include three major subcategories: knowledge based, traditional Machine Learning (ML) based and DL based approaches. We make a brief… ▽ More
Submitted 12 December, 2019; originally announced December 2019.

Comments: 36 pages, 24 figures

arXiv:1912.05364 [pdf, other] eess.SY cs.LO cs.PF
Breaking the Limits of Redundancy Systems Analysis

Authors: Clemens Dubslaff, Kai Ding, Andrey Morozov, Christel Baier, Klaus Janschek

Abstract: Redundancy mechanisms such as triple modular redundancy protect safety-critical components by replication and thus improve systems fault tolerance. However, the gained fault tolerance comes along with costs to be invested, e.g., increasing execution time, energy consumption, or packaging size, for which constraints hav… ▽ More
Submitted 11 December, 2019; originally announced December 2019.

Comments: This paper is a preprint of the corresponding ESREL'19 conference publication

arXiv:1912.03688 [pdf, other] eess.SP
Deep Prototypical Networks Based Domain Adaptation for Fault Diagnosis

Authors: Huanjie Wang, Jie Tan, Xiwei Bai, Jiechao Yang

Abstract: …leads to performance degradation of traditional machine learning methods. This work provides a framework that combines supervised domain adaptation with prototype learning for fault diagnosis. The main idea of domain adaptation is to apply the Siamese architecture to learn a latent space where the mapped features are inter-class separable and intra-class sim… ▽ More
Submitted 11 December, 2019; v1 submitted 8 December, 2019; originally announced December 2019.

arXiv:1912.01096 [pdf, other] cs.LG eess.SP stat.ML
Semi-Supervised Learning of Bearing Anomaly Detection via Deep Variational Autoencoders

Authors: Shen Zhang, Fei Ye, Bingnan Wang, Thomas G. Habetler

Abstract: Most of the data-driven approaches applied to bearing fault diagnosis up to date are established in the supervised learning paradigm, which usually requires a large set of labeled data collected a priori. In practical applications, however, obtaining accurate labels based on real-time bearing conditions can be far more challenging than simply collecting a hu… ▽ More
Submitted 8 December, 2019; v1 submitted 2 December, 2019; originally announced December 2019.

arXiv:1912.00449 [pdf] eess.SY
A Novel Robust Fault Detection Scheme of Lipschitz Nonlinear Systems Using Combination of Bond Graph and Observer

Authors: Mohammad Ghasem Kazemi, Mohsen Montazeri

Abstract: This paper deals with a new robust fault detection (FD) scheme for nonlinear Lipschitz systems wherein a robust nonlinear observer is used in combination with the Bond Graph (BG) method. In order to improve the efficiency of the classical Analytical Redundancy Relations (ARRs) FD scheme based on the BG method, a new form of the ARRs for the nonlinear Lipschi… ▽ More
Submitted 1 December, 2019; originally announced December 2019.

Comments: 22 pages, 14 figures

arXiv:1911.13263 [pdf] cs.LG eess.SP
Multi-PCA based Fault Detection Model Combined with Prior knowledge of HVAC

Authors: Ziming Liu, Xiaobo Liu

Abstract: The traditional PCA fault detection methods completely depend on the training data. The prior knowledge such as the physical principle of the system has not been taken into account. In this paper, we propose a new multi-PCA… ▽ More
Submitted 21 November, 2019; originally announced November 2019.

arXiv:1911.11250 [pdf, other] cs.LG cs.CV eess.IV stat.ML
doi
10.1109/ETFA.2019.8869311
A Novel Visual Fault Detection and Classification System for Semiconductor Manufacturing Using Stacked Hybrid Convolutional Neural Networks

Authors: Tobias Schlosser, Frederik Beuth, Michael Friedrich, Danny Kowerko

Abstract: Automated visual inspection in the semiconductor industry aims to detect and classify manufacturing defects utilizing modern image processing techniques. While an earliest possible detection of defect patterns allows quality control and automation of manufacturing chains, manufacturers benefit from an increased yield and reduced manufacturing costs. Since classical image processing systems are lim… ▽ More
Submitted 26 January, 2020; v1 submitted 25 November, 2019; originally announced November 2019.

Comments: Accepted for: 2019 IEEE 24th International Conference on Emerging Technologies and Factory Automation (ETFA)

arXiv:1911.11225 [pdf] eess.SY
Development of On Board Computer for a Nanosatellite

Authors: Saurabh Raje, Abhishek Goel, Shubham Sharma, Kushagra Aggarwal, Dhananjay Mantri, Tanuj Kumar

Abstract: …However, hardware interrupts are implemented on selected peripherals which ensure an asynchronous switching to the Emergency States for safety. A review of some common fault detection, isolation and removal methods used shall conclude the paper. ▽ More
Submitted 17 March, 2020; v1 submitted 25 November, 2019; originally announced November 2019.

Comments: Conference paper with 6 pages, 3 figures. Corrected author's name in V2

arXiv:1911.11113 [pdf] eess.SY math.DS
Analytical solution to swing equations in power grids

Authors: HyungSeon Oh

Abstract: …system, which is different from conventional approaches that describe the equation in the polar coordinate system. Based on the properties and operational conditions of electric power grids referred to in the literature, we identified the swing equation in the Cartesian coordinate system and derived an analytical solution within a validity region. Results: T… ▽ More
Submitted 25 November, 2019; originally announced November 2019.

Comments: Corrected version of the published paper at PLoS ONE

Journal ref: published, 2019

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関連資料

' @kazuo_reve 私が効果を確認した「小川メソッド」
https://qiita.com/kazuo_reve/items/a3ea1d9171deeccc04da

' @kazuo_reve 新人の方によく展開している有益な情報
https://qiita.com/kazuo_reve/items/d1a3f0ee48e24bba38f1

' @kazuo_reve Vモデルについて勘違いしていたと思ったこと
https://qiita.com/kazuo_reve/items/46fddb094563bd9b2e1e

自己記事一覧

Qiitaで逆リンクを表示しなくなったような気がする。時々、スマフォで表示するとあらわっることがあり、完全に削除したのではなさそう。

４月以降、せっせとリンクリストを作り、統計を取って確率を説明しようとしている。
2025年２月末を目標にしている。

Qiitaの記事に３段階または５段階で到達するための方法
https://qiita.com/kaizen_nagoya/items/6e9298296852325adc5e

プログラマが知っていると良い「公序良俗」
https://qiita.com/kaizen_nagoya/items/9fe7c0dfac2fbd77a945

逆も真：社会人が最初に確かめるとよいこと。OSEK(69)、Ethernet(59)
https://qiita.com/kaizen_nagoya/items/39afe4a728a31b903ddc

「何を」よりも「誰を」。１０年後のために今見習いたい人たち
https://qiita.com/kaizen_nagoya/items/8045978b16eb49d572b2

物理記事　上位100
https://qiita.com/kaizen_nagoya/items/66e90fe31fbe3facc6ff

量子(0) 計算機, 量子力学
https://qiita.com/kaizen_nagoya/items/1cd954cb0eed92879fd4

数学関連記事１００
https://qiita.com/kaizen_nagoya/items/d8dadb49a6397e854c6d

図(0) state, sequence and timing. UML and お絵描き
https://qiita.com/kaizen_nagoya/items/60440a882146aeee9e8f

品質一覧
https://qiita.com/kaizen_nagoya/items/2b99b8e9db6d94b2e971

言語・文学記事　１００
https://qiita.com/kaizen_nagoya/items/42d58d5ef7fb53c407d6

医工連携関連記事一覧
https://qiita.com/kaizen_nagoya/items/6ab51c12ba51bc260a82

自動車　記事　１００
https://qiita.com/kaizen_nagoya/items/f7f0b9ab36569ad409c5

通信記事１００
https://qiita.com/kaizen_nagoya/items/1d67de5e1cd207b05ef7

日本語（０）一欄
https://qiita.com/kaizen_nagoya/items/7498dcfa3a9ba7fd1e68

英語(0) 一覧
https://qiita.com/kaizen_nagoya/items/680e3f5cbf9430486c7d

転職(0)一覧
https://qiita.com/kaizen_nagoya/items/f77520d378d33451d6fe

仮説（0）一覧（目標100現在40）
https://qiita.com/kaizen_nagoya/items/f000506fe1837b3590df

音楽　一覧(0)
https://qiita.com/kaizen_nagoya/items/b6e5f42bbfe3bbe40f5d

「@kazuo_reve 新人の方によく展開している有益な情報」確認一覧
https://qiita.com/kaizen_nagoya/items/b9380888d1e5a042646b

Qiita(0)Qiita関連記事一覧（自分）
https://qiita.com/kaizen_nagoya/items/58db5fbf036b28e9dfa6

鉄道（０）鉄道のシステム考察はてっちゃんがてつだってくれる
https://qiita.com/kaizen_nagoya/items/26bda595f341a27901a0

安全（0）安全工学シンポジウムに向けて: 21
https://qiita.com/kaizen_nagoya/items/c5d78f3def8195cb2409

一覧の一覧( The directory of directories of mine.) Qiita(100)
https://qiita.com/kaizen_nagoya/items/7eb0e006543886138f39

Ethernet 記事一覧　Ethernet(0)
https://qiita.com/kaizen_nagoya/items/88d35e99f74aefc98794

Wireshark 一覧 wireshark(0)、Ethernet(48)
https://qiita.com/kaizen_nagoya/items/fbed841f61875c4731d0

線網（Wi-Fi）空中線(antenna)(0) 記事一覧(118/300目標)
https://qiita.com/kaizen_nagoya/items/5e5464ac2b24bd4cd001

OSEK OS設計の基礎　OSEK(100)
https://qiita.com/kaizen_nagoya/items/7528a22a14242d2d58a3

Error一覧 error(0)
https://qiita.com/kaizen_nagoya/items/48b6cbc8d68eae2c42b8

C++ Support(0)　
https://qiita.com/kaizen_nagoya/items/8720d26f762369a80514

Coding(0) Rules, C, Secure, MISRA and so on
https://qiita.com/kaizen_nagoya/items/400725644a8a0e90fbb0

coding (101) 一覧を作成し始めた。omake:最近のQiitaで表示しない5つの事象
https://qiita.com/kaizen_nagoya/items/20667f09f19598aedb68

プログラマによる、プログラマのための、統計(0)と確率のプログラミングとその後
https://qiita.com/kaizen_nagoya/items/6e9897eb641268766909

なぜdockerで機械学習するか書籍・ソース一覧作成中 (目標100)
https://qiita.com/kaizen_nagoya/items/ddd12477544bf5ba85e2

言語処理100本ノックをdockerで。python覚えるのに最適。:10+12
https://qiita.com/kaizen_nagoya/items/7e7eb7c543e0c18438c4

プログラムちょい替え（0）一覧:4件
https://qiita.com/kaizen_nagoya/items/296d87ef4bfd516bc394

Python(0)記事をまとめたい。
https://qiita.com/kaizen_nagoya/items/088c57d70ab6904ebb53

官公庁・学校・公的団体（NPOを含む）システムの課題、官（０）
https://qiita.com/kaizen_nagoya/items/04ee6eaf7ec13d3af4c3

「はじめての」シリーズ　ベクタージャパン　
https://qiita.com/kaizen_nagoya/items/2e41634f6e21a3cf74eb

AUTOSAR(0)Qiita記事一覧, OSEK(75)
https://qiita.com/kaizen_nagoya/items/89c07961b59a8754c869

プログラマが知っていると良い「公序良俗」
https://qiita.com/kaizen_nagoya/items/9fe7c0dfac2fbd77a945

LaTeX(0) 一覧　
https://qiita.com/kaizen_nagoya/items/e3f7dafacab58c499792

自動制御、制御工学一覧（０）
https://qiita.com/kaizen_nagoya/items/7767a4e19a6ae1479e6b

Rust(0) 一覧　
https://qiita.com/kaizen_nagoya/items/5e8bb080ba6ca0281927

100以上いいねをいただいた記事16選
https://qiita.com/kaizen_nagoya/items/f8d958d9084ffbd15d2a

小川清最終講義、最終講義（再）計画, Ethernet(100) 英語(100) 安全(100)
https://qiita.com/kaizen_nagoya/items/e2df642e3951e35e6a53

参考資料

物理記事　上位100
https://qiita.com/kaizen_nagoya/items/66e90fe31fbe3facc6ff

数学関連記事１００
https://qiita.com/kaizen_nagoya/items/d8dadb49a6397e854c6d

言語・文学記事　１００
https://qiita.com/kaizen_nagoya/items/42d58d5ef7fb53c407d6

医工連携関連記事一覧
https://qiita.com/kaizen_nagoya/items/6ab51c12ba51bc260a82

通信記事１００
https://qiita.com/kaizen_nagoya/items/1d67de5e1cd207b05ef7

自動車　記事　１００
https://qiita.com/kaizen_nagoya/items/f7f0b9ab36569ad409c5

OSEK 記事で views 100,000を目指して　OSEK(8)
https://qiita.com/kaizen_nagoya/items/ff45ee55566eeff5f62e

無線網（Wi-Fi）空中線(antenna)(0) 記事https://qiita.com/kaizen_nagoya/items/5e5464ac2b24bd4cd001

なぜdockerで機械学習するか書籍・ソース一覧作成中
https://qiita.com/kaizen_nagoya/items/ddd12477544bf5ba85e2

仮説（0）一覧
https://qiita.com/kaizen_nagoya/items/f000506fe1837b3590df

安全（0）安全工学シンポジウムに向けて: 21
https://qiita.com/kaizen_nagoya/items/c5d78f3def8195cb2409

日本語（０）一欄
https://qiita.com/kaizen_nagoya/items/7498dcfa3a9ba7fd1e68

英語(0) 一覧
https://qiita.com/kaizen_nagoya/items/680e3f5cbf9430486c7d

転職(0)一覧
https://qiita.com/kaizen_nagoya/items/f77520d378d33451d6fe

一覧の一覧( The directory of directories of mine.) Qiita(100)
https://qiita.com/kaizen_nagoya/items/7eb0e006543886138f39

プログラマが知っていると良い「公序良俗」
https://qiita.com/kaizen_nagoya/items/9fe7c0dfac2fbd77a945

LaTeX(0) 一覧　
https://qiita.com/kaizen_nagoya/items/e3f7dafacab58c499792

自動制御、制御工学一覧（０）
https://qiita.com/kaizen_nagoya/items/7767a4e19a6ae1479e6b

Rust(0) 一覧　
https://qiita.com/kaizen_nagoya/items/5e8bb080ba6ca0281927

小川清最終講義、小川清最終講義（再）計画, Ethernet(100) 英語(100) 安全(100)
https://qiita.com/kaizen_nagoya/items/e2df642e3951e35e6a53

＜この記事は個人の過去の経験に基づく個人の感想です。現在所属する組織、業務とは関係がありません。＞
This article is an individual impression based on the individual's experience. It has nothing to do with the organization or business to which I currently belong.