Parity space-based fault isolation using minimum error minimax probability machine

2020 ◽  
Vol 95 ◽  
pp. 104242
Author(s):  
Yang Song ◽  
Maiying Zhong ◽  
Ting Xue ◽  
Steven X. Ding ◽  
Wenbo Li
Keyword(s):  
Fault Isolation ◽  
Minimum Error ◽  
Parity Space ◽  
Minimax Probability Machine

scholarly journals Parity Space-Based Fault Detection by Minimum Error Minimax Probability Machine

2018 ◽  
Vol 51 (24) ◽  
pp. 1292-1297 ◽  
Author(s):  
Maiying Zhong ◽  
Yang Song ◽  
Ting Xue ◽  
Rui Yang ◽  
Wenbo Li
Keyword(s):  
Fault Detection ◽  
Minimum Error ◽  
Parity Space ◽  
Minimax Probability Machine

FEATURE SELECTION BASED ON MINIMUM ERROR MINIMAX PROBABILITY MACHINE

2007 ◽  
Vol 21 (08) ◽  
pp. 1279-1292 ◽  
Author(s):  
ZENGLIN XU ◽  
IRWIN KING ◽  
MICHAEL R. LYU
Keyword(s):  
Feature Selection ◽  
Prediction Accuracy ◽  
Support Vector ◽  
Feature Subset ◽  
Minimum Error ◽  
Automatic Balance ◽  
Classification Framework ◽  
Data Partitions ◽  
Minimax Probability Machine ◽  
Optimal Feature Subset

Feature selection is an important task in pattern recognition. Support Vector Machine (SVM) and Minimax Probability Machine (MPM) have been successfully used as the classification framework for feature selection. However, these paradigms cannot automatically control the balance between prediction accuracy and the number of selected features. In addition, the selected feature subsets are also not stable in different data partitions. Minimum Error Minimax Probability Machine (MEMPM) has been proposed for classification recently. In this paper, we outline MEMPM to select the optimal feature subset with good stability and automatic balance between prediction accuracy and the size of feature subset. The experiments against feature selection with SVM and MPM show the advantages of the proposed MEMPM formulation in stability and automatic balance between the feature subset size and the prediction accuracy.

scholarly journals Мінімаксний підхід в машинному навчанні: сучасний стан та перспективи розвитку.

2021 ◽  
pp. 164-171
Author(s):  
В. Марценюк ◽  
І. Андрущак ◽  
Н. Мілян
Keyword(s):  
Minimum Error ◽  
Minimax Probability Machine

В статті представлено з.начення машинного навчання у сучасному світі. Звернуто особливу увагу на використання алгоритмів машинного навчання в медицині, зокрема використання різноманітних моделей, починаючи від регресії, SVM, випадкових лісів для контрольованого навчання та PCA для неконтрольованого. Підкреслюються основні невизначеності та завдання машинного навчання, що виникають у основних медичних додатках (діагностика, лікування та профілактика). Математично описано проблеми машинного навчання в медичних дослідженнях. Оптимізація є важливою частиною машинного навчання. Основна увага приділена мінімаксному підходу у машинному навчанні. Розглянуто ряд мінімаксних підходів таких, як: Minimax Probability Machine (MPM), Generalized Hidden-Mapping Minimax Probability Machine (GHM-MPM), Minimum Error Minimax Probability Machine (MEMPM), парна мінімаксна ймовірність екстремального нахилу машини (TMPELM), машина подвійної мінімаксної ймовірності (TWMPM) та деякі інші.

Dimension Reduction by Minimum Error Minimax Probability Machine

2017 ◽  
Vol 47 (1) ◽  
pp. 58-69 ◽  
Author(s):  
Shiji Song ◽  
Yanshang Gong ◽  
Yuli Zhang ◽  
Gao Huang ◽  
Guang-Bin Huang
Keyword(s):  
Dimension Reduction ◽  
Minimum Error ◽  
Minimax Probability Machine

Multiple faults detection and isolation approach applied to a kraft recovery boiler

TAPPI Journal ◽  
2014 ◽  
Vol 13 (1) ◽  
pp. 33-41
Author(s):  
YVON THARRAULT ◽  
MOULOUD AMAZOUZ
Keyword(s):  
Fault Detection ◽  
Early Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Principal Component ◽  
Fault Isolation ◽  
Fault Detection And Isolation ◽  
Detection Scheme ◽  
Multiple Faults ◽  
Recovery Boiler

Recovery boilers play a key role in chemical pulp mills. Early detection of defects, such as water leaks, in a recovery boiler is critical to the prevention of explosions, which can occur when water reaches the molten smelt bed of the boiler. Early detection is difficult to achieve because of the complexity and the multitude of recovery boiler operating parameters. Multiple faults can occur in multiple components of the boiler simultaneously, and an efficient and robust fault isolation method is needed. In this paper, we present a new fault detection and isolation scheme for multiple faults. The proposed approach is based on principal component analysis (PCA), a popular fault detection technique. For fault detection, the Mahalanobis distance with an exponentially weighted moving average filter to reduce the false alarm rate is used. This filter is used to adapt the sensitivity of the fault detection scheme versus false alarm rate. For fault isolation, the reconstruction-based contribution is used. To avoid a combinatorial excess of faulty scenarios related to multiple faults, an iterative approach is used. This new method was validated using real data from a pulp and paper mill in Canada. The results demonstrate that the proposed method can effectively detect sensor faults and water leakage.

Advanced T-LSIM System Detections using Amplified External Isolated Source-Sense Unit

2018 ◽  
Author(s):  
Zhi Jie Lau ◽  
Chris Philips
Keyword(s):  
Fault Isolation ◽  
Isolation Technique ◽  
Laser Signal ◽  
Signal Injection ◽  
Sense Unit ◽  
Types Of Faults

Abstract Thermal-Laser Signal Injection Microscopy (T-LSIM) is a widely used fault isolation technique. Although there are several T-LSIM systems on the market, each is limited in terms of the voltage and current it can produce. In this paper, the authors explain how they incorporated an Amplified External Isolated Source-Sense (AxISS) unit into their T-LSIM platform, increasing its current sourcing capability and voltage biasing range. They also provide examples highlighting the types of faults and failures that the modified system can detect.

Advanced Non-Destructive Fault Isolation Techniques for PCB Substrates Using Magnetic Current Imaging and Terahertz Time Domain Reflectometry

2018 ◽  
Author(s):  
Daechul Choi ◽  
Yoonseong Kim ◽  
Jongyun Kim ◽  
Han Kim
Keyword(s):  
Time Domain ◽  
Quantum Interference ◽  
Flip Chip ◽  
Imaging System ◽  
Time Domain Reflectometry ◽  
Fault Isolation ◽  
Magnetic Current ◽  
Isolation Techniques ◽  
Super Conducting ◽  
Non Destructive

Abstract In this paper, we demonstrate cases for actual short and open failures in FCB (Flip Chip Bonding) substrates by using novel non-destructive techniques, known as SSM (Scanning Super-conducting Quantum Interference Device Microscopy) and Terahertz TDR (Time Domain Reflectometry) which is able to pinpoint failure locations. In addition, the defect location and accuracy is verified by a NIR (Near Infra-red) imaging system which is also one of the commonly used non-destructive failure analysis tools, and good agreement was made.

Fault Isolation of MOL and FEOL Buried Defects Using Conductive Atomic Force Microscopy as a Complement to Passive Voltage Contrast Imaging

2017 ◽  
Author(s):  
Lucile C. Teague Sheridan ◽  
Linda Conohan ◽  
Chong Khiam Oh
Keyword(s):  
Atomic Force Microscopy ◽  
Cmos Technology ◽  
Fault Isolation ◽  
Contrast Imaging ◽  
Conductive Atomic Force Microscopy ◽  
Isolation Technique ◽  
Conductive Afm ◽  
Force Microscopy ◽  
Atomic Force ◽  
Voltage Contrast

Abstract Atomic force microscopy (AFM) methods have provided a wealth of knowledge into the topographic, electrical, mechanical, magnetic, and electrochemical properties of surfaces and materials at the micro- and nanoscale over the last several decades. More specifically, the application of conductive AFM (CAFM) techniques for failure analysis can provide a simultaneous view of the conductivity and topographic properties of the patterned features. As CMOS technology progresses to smaller and smaller devices, the benefits of CAFM techniques have become apparent [1-3]. Herein, we review several cases in which CAFM has been utilized as a fault-isolation technique to detect middle of line (MOL) and front end of line (FEOL) buried defects in 20nm technologies and beyond.

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