Fault detection method based on principal component difference associated with DPCA

2018 ◽  
Vol 33 (1) ◽  
pp. e3082 ◽  
Author(s):  
Cheng Zhang ◽  
Qingxiu Guo ◽  
Yuan Li
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Principal Component

A Principal Component Analysis Based Fault Detection Method in Etch Process of Semiconductor Manufacturing

2012 ◽  
Vol 522 ◽  
pp. 793-798 ◽  
Author(s):  
Jun Gang Yang ◽  
Jie Zhang ◽  
Jian Xiong Yang ◽  
Ying Huang
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Semiconductor Manufacturing ◽  
Detection Method ◽  
Main Idea ◽  
Simple Algorithm ◽  
Principal Component ◽  
Component Analysis ◽  
Training Data ◽  
Detection Model

A Principal Component Analysis based Fault Detection method is proposed here to detect faults in etch process of semiconductor manufacturing. The main idea of this method is to calculate the loading vector and build the fault detection model according to training data. Using this model, the main information of fault data can be obtained immediately and easily. Also the principal component subspace and residual subspace can be constructed. Then, faults are detected by calculating Squared Prediction Error. Finally, an industrial example of Lam 9600 TCP metal etcher at Texas Instruments is used to demonstrate the performance of the proposed PCA-based method in fault detection, and the results show that it has such advantages as simple algorithm and low time cost, thus especially adapts to the real time fault detection of semiconductor manufacturing.

Using Weather and Schedule based Pattern Matching and Feature based PCA for Whole Building Fault Detection — Part II Field Evaluation

2021 ◽  
pp. 1-16
Author(s):  
Yimin Chen ◽  
Jin Wen ◽  
L. James Lo
Keyword(s):  
Fault Detection ◽  
Pattern Matching ◽  
Detection Method ◽  
Principal Component ◽  
Field Evaluation ◽  
Interval Data ◽  
Automation System ◽  
Hvac System ◽  
High Detection Rate ◽  
Feature Based

Abstract In a heating, ventilation and air conditioning (HVAC) system, a whole building fault (WBF) refers to a fault that occurs in one component but may trigger additional faults/abnormalities on different components or subsystems resulting in impacts on the energy consumption or indoor air quality in buildings. At the whole building level, interval data collected from various components/subsystems can be employed to detect WBFs. In the Part I of this study, a novel data-driven method which includes weather and schedule-based Pattern Matching (WPM) procedure and a feature based principal component analysis PCA (FPCA) procedure was developed to detect the WBF. This article is the second of a two-part study of the development of the whole building fault detection method. In the Part II of the study (this paper), various WBFs were designed and imposed in the HVAC system of a campus building. Data from both imposed fault and naturally-occurred faults were collected through the Building Automation System to evaluate the developed fault detection method. Evaluation results show that the developed WPM-FPCA method reaches a high detection rate and a low false alarm rate.

Fault Detection for Batch Processes Based on Segmentation MPCA

2014 ◽  
Vol 1030-1032 ◽  
pp. 1701-1708
Author(s):  
Ning Lv ◽  
Guang Yuan Bai ◽  
Lei Lei Jiang
Keyword(s):  
Fault Detection ◽  
Fermentation Process ◽  
Detection Method ◽  
Piecewise Linear ◽  
Nonlinear Modeling ◽  
Principal Component ◽  
Batch Processes ◽  
Piecewise Linear Approximation ◽  
Process Data ◽  
Speed And Accuracy

Aiming at the dynamic characteristic of batch production process changes fast and the accurate modeling of it is difficult, so this paper proposes an intermittent fault detection method of the principal component analysis based on process segment. According to the different dynamic characteristics of process data, the process is divided into multiple stages, with the method of piecewise linear approximation of nonlinear modeling to model different stages of the process, in order to make up the deficiency of traditional MPCA fault diagnosis methods. Through the fault detection of the beer fermentation process experiments to verify that the method can detect process faults promptly and improve the speed and accuracy of process monitoring.

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

2020 ◽  
pp. 129-139
Author(s):  
Weihai Sun ◽  
Lemei Han
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Detection Method ◽  
Sound Signal ◽  
Practical Significance ◽  
Time Frequency ◽  
Detection Approach ◽  
Machine Fault ◽  
Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

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