scholarly journals Adaptive Fault Detection with Two Time-Varying Control Limits for Nonlinear and Multimodal Processes

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Jinna Li ◽  
Yuan Li ◽  
Yanhong Xie ◽  
Xuejun Zong
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Nearest Neighbors ◽  
Training Data ◽  
Time Varying ◽  
Training Samples ◽  
Mean And Variance ◽  
The Mean ◽  
Control Limits

A novel fault detection method is proposed for detection process with nonlinearity and multimodal batches. Calculating the Mahalanobis distance of samples, the data with the similar characteristics are replaced by the mean of them; thus, the number of training data is reduced easily. Moreover, the super ball regions of mean and variance of training data are presented, which not only retains the statistical properties of original training data but also avoids the reduction of data unlimitedly. To accurately identify faults, two control limits are determined during investigating the distributions of distances and angles between training samples to their nearest neighboring samples in the reduced database; thus, the traditionalk-nearest neighbors (only considering distances) fault detection (FD-kNN) method is developed. Another feature of the proposed detection method is that the control limits vary with updating database such that an adaptive fault detection technique is obtained. Finally, numerical examples and case study are given to illustrate the effectiveness and advantages of the proposed method.

scholarly journals Application of Residual-Based EWMA Control Charts for Detecting Faults in Variable-Air-Volume Air Handling Unit System

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Haitao Wang
Keyword(s):  
Time Series ◽  
Fault Detection ◽  
Control Chart ◽  
Detection Method ◽  
Time Data ◽  
Air Handling Unit ◽  
Ewma Control Chart ◽  
Robust Fault Detection ◽  
Modeling Errors ◽  
Control Limits

An online robust fault detection method is presented in this paper for VAV air handling unit and its implementation. Residual-based EWMA control chart is used to monitor the control processes of air handling unit and detect faults of air handling unit. In order to provide a level of robustness with respect to modeling errors, control limits are determined by incorporating time series model uncertainty in EWMA control chart. The fault detection method proposed was tested and validated using real time data collected from real VAV air-conditioning systems involving multiple artificial faults. The results of validation show residual-based EWMA control chart with designing control limits can improve the accuracy of fault detection through eliminating the negative effects of dynamic characteristics, serial correlation, normal transient changes of system, and time series modeling errors. The robust fault detection method proposed can provide an effective tool for detecting the faults of air handling units.

Robust Design Through the Use of a Hybrid Genetic Algorithm

2002 ◽  
Author(s):  
Kurt Hacker ◽  
Kemper Lewis
Keyword(s):  
Robust Design ◽  
Hybrid Genetic Algorithm ◽  
Design Parameters ◽  
Optimization Approach ◽  
Engineering Systems ◽  
Local Optima ◽  
Good Design ◽  
Mean And Variance ◽  
The Mean

In this paper we present a hybrid optimization approach to perform robust design. The motivation for this work is the fact that many realistic engineering systems are mutimodal in nature with multiple local optima, and moreover may have one or more uncertain design parameters. The approach that is presented utilizes both local and global optimization algorithms to find good design points more efficiently than either could alone. The mean and variance of the objective function at a design point is calculated using Monte Carlo simulation and is used to drive the optimization process. To demonstrate the usefulness of this approach a case study is considered involving the design of a beam with dimensional uncertainty.

MSRD: Multimodal Web Rumor Detection Method

2020 ◽  
Author(s):  
Jinshuo Liu ◽  
Kuo Feng ◽  
Jeff Z Pan ◽  
Juan Deng ◽  
Lina Wang
Keyword(s):  
Short Term Memory ◽  
Detection Method ◽  
Image Features ◽  
Social Stability ◽  
Image Content ◽  
Mean And Variance ◽  
Memory Network ◽  
The Mean ◽  
Rumor Detection ◽  
Text Content

Abstract Multimodal web rumors, which combine images and text, are confusing and can be inflammatory, and therefore can be harmful to national security and social stability. Currently, web rumor detection fully considers text content but ignores image content, including text embedded in images. This paper proposes a multimodal web rumor detection method based on a deep neural network considering images, image-embedded text, and text content. This method uses a VGG-19 network to extract image content features, DenseNet to extract embedded text content, and an LSTM (Long Short-term Memory) network to extract text content features. After concatenation with image features, the mean and variance vectors of the image and text shared representations are obtained through a completely connected layer, and random variables sampled from a Gaussian distribution are used to form a reparameterized multimodal feature as the input of the rumor detector. Experiments show that the accuracy of this method is 68.5% and 79.4% on Twitter and Weibo, respectively.

scholarly journals An Arc Fault Detection Method Based on Multidictionary Learning

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Na Qu ◽  
Jianhui Wang ◽  
Jinhai Liu ◽  
Zhi Wang
Keyword(s):  
Fault Detection ◽  
Sparse Representation ◽  
Dictionary Learning ◽  
Detection Method ◽  
Amplitude Spectrum ◽  
Learning Method ◽  
Test Results ◽  
Training Samples ◽  
Simulation Results ◽  
Normal Work

This paper uses the dictionary learning of sparse representation algorithm to detect the arc fault. Six kinds of characteristics, that is, the normalized amplitudes of 0Hz, 50Hz, 100Hz, 150Hz, 200Hz, and 250Hz in the current amplitude spectrum, are used as inputs. The output is normal work or arc fault. Increasing the number of training samples can improve the accuracy of the tests. But if the training samples are too many, it is difficult to be expressed by single dictionary. This paper designs a multidictionary learning method to solve the problem. Firstly, n training samples are selected to form s overcomplete dictionaries. Then a dictionary library consisting of s dictionaries is constructed. Secondly, t (t≤s) dictionaries are randomly selected from the dictionary library to judge the test results, respectively. Finally, the final detest result is obtained through the maximum number of votes, that is, the modality with the most votes is the detest result. Simulation results show that the accuracy of detection can be improved.

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.

scholarly journals Best Practices for Postprocessing Ensemble Climate Forecasts. Part I: Selecting Appropriate Recalibration Methods

2016 ◽  
Vol 29 (20) ◽  
pp. 7247-7264 ◽  
Author(s):  
Philip G. Sansom ◽  
Christopher A. T. Ferro ◽  
David B. Stephenson ◽  
Lisa Goddard ◽  
Simon J. Mason
Keyword(s):  
Linear Time ◽  
Training Data ◽  
Training Period ◽  
Limited Data ◽  
Near Surface ◽  
Climate Forecasts ◽  
Mean And Variance ◽  
Overall Performance ◽  
The Mean ◽  
And Training

Abstract This study describes a systematic approach to selecting optimal statistical recalibration methods and hindcast designs for producing reliable probability forecasts on seasonal-to-decadal time scales. A new recalibration method is introduced that includes adjustments for both unconditional and conditional biases in the mean and variance of the forecast distribution and linear time-dependent bias in the mean. The complexity of the recalibration can be systematically varied by restricting the parameters. Simple recalibration methods may outperform more complex ones given limited training data. A new cross-validation methodology is proposed that allows the comparison of multiple recalibration methods and varying training periods using limited data. Part I considers the effect on forecast skill of varying the recalibration complexity and training period length. The interaction between these factors is analyzed for gridbox forecasts of annual mean near-surface temperature from the CanCM4 model. Recalibration methods that include conditional adjustment of the ensemble mean outperform simple bias correction by issuing climatological forecasts where the model has limited skill. Trend-adjusted forecasts outperform forecasts without trend adjustment at almost 75% of grid boxes. The optimal training period is around 30 yr for trend-adjusted forecasts and around 15 yr otherwise. The optimal training period is strongly related to the length of the optimal climatology. Longer training periods may increase overall performance but at the expense of very poor forecasts where skill is limited.

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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