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.

Dictionary learning method for joint sparse representation-based image fusion

2013 ◽  
Vol 52 (5) ◽  
pp. 057006 ◽  
Author(s):  
Qiheng Zhang ◽  
Yuli Fu ◽  
Haifeng Li ◽  
Jian Zou
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Dictionary Learning ◽  
Learning Method ◽  
Joint Sparse Representation

A Fast Gradual Fault Detection Method for Underwater Integrated Navigation Systems

2015 ◽  
Vol 69 (1) ◽  
pp. 93-112 ◽  
Author(s):  
Liu Yi-ting ◽  
Xu Xiao-su ◽  
Liu Xi-xiang ◽  
Zhang Tao ◽  
Li Yao ◽  
...  
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
System Failure ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Detection Program ◽  
Absolute Residual ◽  
Simulation Results ◽  
Short Time

Gradual fault detection is always an important issue in integrated navigation systems, and the gradual fault is the most difficult fault to detect. To detect gradual faults in a timely and precise manner in integrated navigation systems, the statistical concepts of the normalised residual mean and the sum of absolute residuals are introduced according to the characteristics of gradual system failure in this paper. The applicability of the improved residual χ2 detection method is discussed. Then, the gradual fault detection program based on the improved residual χ2 detection method is designed with the criterion of normalised residual mean and the sum of absolute residual. The simulation results and vehicle tests show that: 1) The residual of the failed sub-system can be calculated accurately with the improved residual χ2 detection method, which has strong applicability in gradual fault detection; 2) The gradual fault can be detected in a short time by using the normalised residual mean and the sum of absolute residual.

scholarly journals Research on Fault Detection Method of FADS System

2020 ◽  
Vol 38 (6) ◽  
pp. 1210-1217
Author(s):  
Qianlei Jia ◽  
Weiguo Zhang ◽  
Jingping Shi ◽  
Guangwen Li ◽  
Xiaoxiong Liu
Keyword(s):  
Fault Detection ◽  
High Precision ◽  
Detection Method ◽  
Measurement Noise ◽  
Distribution Characteristics ◽  
Detection Problem ◽  
Chi Square ◽  
Statistical Knowledge ◽  
Noise Simulation ◽  
Simulation Results

In order to solve the fault detection problem of flush air data sensing (FADS), an advanced airborne sensor, a new method is proposed in this paper. First, the high-precision FADS model is established on the basis of the database obtained from the CFD software and aerodynamics knowledge. Then, the distribution characteristics of each group of signals under fault condition are derived through strict formulas. Meanwhile, the threshold of alarm times is designed with statistical knowledge. For verifying the effectiveness of the newly proposed method, a comparison with other two widely adopted methods, including the methods based on parity equation and Chi-square χ2 distribution, is conducted under different measurement noise. Simulation results show that the proposed fault detection method for FADS possess higher accuracy and stronger anti-interference.

An enhanced fault-detection method based on adaptive spectral decomposition and super-resolution deep learning

2019 ◽  
Vol 7 (3) ◽  
pp. T713-T725
Author(s):  
Zhenyu Yuan ◽  
Handong Huang ◽  
Yuxin Jiang ◽  
Jinbiao Tang ◽  
Jingjing Li
Keyword(s):  
Deep Learning ◽  
Fault Detection ◽  
Spectral Decomposition ◽  
Detection Method ◽  
Eastern China ◽  
Amplitude Spectrum ◽  
Super Resolution ◽  
Dominant Frequency ◽  
Seismic Survey ◽  
Amplitude Data

Coherence is widely used for detecting faults in reservoir characterization. However, faults detected from coherence may be contaminated by some other discontinuities (e.g., noise and stratigraphic features) that are unrelated to faults. To further improve the accuracy and efficiency of coherence, preprocessing or postprocessing techniques are required. We developed an enhanced fault-detection method with adaptive scale highlighting and high resolution, by combining adaptive spectral decomposition and super-resolution (SR) deep learning into coherence calculation. As a preprocessing technique, adaptive spectral decomposition is first proposed and applied on seismic data to get a dominant-frequency-optimized amplitude spectrum, which has features of scale focus and multiple resolution. Eigenstructure-based coherence with dip correction is then calculated to delineate fault discontinuities. Following the remarkable success of SR deep learning in image reconstruction, a convolutional neural network (CNN) model is built and it then takes fault-detection images as the input to achieve enhanced results. The effectiveness of our proposed method is validated on a seismic survey acquired from Eastern China. Examples demonstrate that coherence from adaptive amplitude spectrum without dip correction is comparable to the dip-corrected one from seismic amplitude data at a certain degree, and they even highlight the specific scale of fault targets. Comparing fault detections from adaptive spectrum and some specific-frequency components, it can be concluded that adaptive spectral-based coherence highlights the primary scale of faults at various depths with only one single volume of data, thus improving the interpretation efficiency and reducing storage cost. Furthermore, with the trained CNN model, the resolution and signal-to-noise ratio of coherence images are effectively improved and the continuity of detected fault is promisingly enhanced.

scholarly journals A High-Speed Fault Detection, Identification, and Isolation Method for a Last Mile Radial LVDC Distribution Network

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 2901 ◽  
Author(s):  
Saeed Jamali ◽  
Syed Bukhari ◽  
Muhammad Khan ◽  
Khawaja Mehmood ◽  
Muhammad Mehdi ◽  
...  
Keyword(s):  
Fault Detection ◽  
High Speed ◽  
Power Flow ◽  
Detection Method ◽  
Distribution Network ◽  
Test System ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Simulation Results ◽  
Vector Sum

The day-by-day increase in digital loads draws attention towards the need for an efficient and compatible distribution network. An LVDC distribution network has the capability to fulfill such digital load demands. However, the major challenge of an LVDC distribution network is its vulnerability during a fault. The need for a high-speed fault detection method is inevitable before it can be widely adopted. This paper proposes a new fault detection method which extracts the features of the current during a fault. The proposed fault detection method uses the merits of overcurrent, the first and second derivative of current, and signal processing techniques. Three different features are extracted from a time domain current signal through a sliding window. The extracted features are based upon the root squared zero, second, and fourth order moments. The features are then set with individual thresholds to discriminate low-, high-, and very high-resistance faults. Furthermore, a fault is located through the superimposed power flow. Moreover, this study proposes a new method based on the vector sum of positive and negative pole currents to identify the faulty pole. The proposed scheme is verified by using a modified IEEE 13 node distribution network, which is implemented in Matlab/Simulink. The simulation results confirm the effectiveness of the proposed fault detection and identification method. The simulation results also confirm that a fault having a resistance of 1 m Ω is detected and interrupted within 250 μ s for the test system used in this study.

Application of Vibration Analysis in Ball Bearing Fault Detection

2005 ◽  
Author(s):  
F. Bakhtiary-Nejad ◽  
A. H. Nayeb ◽  
S. E. Yeganeh
Keyword(s):  
Fault Detection ◽  
Computer Program ◽  
Time Domain ◽  
Vibration Analysis ◽  
Ball Bearing ◽  
Detection Method ◽  
Ball Bearings ◽  
Test Results ◽  
Localized Defects ◽  
Set Up

In this paper, existence of localized defects in a ball bearing has been diagnosed using vibration analysis. First, different kinds of faults which occur in ball bearings have been investigated. Then an analytical model has been proposed for determining the damaged ball bearing vibrations due to a localized defect. Also various methods of fault detection have been evaluated. Next, in order to examine the ball bearings, a testing set-up has been designed and constructed. Then by preparing a computer program, which calculates defect frequencies, some ball bearings have been tested. The test results were originally derived in time-domain. Then by using vibration analysis of healthy and damaged ball bearings in frequency-domain, a fault detection method for ball bearings has been proposed.

A NOVEL SUPERVISED STRUCTURE DICTIONARY LEARNING FOR CLASSIFICATION BASED ON SPARSE REPRESENTATION

2012 ◽  
Vol 26 (07) ◽  
pp. 1255012 ◽  
Author(s):  
XIN TANG ◽  
PATRICK S. WANG ◽  
GUOCAN FENG
Keyword(s):  
Sparse Representation ◽  
Dictionary Learning ◽  
Sparse Coding ◽  
Optimization Method ◽  
Good Representation ◽  
Training Samples ◽  
Label Information ◽  
New Formulation ◽  
Multiclass Classifier ◽  
Structured Dictionary

Sparse representation based classification has led to interesting image recognition results, while the dictionary used for sparse coding plays a key role in it. This paper presents a novel supervised structure dictionary learning (SSDL) algorithm to learn a discriminative and block structure dictionary. We associate label information with each dictionary item and make each class-specific sub-dictionary in the whole structured dictionary have good representation ability to the training samples from the associated class. More specifically, we learn a structured dictionary and a multiclass classifier simultaneously. Adding an inhomogeneous representation term to the objective function and considering the independence of the class-specific sub-dictionaries improve the discrimination capabilities of the sparse coordinates. An iteratively optimization method be proposed to solving the new formulation. Experimental results on four face databases demonstrate that our algorithm outperforms recently proposed competing sparse coding methods.

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