scholarly journals Detection method for dam deformation of tailing pond based on fault diagnosis algorithm

2020 ◽  
Vol 24 (3 Part A) ◽  
pp. 1489-1496
Author(s):  
Xiaoli Meng
Keyword(s):  
Fault Diagnosis ◽  
High Speed ◽  
Detection Method ◽  
Grey Theory ◽  
Detection Model ◽  
Tailing Dam ◽  
Feature Sequence ◽  
Diagnosis Algorithm ◽  
Dam Deformation ◽  
Original Feature

The existing methods of dam deformation detection of tailings reservoir have the problems of poor accuracy and slow speed. Therefore, a fault diagnosis algorithm based on tailing dam deformation detection method is proposed. The grey theory is used to accumulate the original feature sequence, and the first cumulative sequence is obtained. Based on this, the grey detection model is constructed, and then the concrete deformation of tailings dam body is accurately detected by precision test. Experimental results show that the method has high accuracy, high speed and practicability

scholarly journals The Assisted Positioning Technology for High Speed Train Based on Deep Learning

2020 ◽  
Vol 10 (23) ◽  
pp. 8625
Author(s):  
Yali Song ◽  
Yinghong Wen
Keyword(s):  
Deep Learning ◽  
High Speed ◽  
Detection Method ◽  
Generative Adversarial Networks ◽  
Single Shot ◽  
High Speed Train ◽  
Data Set ◽  
Detection Model ◽  
Model Based ◽  
Cumulative Error

In the positioning process of a high-speed train, cumulative error may result in a reduction in the positioning accuracy. The assisted positioning technology based on kilometer posts can be used as an effective method to correct the cumulative error. However, the traditional detection method of kilometer posts is time-consuming and complex, which greatly affects the correction efficiency. Therefore, in this paper, a kilometer post detection model based on deep learning is proposed. Firstly, the Deep Convolutional Generative Adversarial Networks (DCGAN) algorithm is introduced to construct an effective kilometer post data set. This greatly reduces the cost of real data acquisition and provides a prerequisite for the construction of the detection model. Then, by using the existing optimization as a reference and further simplifying the design of the Single Shot multibox Detector (SSD) model according to the specific application scenario of this paper, the kilometer post detection model based on an improved SSD algorithm is established. Finally, from the analysis of the experimental results, we know that the detection model established in this paper ensures both detection accuracy and efficiency. The accuracy of our model reached 98.92%, while the detection time was only 35.43 ms. Thus, our model realizes the rapid and accurate detection of kilometer posts and improves the assisted positioning technology based on kilometer posts by optimizing the detection method.

scholarly journals Arching Detection Method of Slab Track in High-Speed Railway Based on Track Geometry Data

2020 ◽  
Vol 10 (19) ◽  
pp. 6799
Author(s):  
Zhuoran Ma ◽  
Liang Gao ◽  
Yanglong Zhong ◽  
Shuai Ma ◽  
Bolun An
Keyword(s):  
High Speed ◽  
Large Scale ◽  
Detection Method ◽  
Low Cost ◽  
Detection Methods ◽  
Data Conversion ◽  
Track Geometry ◽  
Slab Track ◽  
Detection Model ◽  
Characteristics Analysis

During the long-term service of slab track, various external factors (such as complicated temperature) can result in a series of slab damages. Among them, slab arching changes the structural mechanical properties, deteriorates the track geometry conditions, and even threatens the operation of trains. Therefore, it is necessary to detect slab arching accurately to achieve effective maintenance. However, the current damage detection methods cannot satisfy high accuracy and low cost simultaneously, making it difficult to achieve large-scale and efficient arching detection. To this end, this paper proposed a vision-based arching detection method using track geometry data. The main works include: (1) data nonlinear deviation correction and arching characteristics analysis; (2) data conversion and augmentation; (3) design and experiments of convolutional neural network- based detection model. The results show that the proposed method can detect arching damages effectively, and the F1-score reaches 98.4%. By balancing the sample size of each pattern, the performance can be further improved. Moreover, the method outperforms the plain deep learning network. In practice, the proposed method can be employed to detect slab arching and help to make maintenance plans. The method can also be applied to the data-based detection of other structural damages and has broad prospects.

VIBRATION INDUCED FAILURE ANALYSIS OF A HIGH SPEED ROTOR SUPPORTED BY ACTIVE MAGNETIC BEARINGS

2015 ◽  
Vol 39 (4) ◽  
pp. 855-866 ◽  
Author(s):  
Sarvat M. Ahmad ◽  
Osman A. Ahmed ◽  
Zaharuddin Mohamed
Keyword(s):  
Fault Diagnosis ◽  
High Speed ◽  
Magnetic Bearings ◽  
Active Magnetic Bearings ◽  
Flexible Rotor ◽  
Describing Function ◽  
Industrial Grade ◽  
Sinusoidal Input ◽  
Diagnosis Algorithm ◽  
Amb System

Active Magnetic Bearings (AMBs) are increasingly used in various industries and a quick re-levitation of AMBs supported high speed flexible rotor is necessary in case of vibration induced failure. A robust fault diagnosis algorithm is presented to detect suspected saturation type of nonlinearity associated with a power amplifier. A five degree-of-freedom AMB system consisting of four opposing pair of radial magnets and a pair of axial magnets is considered. In this paper failure of an industrial grade AMB system is investigated using Sinusoidal Input Describing Function (SIDF) method. SIDF predicts the gain and frequency at which failure occurs. It is demonstrated that the predicted frequency is in agreement with the frequency at which failure occurs.

A high-speed D-CART online fault diagnosis algorithm for rotor systems

2019 ◽  
Vol 50 (1) ◽  
pp. 29-41 ◽  
Author(s):  
Huaxia Deng ◽  
Yifan Diao ◽  
Wei Wu ◽  
Jin Zhang ◽  
Mengchao Ma ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
High Speed ◽  
Rotor Systems ◽  
Diagnosis Algorithm

Diagnostic Fault Simulation for the Failure Analyst

2004 ◽  
Author(s):  
Dan Bodoh ◽  
Anthony Blakely ◽  
Terry Garyet
Keyword(s):  
Fault Diagnosis ◽  
Failure Analysis ◽  
Complete Solution ◽  
Fault Simulation ◽  
Building Blocks ◽  
Physical World ◽  
Design For Test ◽  
Fault Diagnosis System ◽  
Diagnosis Algorithm ◽  
Diagnosis Tool

Abstract Since failure analysis (FA) tools originated in the design-for-test (DFT) realm, most have abstractions that reflect a designer's viewpoint. These abstractions prevent easy application of diagnosis results in the physical world of the FA lab. This article presents a fault diagnosis system, DFS/FA, which bridges the DFT and FA worlds. First, it describes the motivation for building DFS/FA and how it is an improvement over off-the-shelf tools and explains the DFS/FA building blocks on which the diagnosis tool depends. The article then discusses the diagnosis algorithm in detail and provides an overview of some of the supporting tools that make DFS/FA a complete solution for FA. It also presents a FA example where DFS/FA has been applied. The example demonstrates how the consideration of physical proximity improves the accuracy without sacrificing precision.

Efficient Fault Diagnosis Algorithm in Dynamic Internet Service Environment

2009 ◽  
Vol 20 (9) ◽  
pp. 2520-2530
Author(s):  
Ling-Wei CHU ◽  
Shi-Hong ZOU ◽  
Shi-Duan CHENG ◽  
Chun-Qi TIAN ◽  
Wen-Dong WANG
Keyword(s):  
Fault Diagnosis ◽  
Internet Service ◽  
Service Environment ◽  
Diagnosis Algorithm

Orthotopic-filtering-based hierarchical fault diagnosis algorithm for linear recursive models

2020 ◽  
Vol 14 (16) ◽  
pp. 2310-2318
Author(s):  
Ziyun Wang ◽  
Guixiang Xu ◽  
Yan Wang ◽  
Ju H. Park ◽  
Zhicheng Ji
Keyword(s):  
Fault Diagnosis ◽  
Recursive Models ◽  
Diagnosis Algorithm

scholarly journals Multiview deep learning based on tensor decomposition and its application in fault detection of overhead contact systems

2021 ◽  
Author(s):  
Xuewu Zhang ◽  
Yansheng Gong ◽  
Chen Qiao ◽  
Wenfeng Jing
Keyword(s):  
High Speed ◽  
Tensor Decomposition ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Feature Maps ◽  
Training Time ◽  
Detection Model ◽  
Railway Line ◽  
Result Show ◽  
Deep Layers

AbstractThis article mainly focuses on the most common types of high-speed railways malfunctions in overhead contact systems, namely, unstressed droppers, foreign-body invasions, and pole number-plate malfunctions, to establish a deep-network detection model. By fusing the feature maps of the shallow and deep layers in the pretraining network, global and local features of the malfunction area are combined to enhance the network's ability of identifying small objects. Further, in order to share the fully connected layers of the pretraining network and reduce the complexity of the model, Tucker tensor decomposition is used to extract features from the fused-feature map. The operation greatly reduces training time. Through the detection of images collected on the Lanxin railway line, experiments result show that the proposed multiview Faster R-CNN based on tensor decomposition had lower miss probability and higher detection accuracy for the three types faults. Compared with object-detection methods YOLOv3, SSD, and the original Faster R-CNN, the average miss probability of the improved Faster R-CNN model in this paper is decreased by 37.83%, 51.27%, and 43.79%, respectively, and average detection accuracy is increased by 3.6%, 9.75%, and 5.9%, respectively.

Sign in / Sign up

Export Citation Format

Share Document