Classification of Common Geotechnical Failure Types Occurring in Queensland's Heavy Haul Rail Network

2014 ◽  
Author(s):  
M. Mirzababaei ◽  
J. Egwurube ◽  
Y. Gyasi-Agyei ◽  
D. Foun ◽  
A. Hammond ◽  
...  
Keyword(s):  
Rail Network ◽  
Heavy Haul ◽  
Failure Types

Features of current distribution in the traction rail network for heavy-haul traffic

2021 ◽  
pp. 52-58
Author(s):  
Maxim Viktorovich Basharkin ◽  
◽  
Alevtina Gennadyevna Isaycheva ◽  
Keyword(s):  
Current Distribution ◽  
Current Flow ◽  
Dynamic Loads ◽  
Automated Systems ◽  
Heavy Weight ◽  
Rail Network ◽  
Traction Current ◽  
Heavy Haul ◽  
Resistance Value

The paper investigates the limits of change in resistance value of traction rail network elements due to dynamic loads arising during the movement of trains with increased weight and length. An augmented electric diagram of rail joint with a duplicating junction coupler taken into account has been presented. The ways of traction current flow during simultaneous passing of heavy-weight trains along the adjacent track connected by intertrack junctions have been determined. Conclusions have been made about the necessity of constant monitoring of traction rail network elements condition, which can be ensured only by implementing special automated systems.

Models and Algorithms of Locomotive Layout Optimization in Regional Heavy Haul Rail Network

2013 ◽  
Vol 13 (9) ◽  
pp. 1544-1550
Author(s):  
Junhua Chen ◽  
Qi Liu ◽  
Jian Yu ◽  
Yajing Zheng
Keyword(s):  
Layout Optimization ◽  
Rail Network ◽  
Heavy Haul

Axle-Equivalent Method for Concrete Bridge Deck Thin Pavement

2010 ◽  
Vol 44-47 ◽  
pp. 2757-2761
Author(s):  
Xun Qian Xu ◽  
Zhen Qing Liu ◽  
Guo Qing Wu ◽  
Chen Zhang
Keyword(s):  
Mechanical Characteristics ◽  
Bridge Deck ◽  
Load Condition ◽  
Influential Factors ◽  
Concrete Bridge ◽  
Concrete Bridge Deck ◽  
Pavement Structures ◽  
Equivalent Method ◽  
Failure Types

An axle-equivalent system was conducted for the concrete bridge deck thin pavement design according to classification of failure types. Based on indoor experiments and theoretical analysis, the mechanical characteristics of three different axle types was studied, which were corresponding with the other two typical pavement structures, and also the parameters of each axle’s equivalent formula was put forward. Including the influential factors such as climate, environment, traffic and load condition, the developed axle equivalent formula and the parameters were modified by the field test data. The achieved axle-equivalent method can be applied in the design of a new concrete bridge deck thin pavement and the assessment of an existing concrete bridge deck thin pavement.

Classification of Fault and Failure Types Determined by Dissolved Gas Analysis for Transformers

2019 ◽  
Vol 14 (4) ◽  
pp. 1665-1674
Author(s):  
Yonghyun Kim ◽  
Dongjin Kweon ◽  
Taesik Park ◽  
Seonghwan Kim ◽  
Jang-Seob Lim
Keyword(s):  
Gas Analysis ◽  
Dissolved Gas ◽  
Dissolved Gas Analysis ◽  
Failure Types

scholarly journals Machine Learning based Identification and Classification of Field-Operation caused Solar Panel Failures observed in Electroluminescence Images

2021 ◽  
Author(s):  
Stefan Bordihn
Keyword(s):  
Machine Learning ◽  
Solar Panel ◽  
Nearest Neighbour ◽  
Analysis Method ◽  
Solar Panels ◽  
Power Losses ◽  
Field Operation ◽  
Nearest Neighbour Classifier ◽  
Failure Types

Failure or degradation effects lead to power losses in solar panels during their field operation and are identified commonly by electroluminescence imaging. Failures like potential induced degradation and light and enhanced temperature induced degradation require an identification of the electroluminescence pattern over the entire solar panel. As the manual process of analysing patterns is prone to error, we seek for an automatic detection of these failure types. We predict automatically the failure types potential induced degradation and light and enhanced temperature induced degradation by adopting the principle component analysis method in combination with a k-nearest neighbour classifier.<br>

scholarly journals Machine Learning based Identification and Classification of Field-Operation caused Solar Panel Failures observed in Electroluminescence Images

2021 ◽  
Author(s):  
Stefan Bordihn
Keyword(s):  
Machine Learning ◽  
Solar Panel ◽  
Nearest Neighbour ◽  
Analysis Method ◽  
Solar Panels ◽  
Power Losses ◽  
Field Operation ◽  
Nearest Neighbour Classifier ◽  
Failure Types

Failure or degradation effects lead to power losses in solar panels during their field operation and are identified commonly by electroluminescence imaging. Failures like potential induced degradation and light and enhanced temperature induced degradation require an identification of the electroluminescence pattern over the entire solar panel. As the manual process of analysing patterns is prone to error, we seek for an automatic detection of these failure types. We predict automatically the failure types potential induced degradation and light and enhanced temperature induced degradation by adopting the principle component analysis method in combination with a k-nearest neighbour classifier.<br>

Note on the spectral classification of carbon stars in the photographic infrared region

1966 ◽  
Vol 24 ◽  
pp. 21-23
Author(s):  
Y. Fujita
Keyword(s):  
Infrared Region ◽  
Spectral Classification ◽  
Carbon Stars

We have investigated the spectrograms (dispersion: 8Å/mm) in the photographic infrared region fromλ7500 toλ9000 of some carbon stars obtained by the coudé spectrograph of the 74-inch reflector attached to the Okayama Astrophysical Observatory. The names of the stars investigated are listed in Table 1.

Diagnostic Value of Electron Microscopy in Soft Tissue Tumors

1970 ◽  
Vol 28 ◽  
pp. 220-221
Author(s):  
Gerald Fine ◽  
Azorides R. Morales
Keyword(s):  
Cardiac Myxoma ◽  
Osteogenic Sarcoma ◽  
Diagnostic Value ◽  
Soft Tissue Tumors ◽  
Amelanotic Melanoma ◽  
Growth Patterns ◽  
Light Microscopic Level ◽  
Mesenchymal Tumors ◽  
Good Agreement

For years the separation of carcinoma and sarcoma and the subclassification of sarcomas has been based on the appearance of the tumor cells and their microscopic growth pattern and information derived from certain histochemical and special stains. Although this method of study has produced good agreement among pathologists in the separation of carcinoma from sarcoma, it has given less uniform results in the subclassification of sarcomas. There remain examples of neoplasms of different histogenesis, the classification of which is questionable because of similar cytologic and growth patterns at the light microscopic level; i.e. amelanotic melanoma versus carcinoma and occasionally sarcoma, sarcomas with an epithelial pattern of growth simulating carcinoma, histologically similar mesenchymal tumors of different histogenesis (histiocytoma versus rhabdomyosarcoma, lytic osteogenic sarcoma versus rhabdomyosarcoma), and myxomatous mesenchymal tumors of diverse histogenesis (myxoid rhabdo and liposarcomas, cardiac myxoma, myxoid neurofibroma, etc.)

Ultrastructure of mesotheliomas

1984 ◽  
Vol 42 ◽  
pp. 98-101
Author(s):  
Irving Dardick
Keyword(s):  
Electron Microscopy ◽  
Latent Period ◽  
Spindle Cell ◽  
Spindle Cell Sarcoma ◽  
Metastatic Adenocarcinoma ◽  
Cell Sarcoma ◽  
Cytological Features ◽  
Industrial Use ◽  
Degree Of Differentiation

With the extensive industrial use of asbestos in this century and the long latent period (20-50 years) between exposure and tumor presentation, the incidence of malignant mesothelioma is now increasing. Thus, surgical pathologists are more frequently faced with the dilemma of differentiating mesothelioma from metastatic adenocarcinoma and spindle-cell sarcoma involving serosal surfaces. Electron microscopy is amodality useful in clarifying this problem.In utilizing ultrastructural features in the diagnosis of mesothelioma, it is essential to appreciate that the classification of this tumor reflects a variety of morphologic forms of differing biologic behavior (Table 1). Furthermore, with the variable histology and degree of differentiation in mesotheliomas it might be expected that the ultrastructure of such tumors also reflects a range of cytological features. Such is the case.

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