scholarly journals Intelligent Methods for Condition Diagnosis of Plant Machinery

10.5772/15410 ◽  
2011 ◽  
Author(s):  
Huaqing Wang ◽  
Peng Che
Keyword(s):  
Intelligent Methods ◽  
Condition Diagnosis

Diagnostics of Track Infrastructure as Part of the Digitisation of Russian Railways

2020 ◽  
Vol 64 (188) ◽  
pp. 149-160
Author(s):  
Janusz Poliński
Keyword(s):  
Diagnostic Tools ◽  
Gradual Transition ◽  
Technical Diagnostics ◽  
Strategic Development ◽  
Railway Infrastructure ◽  
Monitoring And Forecasting ◽  
Infrastructure Maintenance ◽  
Result Analysis ◽  
Railway Maintenance ◽  
Condition Diagnosis

Technical diagnostics is an integral part of the railway maintenance process. Through timely maintenance, in addition to ensuring the safety, functional and technical reliability of the infrastructure, maintenance costs are reduced and downtime losses, due to failures or premature repair requests, are eliminated or reduced. The track infrastructure diagnostic tools have evolved. This is related to, among others, the miniaturisation of instruments, reading accuracy during motion, as well as upgraded measurement automation and result analysis. Currently, data obtained from multifunctional diagnostic tools is the basis for the developed Russian railway infrastructure maintenance and operation digital model. The strategic development of mobile diagnostic labs is the gradual transition to solutions with advanced digital analysis, supported by artificial intelligence, monitoring and forecasting. The article presents the development of mobile labs for the railroad infrastructure condition diagnosis up to the current solutions, in which measurements take place without human intervention and the obtained information is transmitted in real time to the analysis and decision centres. Keywords: rail transport, measuring wagons, digitisation of railways, Russian railways

scholarly journals Decision Making for Project Appraisal in Uncertain Environments: A Fuzzy-Possibilistic Approach of the Expanded NPV Method

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 27
Author(s):  
Konstantinos A. Chrysafis ◽  
Basil K. Papadopoulos
Keyword(s):  
Net Present Value ◽  
Fuzzy Numbers ◽  
Cost Of Capital ◽  
Weighted Average ◽  
Present Value ◽  
Major Drawback ◽  
Project Appraisal ◽  
Equity Cost ◽  
Set Up ◽  
Intelligent Methods

The major drawback of the classic approaches for project appraisal is the lack of the possibility to handle change requests during the project’s life cycle. This fact incorporates the concept of uncertainty in the estimation of this investment’s worth. To resolve this issue, the authors use fuzzy numbers, possibilistic moments of fuzzy numbers and the hybrid (fuzzy statistic) fuzzy estimators’ method in order to introduce a fuzzy possibilistic version of the expanded net present value method (FPeNPV). This approach consists of two factors: the fuzzy possibilistic NPV and the fuzzy option premium. For the estimation of the fuzzy NPV, some basic assumptions are taken into consideration: (1) the opportunity cost of capital, used as the present value interest factor calculated through the weighted average cost of capital (WACC), (2) the equity cost, determined through the possibilistic set-up of the capital asset pricing model CAPM, and (3) the inflation factor, also included in the estimation of the NPV. The fuzzy estimators’ method is used for the computation of the fuzzy option premium. An algorithm of nine major steps leads to the computation of the FPeNPV. This gives the administration the opportunity to adapt to potential changes in the company’s internal and external environments. In this way, the symmetry between the planning and execution phase of a project can be reinstated. The results validate the statement that fuzzy and intelligent methods remain valuable tools to express uncertainty in various scientific areas. Finally, an illustrative example aims at a thorough comprehension of this new approach of the expanded NPV method.

Applications of intelligent methods in various types of heat exchangers: a review

2021 ◽  
Author(s):  
Mohammad Ghalandari ◽  
Misagh Irandoost Shahrestani ◽  
Akbar Maleki ◽  
Mostafa Safdari Shadloo ◽  
Mamdouh El Haj Assad
Keyword(s):  
Heat Exchangers ◽  
Intelligent Methods

scholarly journals Estimation of tunnelling-induced settlement by modern intelligent methods

2015 ◽  
Vol 55 (4) ◽  
pp. 737-748 ◽  
Author(s):  
Kaveh Ahangari ◽  
Sayed Rahim Moeinossadat ◽  
Danial Behnia
Keyword(s):  
Intelligent Methods

scholarly journals Modeling the Process of Event Sequence Data Generated for Working Condition Diagnosis

2015 ◽  
Vol 2015 ◽  
pp. 1-13
Author(s):  
Jianwei Ding ◽  
Yingbo Liu ◽  
Li Zhang ◽  
Jianmin Wang
Keyword(s):  
Working Condition ◽  
Sequence Data ◽  
A Priori ◽  
Real Data ◽  
Data Sets ◽  
Main Task ◽  
Event Sequence ◽  
Telemetry Data ◽  
Condition Monitoring Systems ◽  
Condition Diagnosis

Condition monitoring systems are widely used to monitor the working condition of equipment, generating a vast amount and variety of telemetry data in the process. The main task of surveillance focuses on analyzing these routinely collected telemetry data to help analyze the working condition in the equipment. However, with the rapid increase in the volume of telemetry data, it is a nontrivial task to analyze all the telemetry data to understand the working condition of the equipment without any a priori knowledge. In this paper, we proposed a probabilistic generative model called working condition model (WCM), which is capable of simulating the process of event sequence data generated and depicting the working condition of equipment at runtime. With the help of WCM, we are able to analyze how the event sequence data behave in different working modes and meanwhile to detect the working mode of an event sequence (working condition diagnosis). Furthermore, we have applied WCM to illustrative applications like automated detection of an anomalous event sequence for the runtime of equipment. Our experimental results on the real data sets demonstrate the effectiveness of the model.

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