Fault Diagnosis and Prognosis Techniques for Complex Engineering Systems

2021 ◽  
Keyword(s):  
Fault Diagnosis ◽  
Engineering Systems ◽  
Diagnosis And Prognosis ◽  
Complex Engineering ◽  
Fault Diagnosis And Prognosis ◽  
Complex Engineering Systems

An integrated architecture for fault diagnosis and failure prognosis of complex engineering systems

2012 ◽  
Vol 39 (10) ◽  
pp. 9031-9040 ◽  
Author(s):  
Chaochao Chen ◽  
Douglas Brown ◽  
Chris Sconyers ◽  
Bin Zhang ◽  
George Vachtsevanos ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Engineering Systems ◽  
Integrated Architecture ◽  
Complex Engineering ◽  
Failure Prognosis ◽  
Complex Engineering Systems

scholarly journals A machine learning-based clustering approach to diagnose multi-component degradation of aircraft fuel systems

2021 ◽  
Author(s):  
Haochen Liu ◽  
Yifan Zhao ◽  
Anna Zaporowska ◽  
Zakwan Skaf
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Engineering Systems ◽  
Diagnosis And Prognosis ◽  
System A ◽  
Multiple Components ◽  
Fault Severity ◽  
Aircraft Fuel ◽  
Clustering Approach ◽  
Fault Diagnosis And Prognosis

AbstractAccurate fault diagnosis and prognosis can significantly reduce maintenance costs, increase the safety and availability of engineering systems that have become increasingly complex. It has been observed that very limited researches have been reported on fault diagnosis where multi-component degradation are presented. This is essentially a challenging Complex Systems problem where features multiple components interacting simultaneously and nonlinearly with each other and its environment on multiple levels. Even the degradation of a single component can lead to a misidentification of the fault severity level. This paper introduces a new test rig to simulate the multi-component degradation of the aircraft fuel system. A machine learning-based data analytical approach based on the classification of clustering features from both time and frequency domains is proposed. The scope of this framework is the identification of the location and severity of not only the system fault but also the multi-component degradation. The results illustrate that (a) the fault can be detected with accuracy > 99%; (b) the severity of fault can be identified with an accuracy of almost 100%; (c) the degradation level can be successfully identified with the R-square value > 0.9.

Intelligent Fault Diagnosis and Prognosis for Engineering Systems

2006 ◽  
Author(s):  
George Vachtsevanos ◽  
Frank Lewis ◽  
Michael Roemer ◽  
Andrew Hess ◽  
Biqing Wu
Keyword(s):  
Fault Diagnosis ◽  
Engineering Systems ◽  
Intelligent Fault Diagnosis ◽  
Diagnosis And Prognosis ◽  
Fault Diagnosis And Prognosis

Effectiveness Of Life Cycle Management Of Projects Using Information Modeling

2019 ◽  
pp. 24-29 ◽  
Keyword(s):  
Life Cycle ◽  
Construction Projects ◽  
Life Cycle Management ◽  
Information Modeling ◽  
Engineering Systems ◽  
Basic Principles ◽  
Complex Engineering ◽  
Domestic Practices ◽  
Capital Construction ◽  
Complex Engineering Systems

Improving the efficiency of life cycle management of capital construction projects using information modeling technologies is one of the important tasks of the construction industry. The paper presents an analysis of accumulated domestic practices, including the legal and regulatory framework, assessing the effectiveness of managing the implementation of investment construction projects and of complex and serial capital construction projects, as well as the life cycle management of especially dangerous technically complex and unique capital construction projects using information modeling technologies, especially capital construction projects, as well as their supporting and using systems, primarily in the nuclear and transport sectors. A review of modern approaches to assessing the effectiveness of life cycle management systems of complex engineering systems in relation to capital construction projects is carried out. The presented material will make it possible to formulate the basic principles and prospects of applying approaches to assessing the effectiveness of the life cycle management system of a capital construction project using information modeling technologies.

scholarly journals Special Issue “Advances in Machine Learning and Deep Learning Based Machine Fault Diagnosis and Prognosis”

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 532
Author(s):  
Mohand Djeziri ◽  
Marc Bendahan
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Special Issue ◽  
Maintenance Strategies ◽  
Corrective Maintenance ◽  
Diagnosis And Prognosis ◽  
Machine Fault Diagnosis ◽  
Failure Prognosis ◽  
Fault Diagnosis And Prognosis

Fault diagnosis and failure prognosis aim to reduce downtime of the systems and to optimise their performance by replacing preventive and corrective maintenance strategies with predictive or conditional ones [...]

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