scholarly journals Incorporating Uncertainty in Diagnostic Analysis of Mechanical Systems

2005 ◽  
Vol 127 (2) ◽  
pp. 315-325 ◽  
Author(s):  
Gregory M. Mocko ◽  
Robert Paasch
Keyword(s):  
Life Cycle ◽  
Failure Modes ◽  
Mechanical Systems ◽  
Fault Tree Analysis ◽  
Joint Probability ◽  
Fault Isolation ◽  
Diagnostic Model ◽  
Component Reliability ◽  
Bayes Formula ◽  
Alternative Designs

The increase in complexity of modern mechanical systems can often lead to systems that are difficult to diagnose and, therefore, require a great deal of time and money to return to a normal operating condition. Analyzing mechanical systems during the product development stages can lead to systems optimized in the area of diagnosability and, therefore, to a reduction of life cycle costs for both consumers and manufacturers and an increase in the useable life of the system. A methodology for diagnostic evaluation of mechanical systems incorporating indication uncertainty is presented. First, Bayes’ formula is used in conjunction with information extracted from the Failure Modes and Effects Analysis (FMEA), Fault Tree Analysis (FTA), component reliability, and prior system knowledge to construct the Component-Indication Joint Probability Matrix (CIJPM). The CIJPM, which consists of joint probabilities of all mutually exclusive diagnostic events, provides a diagnostic model of the system. The replacement matrix is constructed by applying a predetermined replacement criterion to the CIJPM. Diagnosability metrics are extracted from a replacement probability matrix, computed by multiplying the transpose of the replacement matrix by the CIJPM. These metrics are useful for comparing alternative designs and addressing diagnostic problems of the system, to the component and indication level. Additionally, the metrics can be used to predict cost associated with fault isolation over the life cycle of the system.

scholarly journals Incorporating Uncertainty in Diagnostic Analysis of Mechanical Systems

2002 ◽  
Author(s):  
Gregory Mocko ◽  
Robert Paasch
Keyword(s):  
Life Cycle ◽  
Failure Modes ◽  
Mechanical Systems ◽  
Fault Tree Analysis ◽  
Joint Probability ◽  
Fault Isolation ◽  
Diagnostic Model ◽  
Component Reliability ◽  
Bayes Formula ◽  
Alternative Designs

The increase in complexity of modern mechanical systems can often lead to systems that are difficult to diagnose, and therefore require a great deal of time and money to return to a normal operating condition. Analyzing mechanical systems during the product development stages can lead to systems optimized in the area of diagnosability, and therefore to a reduction of life cycle costs for both consumers and manufacturers and an increase in the useable life of the system. A methodology for diagnostic evaluation of mechanical systems incorporating indication uncertainty is presented. First, Bayes formula is used in conjunction with information extracted from the Failure Modes and Effects Analysis (FMEA), Fault Tree Analysis (FTA), component reliability, and prior system knowledge to construct the Component-Indication Joint Probability Matrix (CIJPM). The CIJPM, which consists of joint probabilities of all mutually exclusive diagnostic events, provides a diagnostic model of the system. The Replacement Matrix is constructed by applying a predetermined replacement criterion to the CIJPM. Diagnosability metrics are extracted from a Replacement Probability Matrix, computed by multiplying the transpose of the Replacement Matrix by the CIJPM. These metrics are useful for comparing alternative designs and addressing diagnostic problems of the system, to the component and indication level. Additionally, the metrics can be used to predict cost associated with fault isolation over the life cycle of the system.

Diagnostic Modeling and Diagnosability Evaluation of Mechanical Systems

1996 ◽  
Vol 118 (3) ◽  
pp. 425-431 ◽  
Author(s):  
G. E. Clark ◽  
R. K. Paasch
Keyword(s):  
Failure Modes ◽  
Mechanical Systems ◽  
Diagnostic Model ◽  
System Failure ◽  
System Failures ◽  
Maintenance Time ◽  
Increase Product Quality ◽  
Reliability Methods ◽  
Air Control ◽  
Diagnostic Modeling

Consideration of diagnosability in product design promises to increase product quality by reducing maintenance time without increasing cost or decreasing reliability. Methods for investigating the diagnosability of mechanical and electro-mechanical systems are described and are applied to the Bleed Air Control System (BACS) on the Boeing 747-400. The BACS is described and a diagnostic model is developed using information from the system Failure Modes and Effects Analysis. Emphasis is placed on the relationships between the system’s functions and its components. Two metrics for the evaluation of system diagnosability and two metrics for the evaluation of component diagnosability are defined. These metrics emphasize diagnostic ambiguity and are combined with the probability of different system failures to weight the effects of each failure. Three modified systems are produced by reassigning functions from one component to another. The resulting effects on the system and component diagnosability are evaluated. We show that by changing these relationships system diagnosability can be improved without adding sensors or other components.

scholarly journals Diagnostic Analysis for Mechanical Systems

2000 ◽  
Author(s):  
Scott Henning ◽  
Robert Paasch
Keyword(s):  
Life Cycle ◽  
Failure Modes ◽  
Matrix Multiplication ◽  
Fault Isolation ◽  
Electromechanical System ◽  
Life Cycle Costs ◽  
Diagnostic Analysis ◽  
Mathematical Methods ◽  
Relationship Analysis ◽  
Diagnostic Characteristics

Abstract An analysis and modeling method of the diagnostic characteristics of a mechanical or electromechanical system is presented. Diagnosability analysis is especially relevant given the complexities and functional interdependencies of modern-day systems, since improvements in diagnosability can lead to a reduction of a system’s life-cycle costs. Failure and diagnostic analysis leads to system diagnosability modeling with the Failure Modes and Effects Analysis (FMEA) and component-indication relationship analysis. Methods are then developed for translating the diagnosability model into mathematical methods for computing metrics such as distinguishability and susceptibility. These methods involve the use of matrices to represent the failure and replacement characteristics of the system. Diagnosability metrics are extracted by matrix multiplication. These metrics are useful when comparing the diagnosability of proposed designs or predicting the life-cycle costs of fault isolation.

Diagnostic Modeling and Diagnosability Evaluation of Mechanical Systems

1994 ◽  
Author(s):  
Garrett E. Clark ◽  
Robert K. Paasch
Keyword(s):  
Failure Modes ◽  
Mechanical Systems ◽  
Diagnostic Model ◽  
System Failure ◽  
System Failures ◽  
Maintenance Time ◽  
Increase Product Quality ◽  
Reliability Methods ◽  
Air Control ◽  
Diagnostic Modeling

Abstract Consideration of diagnosability in product design promises to increase product quality by reducing maintenance time without increasing cost or decreasing reliability. Methods for investigating the diagnosability of mechanical and electro-mechanical systems are described and are applied to the Bleed Air Control System (BACS) on the Boeing 747-400. The BACS is described and a diagnostic model is developed using information from the system Failure Modes and Effects Analysis. Emphasis is placed on the relationships between the system’s functions and its components. Two metrics for the evaluation of system diagnosability and two metrics for the evaluation of component diagnosability are defined. These metrics emphasize diagnostic ambiguity and are combined with the probability of different system failures to weight the affects of each failure. Three modified systems are produced by reassigning functions from one component to another. The resulting affects on the system and component diagnosability are evaluated. We show that by changing these relationships system diagnosability can be improved without adding sensors or other components.

Electrical Characterization of Different Failure Modes in Sub-100 nm Devices Using Nanoprobing Technique

2009 ◽  
Author(s):  
E. Hendarto ◽  
S.L. Toh ◽  
J. Sudijono ◽  
P.K. Tan ◽  
H. Tan ◽  
...  
Keyword(s):  
Electron Microscope ◽  
Focused Ion Beam ◽  
Failure Modes ◽  
Ion Beam ◽  
Electrical Characterization ◽  
Nickel Silicide ◽  
Fault Isolation ◽  
Technology Nodes ◽  
Scanning Electron

Abstract The scanning electron microscope (SEM) based nanoprobing technique has established itself as an indispensable failure analysis (FA) technique as technology nodes continue to shrink according to Moore's Law. Although it has its share of disadvantages, SEM-based nanoprobing is often preferred because of its advantages over other FA techniques such as focused ion beam in fault isolation. This paper presents the effectiveness of the nanoprobing technique in isolating nanoscale defects in three different cases in sub-100 nm devices: soft-fail defect caused by asymmetrical nickel silicide (NiSi) formation, hard-fail defect caused by abnormal NiSi formation leading to contact-poly short, and isolation of resistive contact in a large electrical test structure. Results suggest that the SEM based nanoprobing technique is particularly useful in identifying causes of soft-fails and plays a very important role in investigating the cause of hard-fails and improving device yield.

Study on Hazards Identification for the Life Cycle of Household Electric Blankets

2014 ◽  
Vol 968 ◽  
pp. 218-221
Author(s):  
Xia Liu ◽  
Hong Qi Luo ◽  
Rui Fu ◽  
He Liang Song
Keyword(s):  
Life Cycle ◽  
Health And Safety ◽  
Fault Tree Analysis ◽  
Hazard Identification ◽  
Quality And Safety ◽  
Effect Analysis ◽  
Structure Characteristics ◽  
Full Life Cycle ◽  
Working Principle ◽  
Full Life

Household electric blankets are widely used in China, but the problem of quality and safety is also more prominent, which is a serious threat to the health and safety of consumers. The structure characteristics and working principle of household electric blanket are analyzed. The hazards in the each stage of full life cycle are identified, including the stages of designing, manufacturing, packaging, transporting, utilizing and recycling. Hazard identification of each stage is made with methods of scenario analysis, safety check list, fault hypothesis analysis, hazard and operability analysis, failure mode and effect analysis and fault tree analysis, respectively.

Reliability Analysis of Great Lakes Marine Diesels: State of the Art and Current Modeling

1990 ◽  
Vol 27 (04) ◽  
pp. 237-249
Author(s):  
Anastassios N. Perakis ◽  
Bahadir Inozu
Keyword(s):  
Great Lakes ◽  
Diesel Engines ◽  
Failure Modes ◽  
State Of The Art ◽  
Relevant Literature ◽  
Fault Tree Analysis ◽  
Reliability Engineering ◽  
Marine Industry ◽  
Marine Diesel ◽  
Basic Concepts

Some essential steps for the application of reliability, availability, and maintainability (RAM) techniques to marine diesel engines are presented. The paper begins with a summary of the basic concepts of reliability engineering, followed by a survey of the relevant literature on RAM applications to the marine industry and to marine diesel engines in particular. Next, the results of an informal survey of the reliability, maintenance, and replacement practices of Great Lakes operators are presented. Finally, the first two steps for a RAM application, failure modes and effects analysis and fault tree analysis, are introduced and applied for a prototype Colt-Pielstick marine diesel engine.

ADS-B: Probabilistic Safety Assessment

2017 ◽  
Vol 70 (4) ◽  
pp. 887-906 ◽  
Author(s):  
Busyairah Syd Ali ◽  
Washington Yotto Ochieng ◽  
Arnab Majumdar
Keyword(s):  
Safety Assessment ◽  
Failure Modes ◽  
Fault Tree Analysis ◽  
Performance Standards ◽  
System Safety ◽  
Probabilistic Safety Assessment ◽  
Assessment Approach ◽  
Primary Means ◽  
Importance Analysis ◽  
Probabilistic Safety

In the effort to quantify Automatic Dependent Surveillance Broadcast (ADS-B) system safety, the authors have identified potential ADS-B failure modes in Syd Ali et al. (2014). Based on the findings, six potential hazards of ADS-B are identified in this paper. The authors then applied the Probabilistic Safety Assessment approach which includes Fault Tree Analysis (FTA) and Importance Analysis methods to quantify the system safety. FTA is applied to measure ADS-B system availability for each identified hazard while Importance Analysis is conducted to identify the most significant failure modes that may lead to the occurrence of the hazards. In addition, risk significance and safety significance of each failure mode are also identified. The result shows that the availability for the ADS-B system as a sole surveillance means is low at 0·898 in comparison to the availability of ADS-B system as supplemental or as primary means of surveillance at 0·95 and 0·999 respectively. The latter availability values are obtained from Minimum Aviation System Performance Standards (MASPS) for Automatic Dependent Surveillance-Broadcast (DO-242A).

Safety Analysis Model of DUCG Based on FMEA/FTA

2018 ◽  
Author(s):  
Zhenxu Zhou ◽  
Hao Nie ◽  
Chunling Dong ◽  
Qin Zhang
Keyword(s):  
Failure Modes ◽  
Fault Tree Analysis ◽  
Safety Analysis ◽  
Nuclear Industry ◽  
Analysis Model ◽  
Industrial Systems ◽  
The Past ◽  
Research Cycle ◽  
Dynamic Uncertain Causality Graph ◽  
Analytical Power

Failure Modes and Effects Analysis (FMEA) is a useful tool to find possible flaws, to reduce cost and to shorten research cycle in complex industrial systems. Fault Tree Analysis (FTA) has gained credibility over the past years, not only in nuclear industry, but also in other industries like aerospace, petrochemical, and weapon. Both FMEA and FTA are effective techniques in safety analysis, but there are still many uncertain factors in them that are not well addressed until now. This paper combines FMEA and FTA based on Dynamic Uncertain Causality Graph (DUCG) to solve this issue. Firstly, the FMEA model is mapped into a corresponding DUCG graph. Secondly, FTA model is mapped into a corresponding DUCG graph. Thirdly, combine the above DUCG graphs. Finally, users can modify the combined DUCG graph and calculations are made. This paper bridges the gap between FMEA and FTA by combining the two methods using DUCG. And additional modeling power and analytical power can be achieved with the advantages of the combined DUCG safety analysis model and its inference algorithm. This method can also promote the application of DUCG in the system reliability and safety analysis. An example is used to illustrate this method.

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