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.

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.

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.

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.

Life-Cycle Costs of Selected Uniformed Health Professions (Phase II: The Impact of Constraints and Policies on the Optimal-Mix-of-Accession Model)

2003 ◽  
Author(s):  
Shayne Brannman ◽  
Eric W. Christensen ◽  
Ronald H. Nickel ◽  
Cori Rattelman ◽  
Richard D. Miller
Keyword(s):  
Life Cycle ◽  
Phase Ii ◽  
Health Professions ◽  
Life Cycle Costs ◽  
Optimal Mix ◽  
The Impact

Impacts of Various Maintenance Tasks on Life-cycle Costs of a Power Grid Project

2020 ◽  
Author(s):  
Shuyan Zhang ◽  
Shuyin Duan ◽  
Fushuan Wen ◽  
Farhad Shahnia ◽  
Qingfang Chen ◽  
...  
Keyword(s):  
Life Cycle ◽  
Power Grid ◽  
Life Cycle Costs

scholarly journals Impact of Cycle Time and Payload of an Industrial Robot on Resource Efficiency

Robotics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 33
Author(s):  
Florian Stuhlenmiller ◽  
Steffi Weyand ◽  
Jens Jungblut ◽  
Liselotte Schebek ◽  
Debora Clever ◽  
...  
Keyword(s):  
Life Cycle Assessment ◽  
Life Cycle ◽  
Energy Consumption ◽  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Resource Efficiency ◽  
Life Cycle Costs ◽  
Gas Emissions ◽  
Cycle Times ◽  
The Individual

Modern industry benefits from the automation capabilities and flexibility of robots. Consequently, the performance depends on the individual task, robot and trajectory, while application periods of several years lead to a significant impact of the use phase on the resource efficiency. In this work, simulation models predicting a robot’s energy consumption are extended by an estimation of the reliability, enabling the consideration of maintenance to enhance the assessment of the application’s life cycle costs. Furthermore, a life cycle assessment yields the greenhouse gas emissions for the individual application. Potential benefits of the combination of motion simulation and cost analysis are highlighted by the application to an exemplary system. For the selected application, the consumed energy has a distinct impact on greenhouse gas emissions, while acquisition costs govern life cycle costs. Low cycle times result in reduced costs per workpiece, however, for short cycle times and higher payloads, the probability of required spare parts distinctly increases for two critical robotic joints. Hence, the analysis of energy consumption and reliability, in combination with maintenance, life cycle costing and life cycle assessment, can provide additional information to improve the resource efficiency.

Life Cycle Costs

World Pumps ◽  
2001 ◽  
Vol 2001 (419) ◽  
pp. 28 ◽  
Author(s):  
Kjell Alfredsson
Keyword(s):  
Life Cycle ◽  
Life Cycle Costs
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