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.

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.

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.

Design FMEA for Mechatronic Systems Using Bayesian Network Causal Models

1999 ◽  
Author(s):  
Burton Hoyt Lee
Keyword(s):  
Failure Modes ◽  
Directed Acyclic Graphs ◽  
Design Evaluation ◽  
System Failure ◽  
Graph Structure ◽  
Design Teams ◽  
Mechatronic Systems ◽  
Acyclic Graphs ◽  
Dependency Structures ◽  
Diagnostic Modeling

Abstract This paper presents the use of Bayesian networks as a methodology for eliciting and encoding Failure Modes and Effects Analysis (BN-FMEA) models of mechatronic systems. The method uses probabilistic directed acyclic graphs to construct causal dependency structures between functional, behavioral and structural random variables of the physical system. Default apriori probabilities and conditional probability tables are generated and attached to the graph structure for post-design evaluation by diagnostic engineers. BN-FMEA provides a language for design teams to articulate — with less ambiguity and greater precision — component failure cause-effect relationships across sub-systems. An example of an inkjet printer illustrates how BN-FMEA can be applied. The approach supports traditional FMEA objectives — identification of system failure modes, provides improved knowledge representation and inferencing power over spreadsheets, and is generally applicable to the class of industry- and government-standard FMEA spreadsheets and tables in use today. Finally, BN-FMEA is presented as a basis for improved integration of design and diagnostic modeling of mechatronic systems.

Total System Intervention for System Failure

2013 ◽  
pp. 193-213
Author(s):  
Takafumi Nakamura ◽  
Kyoich Kijima
Keyword(s):  
Information And Communication Technologies ◽  
Total System ◽  
System Failure ◽  
Critical System ◽  
System Failures ◽  
System Intervention ◽  
Actual Application ◽  
Preventative Measures ◽  
Subsequent Discussion ◽  
Total System Intervention

In this paper, total system intervention for system failure (TSI for SF) is proposed for preventing further occurrences of system failures. TSI is a critical system practice for managing complex and differing viewpoints. First, the authors introduce meta-methodology called “system of system failures” (SOSF) as a common language among various stakeholders to improve their understanding of system failures. The actual application scenario is proposed: “TSI for SF.” The SOSF and related methodologies are used in the course of the subsequent discussion and debate to agree on who is responsible for the failure and identify the preventative measures to be applied. An application example in information and communication technologies engineering demonstrates that using the proposed “TSI for SF” helps prevent future system failures by learning from previous system failures. Three actions are identified for preventing further system failures: closing the gap between the stakeholders, introducing absolute goals, and enlarging system boundary.

Fault-Tolerant Control of Mechanical Systems Using Neural Networks

2012 ◽  
pp. 187-205
Author(s):  
Sunan Huang ◽  
Kok Kiong Tan ◽  
Tong Heng Lee
Keyword(s):  
Control System ◽  
Failure Modes ◽  
Fault Tolerant ◽  
A Priori ◽  
Mechanical Systems ◽  
Fault Tolerant Control ◽  
Working Environment ◽  
Harsh Environment ◽  
Environment Control ◽  
Component Failures

Due to harsh working environment, control systems may degrade to an unacceptable level, causing more regular fault occurrences. In this case, it is necessary to provide the fault-tolerant control for operating the system continuously. The existing control techniques have given some ways to solve this problem, but if the system behaves in an unanticipated manner, then the control system may need to be modified, so that it handles the modified system. In this chapter, the authors are concerned with how this control system can be done automatically, and when it can be done successfully. They aimed in this work at handling unanticipated failure modes, for which solutions have not been solved completely. The model-based fault-tolerant controller with a self-detecting algorithm is proposed. Here, the radial basis function neural network is used in the controller to estimate the unknown failures. Once the failure is detected, the re-configured control is activated and then maintains the system continously. The fault-tolerant control is illustrated in two cases. It is shown that the proposed method can cope with different failure modes which are unknown a priori. The result indicates that the solution is suitable for a class of mechanical systems whose dynamics are subject to sudden changes resulting from component failures when working in a harsh environment.

Prognostics and Health Management of Mechanical Systems

2013 ◽  
Vol 694-697 ◽  
pp. 872-875
Author(s):  
Jiang Chang ◽  
Fang Wei
Keyword(s):  
Health Management ◽  
Mechanical Systems ◽  
Smart Sensors ◽  
Electronic Systems ◽  
System Failure ◽  
Prognostics And Health Management ◽  
Critical Systems ◽  
Embedded Sensing ◽  
Safety Critical Systems ◽  
Self Test

Reliability is an important issue to consider for mechanical systems. The state of art is regular checkup and maintenance to ensure normal operations. This is not good enough for safety-critical systems like gearboxes in vehicles and helicopters because the risk of system failure still exists, let alone the manpower and monetary cost required. Prognostics and health management (PHM) was first raised by the U.S. armed force, which should ideally be able to predict faults and schedule maintenance only when necessary by monitoring the system condition. In this paper, inspired by the idea of Built-In Self Test (BIST) in electronic systems, we propose a novel framework to fulfill the task of prognostics and health management with a set of smart sensors, consisting of embedded sensing elements, wireless communication modules and micro-controllers. Both the significance and challenges of the framework are discussed.

Marketing System Failure: Revisioning Layton’s Marketing System Model

2020 ◽  
pp. 027614672096145
Author(s):  
Janine Williams ◽  
Janet Davey ◽  
Micael-Lee Johnstone
Keyword(s):  
System Failure ◽  
Marketing System ◽  
System Failures ◽  
Systems Perspective ◽  
Self Organized ◽  
Critical Junctures ◽  
Marketing Systems ◽  
Conceptual Paper ◽  
Path Dependent

The purpose of this conceptual paper is to examine the mechanisms of long term marketing system failure from a path dependent, marketing systems perspective in order to identify ways of avoiding such situations in future. Using the model of Layton and Duffy (2018) we critically analyze the current plastic packaging crisis and its evolution over time. Through examining the mechanisms of failure from a systems perspective, we extend the path dependent, marketing system evolution model and advance understanding of marketing system failures. As a result of this analysis, the paper provides five propositions regarding where failure occurs within the system and identifies critical junctures where intervention (self-organized stakeholder initiatives and/or public policy intervention) can facilitate desirable outcomes in the future.

A general formula for the downtime distribution of a parallel system

1996 ◽  
Vol 33 (03) ◽  
pp. 772-785
Author(s):  
Harald Haukås ◽  
Terje Aven
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Time Distribution ◽  
Life Time ◽  
Exact Formula ◽  
Parallel System ◽  
System Failure ◽  
System Failures ◽  
Failure Scenario ◽  
Common Situation

In this paper we study the problem of computing the downtime distribution of a parallel system comprising stochastically identical components. It is assumed that the components are independent, with an exponential life-time distribution and an arbitrary repair time distribution. An exact formula is established for the distribution of the system downtime given a specific type of system failure scenario. It is shown by performing a Monte Carlo simulation that the portion of the system failures that occur as described by this scenario is close to one when we consider a system with quite available components, the most common situation in practice. Thus we can use the established formula as an approximation of the downtime distribution given system failure. The formula is compared with standard Markov expressions. Some possible extensions of the formula are presented.

Sign in / Sign up

Export Citation Format

Share Document