Abstract
The mean time to failure (MTTF) expressing the mean value of the system life is a measure of system effectiveness. To estimate the remaining life of component and/or system, the dynamic mean time to failure concept is suggested. It is the time-dependent property depending on the status of components. The Kalman filter is used to estimate the reliability of components using the on-line information (directly measured sensor output or device-specific diagnostics in the intelligent sensor) in form of the numerical value (state factor). This factor considers the persistency of the fault condition and confidence level in measurement. If there is a complex system with many components, each calculated reliability`s of components are combined, which results in the dynamic MTTF of system. The illustrative examples are discussed. The results show that the dynamic MTTF can well express the component and system failure behaviour whether any kinds of failure are occurred or not. 9 refs., 6 figs. (Author)
Citation Formats
Oh, Deog Yeon, and Lee, Chong Chul.
Prediction of dynamic expected time to system failure.
Korea, Republic of: N. p.,
1997.
Web.
Oh, Deog Yeon, & Lee, Chong Chul.
Prediction of dynamic expected time to system failure.
Korea, Republic of.
Oh, Deog Yeon, and Lee, Chong Chul.
1997.
"Prediction of dynamic expected time to system failure."
Korea, Republic of.
@misc{etde_324106,
title = {Prediction of dynamic expected time to system failure}
author = {Oh, Deog Yeon, and Lee, Chong Chul}
abstractNote = {The mean time to failure (MTTF) expressing the mean value of the system life is a measure of system effectiveness. To estimate the remaining life of component and/or system, the dynamic mean time to failure concept is suggested. It is the time-dependent property depending on the status of components. The Kalman filter is used to estimate the reliability of components using the on-line information (directly measured sensor output or device-specific diagnostics in the intelligent sensor) in form of the numerical value (state factor). This factor considers the persistency of the fault condition and confidence level in measurement. If there is a complex system with many components, each calculated reliability`s of components are combined, which results in the dynamic MTTF of system. The illustrative examples are discussed. The results show that the dynamic MTTF can well express the component and system failure behaviour whether any kinds of failure are occurred or not. 9 refs., 6 figs. (Author)}
place = {Korea, Republic of}
year = {1997}
month = {Dec}
}
title = {Prediction of dynamic expected time to system failure}
author = {Oh, Deog Yeon, and Lee, Chong Chul}
abstractNote = {The mean time to failure (MTTF) expressing the mean value of the system life is a measure of system effectiveness. To estimate the remaining life of component and/or system, the dynamic mean time to failure concept is suggested. It is the time-dependent property depending on the status of components. The Kalman filter is used to estimate the reliability of components using the on-line information (directly measured sensor output or device-specific diagnostics in the intelligent sensor) in form of the numerical value (state factor). This factor considers the persistency of the fault condition and confidence level in measurement. If there is a complex system with many components, each calculated reliability`s of components are combined, which results in the dynamic MTTF of system. The illustrative examples are discussed. The results show that the dynamic MTTF can well express the component and system failure behaviour whether any kinds of failure are occurred or not. 9 refs., 6 figs. (Author)}
place = {Korea, Republic of}
year = {1997}
month = {Dec}
}