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Does condition monitoring deliver the results you expect. Can we sharpen the saw and make a more informed reliability decision? This project investigated the use of a Maintenance Decision Support tool and how it may be used to improve... more
Does condition monitoring deliver the results you expect. Can we sharpen the saw and make a more informed reliability decision? This project investigated the use of a Maintenance Decision Support tool and how it may be used to improve reliability decisions based upon failure prediction. Data collection and manipulation proved to be the single most challenging issue. The accuracy with which failures are reported in the CMMS and the need to understand which failure modes actually occurred and whether they really failed or were suspended was shown to be of prime importance if reliability analysis was to succeed. The effort needed by the Reliability Engineer in performing reliability analysis pales in comparison to that required for the cleansing of the data and for its transformation into analyzable form. Once good data emerges from the anarchy of styles used within the CMMS, software makes light of the task of detailed reliability analysis that will enable good maintenance decisions.
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The most powerful information sought by all maintenance engineers and managers boils down to the conditional failure probability. It is the probability of an item failing in an upcoming period of interest knowing that it is currently in... more
The most powerful information sought by all maintenance engineers and managers boils down to the conditional failure probability. It is the probability of an item failing in an upcoming period of interest knowing that it is currently in an unfailed state. If you knew that the conditional probability of failure of a given part or component were unusually high you could channel your manpower to intervene propitiously, thereby preempting the consequences of a failure in service while avoiding waste of resources and unnecessary downtime on items where failure is not imminent.
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Discusses work completed at Cardinal River Coals in Canada to improve the existing oil analysis condition monitoring program being undertaken for wheel motors. Oil analysis results from a fleet of 55 haul truck wheel motors were analyzed... more
Discusses work completed at Cardinal River Coals in Canada to improve the existing oil analysis condition monitoring program being undertaken for wheel motors. Oil analysis results from a fleet of 55 haul truck wheel motors were analyzed along with their respective failures and repairs over a nine‐year period. Detailed data cleaning procedures were applied to prepare data for modeling. In addition, definitions of failure and suspension were clarified depending on equipment condition at replacement. Using the proportional hazards model approach, the key condition variables relating to failures were found from among the 19 elements monitored, plus sediment and viscosity. Those key variables were then incorporated into a decision model that provided an unambiguous and optimal recommendation on whether to continue operating a wheel motor or to remove it for overhaul on the basis of data obtained from an oil sample. Wheel motor failure implied extensive planetary gear or sun gear damage ...
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: A World Wide Web (WWW) interface to a FMECA (Failure Mode Effects and Criticality Analysis) building tool and database is described in this paper. The WWW FMECA has been linked to an oil analysis expert system. The interface is based on... more
: A World Wide Web (WWW) interface to a FMECA (Failure Mode Effects and Criticality Analysis) building tool and database is described in this paper. The WWW FMECA has been linked to an oil analysis expert system. The interface is based on an Oracle 7 distributed database tightly integrated with Web server functions. A second laboratory Oracle distributed database contains historical oil analysis data. A rule based expert system accesses both databases and matches pertinent failure mode and detection method details from the FMECA database with current and statistical trend data from the laboratory database. The FMECA database contains both public and private data. Publicly accessible tables contain failure rates, failure mode distributions, causes, effects, detection methods, and severity classifications while private tables contain the failure mode analyses accessible only to the authenticated user. The general data tables provide cooperative and synergistic advantages to a diverse ...
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The most powerful information sought by all maintenance engineers and managers boils down to the conditional failure probability. It is the probability of an item failing in an upcoming period of interest knowing that it is currently in... more
The most powerful information sought by all maintenance engineers and managers boils down to the conditional failure probability. It is the probability of an item failing in an upcoming period of interest knowing that it is currently in an unfailed state. If you knew that the conditional probability of failure of a given part or component were unusually high you could channel your manpower to intervene propitiously, thereby preempting the consequences of a failure in service while avoiding waste of resources and unnecessary downtime on items where failure is not imminent.
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Discusses work completed at Cardinal River Coals in Canada to improve the existing oil analysis condition monitoring program being undertaken for wheel motors. Oil analysis results from a fleet of 55 haul truck wheel motors were analyzed... more
Discusses work completed at Cardinal River Coals in Canada to improve the existing oil analysis condition monitoring program being undertaken for wheel motors. Oil analysis results from a fleet of 55 haul truck wheel motors were analyzed along with their respective failures and repairs over a nine-year period. Detailed data cleaning procedures were applied to prepare data for modeling. In