Sponsored Recommendations
Sponsored Recommendations
A large municipal electrical utility (MEU) has been carefully collecting data related to the many assets in its CMMS for a number of years, but was dissatisfied with the value that was being generated. This conversion from data to usable information is a significant and growing issue for many businesses. The MEU approached OMDEC to assist in the key objective, namely to test the integrity and adequacy of the data for the purposes of reliability analysis (RA). The RA questions being asked will determine the array of data required; the MEU settled on the Living RCM module of Bi-Cycle’s product suite for the core analysis. The second objective was to demonstrate the effectiveness of the Living RCM software module.
The LRCM module was linked directly to the client’s CMMS, which contained valuable historical and current knowledge. With LRCM in live mode, the suite of analytical tools allowed LRCM users, with very limited training, to examine assets by class, location or individual component. Work tasks by year or month, top 10 bad actors, trends, reports showing corrective and emergency failures, a planning mode, and more can be accessed via the dashboard. By integrating LRCM with the client data, the EXAKT decision support tool can be installed to predict equipment failure, estimate useful remaining life and achieve an optimal risk/cost/reliability balance.
The steps involved in this pilot project were as follows:
A sample of the results from the analysis is shown below:
The user examines the incidences of a failure mode together with condition monitoring data preceding those failure events. Through statistical correlation analysis, the user will discover predictive patterns in the CM data (if they exist) for optimal decision making. Figure 1a applies an optimized maintenance decision. Figure 1b provides failure probabilities and confidence intervals.
From an analysis of the replacement costs and the repair costs of a fleet of capital items subject to periodic replacement, the user can calculate an annualized cost for various replacement scenarios. The lowest annualized cost represents the optimal economic replacement age. The cost difference from any other given age shows the annual savings derived from implementing the optimum decision.
The reliability continuous improvement procedure automatically updates parameters used in simulation models for predicting equipment availability. LRCM ensures that decisions from simulation studies can benefit from the latest data as recorded in the CMMS.
The Jackknife plot places failure modes that have occurred in a specified time period on a logarithmic grid. The vertical axis measures severity, for example downtime. The horizontal axis measures frequency. The failure modes that fall in the red region require immediate managerial attention. The reliability continuous improvement procedure monitors the desired movement of failure modes towards the origin.
The age reliability relationship can be expressed in many ways. Figure 5 illustrates the failure rate (hazard rate). It is the probability of failure in the next relatively short time interval since the last renewal of the item. This analysis answers the questions: Is preventive maintenance technically feasible? Is there a materials quality or installation problem? What is the “useful life” of the item? Is the MTBF (reliability) of a randomly failing item acceptable?
Rank the failure causes with the greatest impact on profitability in any class category or group and in any time window.
A critical issue in spares management is the appropriate level available spares when a critical long-life component fails, based on required plant availability and cost. Such components would include transformers in an electrical utility or electric motors in a conveyor system. The question to be addressed is "How many critical spares should be stocked at what cost?"
Other analytical results, such as risk and cost analysis, were also available to be shown.
The users were enthusiastic about the functionality of the software. “We now have ready access to useable information that was not previously available. It has opened our eyes to new and different reliability improvement opportunities.” The next step is to expand the application to more equipment and to include LRCM in a more broadly based living reliability project.