Maintenance, Reliability and Troubleshooting in Rotating Machinery. Группа авторов
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For example, let’s say the maintenance team of a chemical plant performs 500 hours of maintenance in a month. Of those 500 hours, 400 of them are planned maintenance hours. This makes our equation:
Figure 2.11 Pareto of machinery RCFA findings related to process outages. This chart points out the primary reasons for machinery-related process outages and can assist in optimizing your repair and PM strategies. In this hypothetical example, we see “End-of-Life” and “Design Issue” are the top reasons for machinery failure associated with unit outages. The “End-of-Life” results could suggest that you are either, 1) not identifying machines that are failing early enough to allow outages to be scheduled, or 2) you don’t have the right inspection intervals identified for our critical equipment. “Design Issues” failures should be analyzed to see if economics warrant design upgrades.
Ideally, at least 90% of planned maintenance time is necessary for optimal productivity. You can use the findings of Planned Maintenance Percentage to strengthen planned maintenance and reduce the likelihood of unexpected failures. There are numerous reasons to want to improve your facility’s planned maintenance percentage. Some of these reasons might include wanting to:
Reduce the amount of unplanned downtime at your site by optimizing equipment PMs.
Lower maintenance costs by reducing overtime pay and expediting costs.
Have greater control of facility budget due to planned costs as opposed to reactive costs.
Reliability Analysis Capabilities of your CMMS Software
Most maintenance organizations rely on some types of CMMS (Computerized maintenance management system) software to assist them in storing and analyzing the massive amounts normally generated by sites with sizable equipment populations. When selecting your CMMS software, it is important to clearly define the reliability metrics and reports you wish to create going forward. The software can only create the reports with data that is going to be available in the maintenance database. For example, if you expect to create Pareto graphs of equipment failure modes, the failure modes will need to be predetermined and the capability of entering failure modes needs to be available within your CMMS software. Since it will be costly to incorporate these database design changes after your software purchase, I recommend that these details are finalized early in your software selection and launch.
The right CMMS software allows you to determine:
Are equipment failure rates getting better or worse?
How well is your current preventive maintenance program working?
Which machines are failing the most?
What are the most common failure modes?
A major advantage of using an advance CMMS database is having the ability to drill down into the failure data. Take for example, the Pareto of the Machine Outage Causes. If you wanted to know which area experienced the most lubrication failures, you would drill down into the lubrication failure data to see if the failures were area specific or equally distributed across the site. If you found that there was a high frequency of failures in Unit A, then you might want to see of the failures were associated with a specific equipment type. However, to have this capability, you need to have the right database design and the data to draw from.
The right CMMS software capabilities will provide reliability metrics and reports on demand, which in turn will assist you in reducing the amount of work required to create useful reliability analysis reports. Having the right data, software, and predefined reports will greatly improve the likelihood of tracking your sitewide machinery reliability. Being able to detect major failure trends and specific machinery issues in a timely manner will allow you to judiciously direct the proper resources to your field problems.
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