Hello, I am new to analyzing failures of packaging machines and would appreciate your input on my approach. I am evaluating machine failures by recording data such as failure location (sensor) and time in a database. I have grouped sensors by sub-machine (packer, foil unit, etc.) to compare different machines and identify areas of strength and weakness. To do this, I plan to calculate MTBF (Mean Time Between Failures) for each machine and sub-area. To calculate MTBF, I will select a time period and count the number of failures per sub-machine during that period. By dividing the total duration of the selected period by the number of failures, I hope to obtain a meaningful MTBF for analysis. This could potentially lead to conclusions such as one machine having a higher MTBF in a specific area compared to another machine. However, I have some concerns. Should the duration of machine stops be taken into account in this analysis? Or is it more practical to use the MTBF method as a quick way to identify problem areas and then delve deeper into analyzing duration, etc.? Additionally, how do I determine a suitable period for analysis that provides statistically relevant results? I welcome any insights or suggestions on this matter. Thank you!
Hello Daan, Welcome to the Maintenance Forums. It is important to consider the time your equipment is not available due to maintenance or in standby mode. One key metric to track is Mean Time Between Failures (MTBF), calculated as: Reliability = Total Hours - Downtime Hours - Standby Hours / Number of Failures. In your case, a more relevant metric could be Production Efficiency, which measures the ratio of actual output meeting quality standards to rated output during operation: Production Efficiency = Actual Production / Total Hours - Downtime Hours - Standby Hours / Rated Capacity (Units per Hour) X 100%. Best regards,
Thank you, Larry, for your prompt response. I agree with your point about Production Efficiency, but I believe it may not provide a comprehensive view. While it indicates whether a machine is functioning well or not, it does not necessarily pinpoint the issues with an underperforming machine. This is why I am considering calculating the Mean Production Between Failures, which represents the average number of good products produced before a failure occurs. By combining this with the MTBF, I can get a more holistic view of our machine's performance and make comparisons between machines. The failures we experience are typically operational, such as paper jams or glue problems, and are usually short in duration. When a major breakdown occurs, our time tracking shifts from "production" to "maintenance," which is not factored into the MTBF calculation. Regarding handling multiple failure types per machine, for example, if failures F1, F2, and F3 occur 8, 3, and 12 times respectively in a 24-hour production period, the calculation would be as follows: MTBF F1 = 3 hours, MTBF F2 = 8 hours, and MTBF F3 = 2 hours. I appreciate your insights. Best regards, Daan.
Dear Daan, in response to your previous inquiry about determining a suitable analysis period for statistical relevance, it is recommended to choose a period that is at least a few times the Mean Time Between Failures (MTBF). For instance, if the MTBF is 8760 hours, the observation period should be at least 35,040 operating hours (4 x MTBF). By grouping similar equipment with the same duty cycle and operating environment, the calendar time can be reduced. For reliability discussions, it is important to consider the aggregate of failures. The failure rate of complex equipment is determined by the collective rate of each individual failure mechanism. Therefore, the MTBF can be calculated by dividing the total elapsed time by the total number of failures. This approach allows for a more comprehensive analysis of equipment performance. Thank you, R.
Larry makes a valid point regarding the optimal timeframe for conducting MTBF analysis. It's crucial to consider the duration of production downtimes and equipment malfunctions. By factoring in these variables, we can gain a more comprehensive understanding of reliability. In essence, MTBF is calculated by adding the Mean Time to Repair (MTTR) and Mean Time to Fail (MTTF), with MTTR measuring maintainability and MTTF measuring availability. This equation helps us assess the overall reliability of a system.
Hello and welcome to the field! Your approach seems like a good starting point. Considering the MTBF is quite intuitive and can provide you with insights on which machines fail more often. However, it's certainly worth considering the duration of the machine stops too. In fact, it's not just about how often the machine fails, but also how long it takes to get it back running. This could significantly impact productivity, even if the failure is not that frequent. Maybe you could look at MTTR (Mean Time To Repair) alongside MTBF, to give a more comprehensive picture. As for selecting a time period, I'd suggest a period that is long enough to capture a representative population of failures, yet short enough not to be affected by changes to the machines or their context (like modifications, major maintenance, etc). Hope this helps, and good luck with your analysis!
Hello! It sounds like you're on the right track with tracking sensors by sub-machine and calculating the MTBF. Duration of machine stops could indeed be relevant, particularly if longer stops indicate more severe issues. You might want to consider calculating MTTR (Mean Time To Repair) as well for a fuller picture. As for your question on determining a suitable period for analysis, I believe a period that captures a full maintenance cycle or two should give you statistically relevant results. Remember, you're analyzing not just the frequency of failures but the impact as well, so the more context you have, the better.
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Answer: MTBF stands for Mean Time Between Failures and is calculated by dividing the total duration of a selected period by the number of failures within that period.
Answer: It is important to consider the duration of machine stops in the analysis to get a more accurate understanding of the machine's performance.
Answer: To determine a suitable period for analysis, consider factors such as the frequency of failures, production schedules, and the need for statistically relevant results.
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