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We can not use Interval II to predict the second breakdown, because this data would not be available when creating the control chart in a real world scenario. If we would do this it would introduce data leakage. The sample of Interval I has a mean of As visualized we would estimate the process is out of control the 13th or 14th of July, shortly after having solved the first breakdown. Now, even if we would create a new control chart right after the clear outliers in the beginning of the graph, we would still have false warnings between the 13th and 14th of July.
When using the previous interval Interval I to make the upper and lower control limits used on Interval II , we get observations that are observed outside of the control limits! Interesting, none of them are at the initial breakdown and is therefore noise. For comparison, if we introduced data leakage, and used data from Interval II to set the Upper and Lower Control limit, we would still get observations outside of the control limits for sensor 47 - again not close to the breakdown.
This is illustrated in the following graph:. As a comparison if we continue the data leakage and add sensor 48, 49, and 50 to our control chart system, we will get observations that would be classified as outliers when using data from Interval II for setting the control limits.
If we remove the data leakage and use Interval I for setting the control limits we get 2, observations! In the real world, where data leakage can not exist, this would amount to being pinged or called 2, times which would indicate that the machine is broken even though it is not. We get a mean of We thereby get a lower control limit of 2. We get several warnings of the water pump not being in control. The warnings starts from the very beginning of this interval, meaning right after the water pump was maintained.
It also illustrates that control charts are sensitive to noise in the data - just a little change in how your machine behaves will give you a warning. A change in vibration from a change to a new bearing can create this noise but it do not necessarily indicate that machine is out of control nor that you should perform maintenance. If you did, you would do excessive maintenance and be throwing out a perfectly good bearing.
The previous sections have been using static control charts. It is however possible to using what is called dynamic control charts where the values for the upper and lower control limit are recalculate continuously. This is done by implementing a moving average and moving standard deviation.
The upper and lower control limits are sensitive to the outliers observed, providing a wide control limit in Interval II. It is important to remember, that any changes and abnormality happening within the control limit is wrongly considered as normal data when using control charts.
The control charts might be improved by using the trimmed mean and calculate the standard deviation from it. Some people suggest to calculate the mean of an interval, and plot this value in to the control chart as an observation.
We do not recommend this method, as you might miss important information by only having the mean e. Others have used multivariate variables to calculate the control limits, that will improve the model as well.
Disclaimer : As mentioned in the beginning of this blog post, there do exist a variety of different methods to perform condition-based maintenance, and those mentioned here, are only a few. This blog post have compared condition-based maintenance and predictive maintenance.
When using the control chart approach, we will observe a problem before the breakdown in all cases. As shown in the graph below, there is a natural change in equipment vibration over time. When using control charts, these natural changes will give you a warning, because the data starts looking different, and you will be alarmed of a process being out of control. This is why we believe the control chart to be very sensitive to noise in the data.
Now if you replace the bearing, or repair on the water pump every time you see a change in the data, you might not use the entire life-cycle of equipment, and spend too much money on maintenance. However, it is a pattern of a longer series we are interested in, if we are going to say something about the state of the equipment.
Condition-based maintenance via control charts can only say something about the observations that falls outside the control limits, whereas predictive maintenance using machine learning can find significant changes within the control limits as well. This is the biggest difference between condition-based maintenance and predictive maintenance.
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