Reading between the lines
May 1st 2009 It is all very well attempting to make spot diagnoses of bearing wear but, according to John Atkinson of C-Cubed, the only true way to determine when a bearing is due to fail is to trend the readings over a period of time.
It is the relative change in the readings that gives the clearest indication of an impending problem. For example, consider the trend of bearing noise in the FreeTrend screen shot in Figure 1. The trend plot clearly indicates that this particular bearing was suffering increasing damage with steadily increasing Bearing Damage Units (BDU) right up to the point where they peaked at 355 BDU immediately prior to the bearing being changed. (100BDU = 1g RMS high frequency vibration).
By comparison, the new bearing can be seen to have a much lower bearing noise figure of 32BDU. However, what is most significant is that the trend of these new readings is now perfectly flat, indicating that this is the normal level for this particular bearing.
Armed with this information, it is now possible to set some useful alarm levels and it is here that trending really comes into its own. The FreeTrend plot of the bearing shown in Figure 2 shows a steadily increasing level of bearing noise, despite the fact that the readings show a high degree of variability with several of the readings lying well outside the underlying tend. However, the underlying trend is immediately obvious to the human eye and illustrates the obvious power of trend plots when dealing with highly variable data.
Based on the readings from the bearing noise shown in figure 1, a decision was taken to add warning and alarm levels at 100 and 200BDUs respectively. Now, when FreeTrend's built-in Trendline function is used to display the underlying trend of the graph, it is possible to predict when this particular bearing is likely to reach any given level. In the example shown in Figure 2, a non-linear trendline is predicting that the alarm level of 300 BDUs would be reached within the next two months. (In fact this particular bearing was changed at the next shutdown of the machine).
When attempting to diagnose a bearing problem, it is always best to look at as much information as possible to obtain the clearest picture of exactly what is happening. Some of the factors that could be taken into account fairly obviously indicate temperature, given that it is well known that worn bearings often exhibit increased running temperatures. Thermographic images are particularly useful here in that they can help to identify whether the increase in temperature is localised to a particular bearing. Other measures could include grease analysis, ultrasound measurements, electrical current, voltage levels, and so on.
It is particularly helpful if these parameters can be simultaneously displayed with the vibration data (and automatically generated in the same report!) and condition monitoring systems are increasingly adding features that allow this. For example, the FreeTrend plot in Figure 3 shows a temperature trend on a machine bearing with clearly displayed warning and alarm levels (colour coded amber and red respectively). This additional information is not only a very useful indicator when embedded in status tables and reports, but shows an increase in the running temperature can sometimes show up long before the alarm threshold is exceeded when the data is viewed on a graph. This highlights further the importance of graphical display and the fitting of trendlines to the data.
The moral is: if you can measure it, do so! Even better, if you can trend it, you will have started down the road to really getting things under control by establishing your baselines.
Bearing vibration varies significantly from machine to machine and what is a bad level on one machine can easily pass as good on another. As long as you stick to the same measurement points, you will be able to use the relative trend of the vibration and other data as a very good indication of the health of a bearing regardless of its actual levels of vibration, temperature and so on.
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