gradual fouling heat exchanger is causing high oil temperature and hence higher bearing temperatures.
•Gradual wear in a sleeve bearing leads to a gradual increase in shaft vibration.
Figure 5.4 An example of an upward trend and downward trend in data levels
Here are a few machinery or process-related reasons for a downward trend:
•Internal pump wear is causing a gradual drop in flow.
•Gradual plugging in a downstream reactor bed is generating higher pump backpressure and, hence, lower pump flows.
•An expected gradual drop in ambient temperatures due to change in the season leads to a predictable drop in bearing temperatures.
•Vibration levels on a variable speed drive gradually drop as pump flow demands drop.
Data Sampling
A commonly asked question is: How frequently should data be sampled to see what is going on? The first question to ask is: What is the sampling frequency? DCS and SCADA system have the capability of sampling and storing data every few seconds or less. However, as time passes, the stored values are either lost completely or averaged so that they may be archived. Data acquisition systems can average older data over longer and longer intervals in an attempt to compress it into more compact forms, i.e., first hourly then daily, then weekly, then monthly, etc. Therefore, the sooner you analyze the data, the better your chances are of getting high time resolution data for analysis.
Manually collected data are usually acquired on a set collection interval. For example, vibration data are typically collected quarterly, monthly, or weekly whereas pump efficiency data are collected annually. This means you are often stuck with a paucity of data to work with. If these values are critical to the plant, then a continuous means of measuring and storing this data should be considered.
For long-term trends—such as those caused by gradual internal wear—weekly or monthly data is probably fine for analysis purposes. However, for rapid events that may occur during start-ups or shutdowns, the shortest data collection interval is preferred. This means that you need to get to the DCS or SCADA data and extract the pertinent data before archiving occurs.
Cyclic trends
Cyclic trends are data trends that appear to oscillate repeatedly over some time interval. The cyclic example shown in Figure 5.5 is a three-year temperature trend of noon air temperatures in a California vineyard. It is easy to see that temperatures oscillate from about 40°F in the winters to almost 110°F in the summers. The trick with cyclic trend is to analyze the data over a time period that significantly exceeds the characteristic cycle period so that multiple cycles may be observed.
Two common causes of cyclic trends are 1) seasonal or daily changes in ambient temperatures and 2) load changes due to seasonal process demands. Another special example of a cyclical trend is a process that operates in batch processes that last over a prescribed time interval, such as a Coker Unit where cycles last between about 24 hours. Here you should see obvious cyclical trends with a minimum of a 24–hour period.
Is it the Machine or the Process?
When a sudden change or trend in data is spotted, your first question should be: Is it the machine or the process? The real questions should be: Is the machine simply responding to a normal change in process conditions?
Figure 5.5 Daily noon air temperatures at a California vineyard over time
Is the process upset or deteriorating? Or, is the machine deteriorating? Before focusing in on a machine problem, interview the process operators to see if either flows, pressures, temperatures, product compositions, etc., have changed or if there is a known process issue, such as plugged reactor bed or fouled cooler. Once you have determined process conditions are normal and steady, you can begin analyzing machine data.
Another popular and frequently used data analysis method is correlation analysis. There is a correlation between two variables if a statistical relationship exists between them. The easiest way to see if a potential correlation exists is to plot one variable versus the other in Excel® or other graphing software. In Figure 5.6, gas turbine output power is plotted versus the ambient air temperature. This plot was generated using the “X-Y Scatter” function of Excel®.
Figure 5.6 illustrates a negative correlation between gas turbine power output and ambient air temperature. It is easy to see that there seems to be a clear relationship between gas turbine output power and the ambient temperature. Note that this correlation is a negative one—as the air temperature rises, the output power falls. Most readers probably already know this correlation exists. The point of this example is to show what a correlation looks like and how they are generated.
Figure 5.6 An example of a data set with a negative correlation, m = −0.2012 and R2 = 0.938
Table 5.1 What R2 Values Mean
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