Figure 5.1 Example trends
Flat Trends
The simplest types of trend are the flat trends as seen in Figure 5.2. The “low” trend is the type of trend we would all like to see. The amplitude is steady and about 40% of the alarm value. We can conclude that this is a well-applied, healthy machine with no indications of an impending problem.
The “high” trend in Figure 5.2 shows a flat data trend that exceeds the recommended alarm level. The first question that should be asked is: Are these high levels a recent occurrence or have levels been high forever, or at least since anyone can remember? If the answer is high levels are a recent occurrence, the reason for the sudden change must be investigated. If the answer is high levels have been around for a while, then one of the following reasons for the consistently high trend must be considered:
Figure 5.2 Two examples of steady state trends
1.Design problem
2.Assembly problem
3.Installation problem
4.The machine is being operated in a way it was not intended to be operated.
5.The machine has been misapplied.
In order to determine which of these might be the cause of the high readings, it is important to have start-up data. If amplitudes were high immediately after start or commissioning and have remained flat, then one of these root causes may apply.
Trends with Step Changes
Another version of a flat trend is a trend with a step change, as seen in Figure 5.3. Step change plots can either illustrate a step change up or a step change down. Whenever a step change is seen, we can deduce that something has changed suddenly inside the machine or around it.
Figure 5.3 An example of an upward step change and downward step change in data levels
Here are a few machinery or process-related reasons for an upward step change:
•Something has become lodged in an impeller, resulting in a sudden imbalance.
•An oil cooler has become suddenly plugged or fouled, leading to higher bearing temperatures.
•A downstream process strainer partially plugs, leading to a higher pump discharge pressure.
•A reciprocating compressor valve fails, causing a sudden rise in compressor discharge temperature.
Here are a few machinery or process-related reasons for a downward step change:
•An upstream process strainer partially plugs, leading to a lower pump flow.
•A cold front blows in, causing sudden oil cooling and subsequently lowers bearing temperatures.
•A reciprocating compressor valves fails due to gas stream particulates, causing a sudden drop in compressor flow.
•A sudden drop in centrifugal compressor speed reduces vibration levels as it falls below a critical speed.
When dealing with step changes, it is useful to talk about percent changes in value observed. Let’s start with the simple equation below to define the term “alarm margin” or AM.
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(4.1) |
where a is the alarm level, c is the current measurement, and n is the normal level. The alarm margin provides a quick and easy way to determine how much margin there is between the present measurement and level where action will be required. Let’s look at a few examples to clarify the use of this equation. Let’s first assume we are looking at vibration. The normal level is 0.15 i.p.s., the alarm level is 0.5 i.p.s., and the current vibration reading is 0.35 ips. Plugging these terms into the alarm margin equation, we get:
This result means 42.9% of the alarm margin remains before reaching the alarm point. If we now assume c=0.45 ips, then the remaining life is:
Here, only 14.3% of the “normal” alarm margin remains. An AM of 100% means you are operating at a normal level and you have 100% of the alarm margin remaining. Working with these two examples, suppose the remaining life changes from 42.9% to 14.3% in just one week. In this case, it’s easy to see you probably should be planning a repair very soon. However, if this same change in condition takes a year; you probably have a week or two to put a repair plan together before having to shut down the machine for a repair. A good rule of thumb for step changes is to investigate any step change that represents a 25% change in the alarm margin.
Upward and Downward Trends
The next commonly encountered trends are upward and downward trends as seen in Figure 5.4. These types of trends provide clear indications that something is changing. The main difference between step changes and upward and downward trends is that upward and downward trends occur over weeks, months, or years whereas step changes are sudden events that occur over minutes, hours, or days.
Here are a few machinery or process-related reasons for an upward trend:
•A pump is gradually eroding, fouling, or corroding, which leads to a change in rotor balance.
•Internal centrifugal compressor clearances are wearing over time and causing a gradual increase of steam consumption to maintain the required flow. This results in an upward trend in driver speed over time.
•A 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.,