alignment, oil change out, piping modification, etc.
•Plan a shutdown for repair (e.g., full machine overhaul if needed).
•Shut down immediately for balancing, alignment, oil change out, piping modification, etc.
•Shut down immediately for repair.
The Potential to Failure Time Interval
For most classes of rotating equipment, there is usually a time interval between the time a failure can first be detected until the time a catastrophic failure occurs. This time interval was given the name the Potential to Failure, or P-F, interval by J. Moubray. Potential to Failure (P-F) curves graphically display the failure time cycle and measurement techniques that can be used to detect machinery failures prior to realization of a catastrophic failure.
Proactive strategies, such as vibration analysis, lubrication analyses, and IR imaging all focus on keeping machines on the far left side of the P-F curve, or early in the failure cycle (Figure 4.1). The times in this chart are for illustration only and should not be considered valid for all cases. The ability-to-detect-early failure is a hallmark of top performing maintenance programs. Failure to maintain vigorous predictive maintenance programs will doom maintenance organizations to operate reactively instead of proactively.
It is important to understand your machine’s P-F curve when making decisions on how to proceed. For example, if you are early in the P-F interval (vibration), then there is probably ample time to perform further tests and/or properly plan a repair. However, if the machine is generating audible noise, there is probably significantly less time, e.g., a week or two, to react before failure. However, if the machine’s is hot to the touch in the vicinity of the bearing, or bearings, or there is visible smoke, the game is over—there is nothing left to do but prepare for an emergency shutdown. It may be useful to generate a P-F curve similar to the one shown in Figure 4.1, as a visual aid to management, so that they can better understand where you think you are in the failure cycle and how much time there is left until failure occurs.
Figure 4.1 P–F Interval Curve
Takeaway: When making a decision, understand where you are in the P-F interval
Before making an informed decision, you need to have answers to the following questions:
a.Do you have enough machine information to effectively analyze it? If not, continue your research.
b.Based on this guide or the equipment manufacturer’s guidelines, are any of the measured machine variables at dangerous or destructive levels? If the answer is yes, your choices are to either shut down, switch to the spare, or attempt to change operating conditions to see if things improve.
c.Is there sufficient field data to make an informed decision? If not, collect more data.
d.Is this machine spared? If it is spared and conditions warrant it, switch to the spare.
e.Is this machine critical enough to warrant additional, more sophisticated tests? This is an economic decision.
f.Is there time to run additional tests? As long as levels are at manageable levels and the economics warrant it, you can continue testing. A good analyst knows when it is no longer economical to continue testing.
g.Can the machine be shut down temporarily for balancing, alignment, oil change out, piping modifications, etc.? This may be your only option for unspared machines. Note: Make sure any maintenance-related outage is well planned before shutting down.
h.Are there sufficient analysis points, such as pressure taps, oil sample points, etc., to get a clear picture of what is happening? If not, the machine may have to be shut down to add these points at some convenient time in the future.
Now that you have all your machine information and machine data, and have answered the questions above, sit down with the machine owners/operators and develop a plan of action that everyone agrees with. Be sure your decision team includes representatives from operations, maintenance, reliability, and management.
“In God we trust; all others must bring data.” W. Edwards Deming,American statistician, professor, author, lecturer and consultant
W. Edward Deming’s quote above illustrates how important he thought data was when attempting to analyze processes. Decision makers always want to know if their processes are steady or changing, and if they are within required tolerances. Similarly, machinery data is vital when trying to state your case about what should be done to correct an undesirable situation. How can you possibly convince a manager to shut down and repair a critical machine without a reliable and convincing set of data? Remember, however, that raw data alone is not information; it is only by carefully analyzing data that we can hope to convert raw data into helpful information. Data analysis can allow you to paint a picture of a machine’s present condition and predict what might happen in the future. The goal in this chapter is to present a common sense approach to data analysis through the use of examples.
Humans are excellent at recognizing and interpreting patterns. (Some would say we are a little too good at seeing celebrity faces, animals, secret symbols, etc., in grilled cheese sandwiches, potato chips, and water stains. Also, there are those who read all kinds of secret messages in magazines, revered documents, and books.) We can quickly spot if the data are steady, trending upward, erratic, or cyclical, to name a few common patterns. The first challenge is to objectively study critical data sets and determine if they are normal or not. Below we will discuss the most commonly encountered data relationships and what they might mean.
Data sets are any group of measurements from a monitoring system—such as a distributed control system (DCS) or a supervisory control and digital acquisition system (SCADA)—which has been archived in a digital format using a data acquisition system, along with a time variable that allows variables to be analyzed and compared. A lion’s share of process and machine-related data that we analyze is static data. This type of data has only amplitude. Typically, static data is analyzed in one of three ways:
1.A single data set is plotted versus time to spot trends.
2.Several data sets are plotted together to see how they compare to one another.
3.Two data sets are plotted one versus the other to see if there is a relationship, i.e. correlation, between the two.
Let’s look at a few commonly encountered data patterns. (In the following analyses, we will assume the data is reliable and accurate, i.e., the appropriate sensor is installed in the right machine location and has been properly calibrated. In the real world, we should always question whether data is dependable.) In theory, there are countless data patterns
