When you access the results of a Reliability Growth Analysis, if you see the data is trending in one direction until a certain point in time and then begins trending in another direction, you can examine what changed at the point in time when the trend shifted to determine the impact of those changes. In addition, if you make a strategy change and then examine whether the data worsens or begins to improve at that point, you can determine the impact of the strategy change. For example, a distinct change in a Mean Time Between Failures (MTBF) plot can identify the point at which improved maintenance strategies were put into place for a piece of equipment.
Similarly, when you observe data that is trending at the same rate over time without distinct changes, you can use those trends to predict the data's future behavior. For example, if you are tracking the cost associated with running a piece of equipment over a certain period of time, and the cost is consistently higher during winter months, you can predict how much more it will cost to run the piece of equipment in December than it will in July.
Trend charts generated by Reliability Growth Analysis can also show outlying events that may have had significant effects on the overall trend of strategy effectiveness. For example, a thunderstorm that results in a two-day power outage at a plant should not reflect poorly on a piece of equipment's reliability. Reliability Growth Analysis allows you to ignore these types of events.
You can perform a Reliability Growth Analysis and examine trend charts for one piece of equipment or location or a group of similar pieces of equipment or locations. For example, you may want to examine trends for one pump that is constantly breaking down, or you may want to examine trends for a set of pumps to detect any improvement after you installed a new maintenance strategy.