
The Exponential Distribution is commonly used to model waiting times before a given event occurs. If this waiting time is unknown it can be considered a random variable, x, with an exponential distribution. It's also used for products with constant failure or arrival rates. The data type is continuous.
Generally, if the probability of an event occurs during a certain time interval is proportional to the length of that time interval, then the time elapsed follows an exponential distribution.
The Exponential Distribution uses a constant failure rate (lambda) and is the only distribution with a constant failure rate. It is a valuable tool to predict the mean time between failures and plays a significant role in Predictive Maintenance, Reliability Engineering, and Overall Equipment Effectiveness (OEE).
When the downtime can be predicted, not only can it control costs, but managing labor becomes easier and machine OEE improves. All three components of OEE (Availability, Performance, Quality) could benefit from effective probability information from use of the PDF).
An understanding of the probability until a failure or a particular event can be extremely powerful when this information is in the hands of decision makers. While we all try to read the crystal ball the best we can, predictive modeling can add substance for a decision.
Applications include modeling :
1) Mean arrival time of planes at a airport
2) Failure rate of electronic devices
3) using the mean time of light bulb, calculate probability of life at specified hours.
4) predicting how long a machine will run before unplanned downtime
5) how long until the next email comes through your Inbox at work
6) time until next rainfall
This is a continuous probability distribution function with formula shown below:
t = time
Lambda = is the failure or arrival rate which = 1/MBT, also called rate parameter
MBT = the mean time between occurrences which = 1/Lambda and must be > 0
Median time between occurrences = ln2 / Lambda or about 0.693/Lambda
Variance of time between occurrences = 1 / Lambda^2
The Exponential Distribution has a Poisson Distribution when:
1) the event can occur more than 1 time
2) the time between two successive occurrences is exponentially distributed
3) the events are independent of previous occurrences
Both the Poisson Distribution and Exponential Distribution are used to to model rates but the later is used when the data type is continuous.
If the random variable, x, has an Exponential distribution then the reciprocal (1/x) has a Poisson Distribution.
The Poisson Distribution is applied to model the number of events (counts) or occurrence per interval or given period (could be arrivals, defects, failures, eruptions, calls, etc). This models discrete random variable.
The Exponential Distribution is applied to model the mean time (such as waiting times) between occurrences, time is a continuous variable.
Shown below are graphical distributions at various values for Lambda and time (t). The formula in Excel is shown at the top of the figure. They each take on a similar shape; however, as Lambda decreases the distribution does flatten.
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