External Object parameter(s); these are the “environmental” circumstances that an object has to run in like climate, geotechnical or other influences.
Pythia defines “Process Behavior” as the “Presentation of the process data during a certain timeslot”.
Pythia uses all kinds of data that is produced in a process or service during a certain timeslot. It takes the measurement and timestamp of a process parameter (like delivery time, cooling temperature or pressure) and stores it in its database. Pythia combines and determines the correlation of the internal and external parameters to make it possible to take all possible causes in effect. All this information together forms the “Behavior” of an installation, a service or an object.
Pythia; Failures And Events:
In general, a failure is considered to be a "negative" event; it causes a "loss". All events are caused or triggered by one or more specific combinations of situations or states that is a system in. On the other hand; a positive event is a "gain". The purpose of the installation or process. Pythia does not distinguish between “Loss” or “Gain”; it just uses “Events”.
Any type of production facility is thought of as being an "Installation" or a “Service”. So, by generalizing this as a concept, it is possible to create a model of virtually every kind of productive organization, regardless the real (or virtual) product/service they deliver.
During production cycles, every installation facility is in any sort of "state" and the most important ones are closely monitored, because they are the main source of information that tells you whether the production cycle is "in control" and the produced goods are within acceptable quality standards. In general, most of this information data is gathered by sensors, but data may also come from (historical) data(bases).
This brings us into the world of "Tolerances".
Tolerances are everywhere and always present. They are the mean measurement of "Quality" because they define whether a situation, product, good or services is acceptable to be used by or sold to always more demanding customers.
Tolerances have to ensure that all the produced goods are continuous, consistent and reliable without variances outside these tolerances.
But processes have tolerances to! Within Pythia, every process parameter has its own tolerances, and these can individually be set and maintained. This enables Pythia to monitor whether a process is “in control” or not. When subsequent readings are reaching the tolerance borders Pythia will detect this and will issue warnings to be send out before the state of event (failure) is reached.
The chain of steps.
Customers and users regard a product as "Acceptable" when they receive their purchase within certain limits of acceptance. What these "Limits of Acceptance" are is greatly depended of the nature of the client, and the designated use of the services or goods they expect, buy or use. But the client is always the last part of the chain. This means that all the steps before that are due to influence the satisfaction of the client. Thus all these steps can be the cause of quality degradation in the eye of the client.
And this is the point that Pythia kicks in!
Every step in any production process can be identified as an (more or less) individual set of activities in the production/service system. And all these individual parts have their own "Key Production Indicators". These indicators are the items that define the "state" that this step is in. By guarding these indicators and by controlling their values (inside or outside tolerances), it is possible to ensure that every production/service step is performed well. But what happens when the quality of an outcome of a step is good, but the end control of the product says "NO!" Obvious there is a problem when individual steps deliver results that appear to be good, but in the end turn out to result in poor end products or services. This needs a Root Cause Analysis.
Root Cause Analysis is the art of recognizing what is the cause and effect of any combination of "State" and "Event" (regardless good or bad) in any type of installation or service.
Because this application records all of the pre-defined "Key Production Parameters" (Internal and External) and also records all the failures or any deviation of produced products, it creates the possibility of analyzing the steps from start to end, i.e. the behavior of the process.
Pythia; Pattern Recognition, catch the event:
So what you do is select an "Event of Interest" and determine the period of measurements that precede this moment. In this way Pattern Recognition makes it possible to detect certain issues, due to the fact that the combination of parameters lead to this failure. So, when you have selected an "Event of Interest", Pythia has to create the pattern of measurement points that go in advance of the moment of event. Now Pythia sets up the pattern. This needs only to be done once for any "Event of Interest" and can be done as a function within the application by selecting the event, determine the pattern points and the treshold factor (between 0 and 1) and then generate the pattern. Now, when running the analysis again, Pythia will re-examine the whole timeslot and sets a marker on every moment the pattern is found. This enables the process analyst to detect weather the event has been occurred earlier and is thereby able to reconstruct the circumstances under which an event will appear.
Pythia; Predictitive Analytics, real-time and on-line:
Predictive analytics is the science about being able to foresee the (near) future, based on historical and new incoming data in a process and its behaviour.
Pythia performs predictive analytics by “learning” from the past by comparing each new incoming dataset by the patterns in the database. Pythia has to connect to the installation’s sensors directly thru a so-called API or a connection to a SCADA system. By evaluating each new incoming dataset from the sensors against the patterns in the event database, Pythia is able to detect events arising as they come in, therefore Pythia is able to “see” and recognize the probability of an known event to happen. We are developing the function in which Pythia is also able to determine when there is a deviation from the “normal” process behaviour, even when no pattern is present.
Pythia; Root Cause analysis, learn from the past:
Root cause analysis (RCA) is a method of problem solving that tries to identify the root causes of faults or problems. A root cause is a cause that once removed from the problem fault sequence, prevents the final undesirable event from recurring. A causal factor is a factor that affects an event's outcome, but is not a root cause. Though removing a causal factor can benefit an outcome, it does not prevent its recurrence for certain.
Because Pythia has all the relevant data in its database, it can present the analysts with the chain of events that preceded the “Root Cause”. The next manager in line can then take appropriate actions to solve the problem(s).
Pythia; Predictitive Maintenance:
Predictitive Maintenance techniques are designed to help determine the condition of in-service equipment in order to predict when maintenance should be performed. This approach promises cost savings over routine or time-based preventive maintenance, because tasks are performed only when warranted.
Pythia performs an analysis in (semi)real time and online by reading all the relevant (sensor)data from sensors and present detailed information about the status that a process is in and weather any event might occur.
Pythia; Reliability Centered Maintenance:
Reliability Centered Maintenance (RCM) is a process to ensure that assets continue to do what their users require in their present operating context.
It is generally used to achieve improvements in fields such as the establishment of safe minimum levels of maintenance, changes to operating procedures and strategies and the establishment of capital maintenance regimes and plans.
With Pythia you can implement RCM to minimize maintenance costs. Pythia informs the maintenance officer that certain processes are at risk because the behavior of the installation, or even an individual object, is triggering a warning signal.
Pythia; Process Stability and Process Capability: