Diagnose dependencies and root causes

A user can study process data iteratively and in a very effective way. With Wedge it’s possible to use advanced algorithms to better understand cause and effect relationships within the process; get the answer to questions why does it behave like this.

Identify relationships between measurements

In Wedge user can easily study cause and effect relationships within the process by selecting a target measurement and asking the tool for similarly behaving measurements. From the result a user can continue to more detailed analysis.

Wedge lists best-correlating measurements with target measurement

Trace the source or consequence of abnormal behavior of the process

In case of process disturbance, it is vital to rapidly detect the root cause. In Wedge user can conduct ad hoc analysis: select target measurement and ask the tool tell “why?”

Often, due to process delays, finding the cause can be challenging. With Wedge, a user does not have to worry about process delays, as the pattern recognition tool can compensate those automatically.

Kuvateksti: Wedge lists best-correlating root cause candidates with target measurement, and process delay. 

It is also possible to use pattern recognition to study consequences of a process change. This is an extremely good way to increase understanding of process behavior.

Kuvateksti: Wedge lists best-correlating consequence candidates with target measurement, and process delay. 

Predict and avoid process disturbances

Wedge can also predict the future. All predictions are based on a process model defined by customer.

Example 1: Tower level prediction based on recent history data (blue area = future)

Example 2: Remaining Useful Life (RUL) for maintenance planning purposes (red line = current situation, green line = the most similar behavior in the history)

 

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