Diagnose dependencies & root causes

You can study process data iteratively and in a highly effective manner. Wedge makes it possible to use advanced algorithms to gain better understanding of cause-and-effect relationships within the process. Get the answer to questions about why it’s behaving this way.

Identify relationships between measurements

In Wedge, the user can easily study cause–effect relationships within the process by selecting a target measurement and asking the tool about measurements that are behaving similarly. From the results, the user can proceed to more in-depth analysis.

Wedge lists the measurements that correlate most with the specified one.

Trace the sources/consequences of abnormal process behavior

In cases of process disturbances, it is vital to determine the root cause rapidly. With Wedge, the user can conduct ad hoc analysis, selecting the target measurement and asking the tool “why?”

Often, process delays can render it challenging to find the cause. With Wedge, the user doesn’t have to worry about process delays, since the pattern-recognition tool can compensate for them automatically.

Wedge lists the root-cause candidates that correlate most strongly with the target measurement and process delay.

Also, pattern recognition can be used to study consequences of a process change. This is an extremely good way to increase understanding of process behavior.Wedge lists the best-correlating consequence candidates with regard to the target measurement and process delay.

Predict & avoid process disturbances

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

Left: Example of tower level prediction based on recent history data (blue area = future). Right: Example of predicting the remaining useful life (RUL) for maintenance planning purposes (red line = current situation, green line = the most similar behavior over history).


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