Thanks to significant advances in machine learning and Big Data analytics , root cause analysis can be performed using automated methods. These methods are unbiased and based purely upon historic and real-time data from the production floor.

Machine Learning is a subfield of artificial intelligence that focuses on developing and researching algorithms that learn from data. The algorithms exist in the form of models which are trained with historical data in a way that allows them to make predictions and decisions based upon new data .

Seebo automated root cause analysis gives process engineers the early causal events that lead to process disturbances

Anomaly Detection

To perform RCA using machine learning, we need to be able to detect that something is out of the ordinary, or in other words, that an anomaly is present.

The machine learning model is trained to analyze the equipment’s data output under regular “healthy” operating conditions. An anomaly can take the form of any pattern of deviation in the amplitude, period, or synchronization phase of a signal when compared to normal behavior.

The algorithm forms a prediction based on the current behavioral pattern of the anomaly. If the predicted values exceed the threshold confirmed during the training phase, an alert is sent.

Examples of anomalies detected using automated root cause analysis include:

Component failure

Abnormal process input parameters (eg. off-spec material composition)

Corrupt sensor values

Changes made to the control logic (eg. via the PLC)

Changes in environmental conditions

So, is this the end of industry expertise?

Automated root cause analysis reduces the overall dependency on expert knowledge, but it doesn’t diminish the value of on-site experts who are vital in monitoring, validating and managing the RCA process.

Additionally, automated root cause analysis is powered by machine learning and probabilistic graphical models that need to be trained in order to be able to perform inference. This makes on-site experience critical in ensuring a system that takes into account all relevant parameters.

Mutual Information

Another mathematical solution suited to RCA is the probabilistic strategy known as Mutual Information. In a manufacturing setting involving a high volume of data and parameters, this approach can be used to leverage complex statistical knowledge to search for patterns.

Mutual information is an investigative tool that aims to describe the mutual dependency between two random variables. When aiming to identify causal relationships – such as in root cause analysis – mutual information helps by identifying which information can be learned about one variable through data about another.