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Predictive Analytics
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 “The key is having the insight and knowledge to make data-driven decisions and support the journey”   

DPS in partnership with Arti Solutions is able to provide a state of art machine learning app evolving the way asset owners are managing their digital data and achieving significant improvements within operations.

 

For increased operational stability, including compliance and quality measures we offer “on the fly” analytics with a focus on:

  • Early alert generation,

  • Prognosis of off-spec processes and patterns being identified,

  • Root Cause Analysis, clustering & relation mapping,

  • Fault diagnosis including predictive health monitoring.

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ARTi-Solutions®  analytics architecture

Inspections

Calculation of the so-called "operation modes" that require clustering using Principal Components extracted from the entire set of variables using highly sophisticated density-based clustering, such as DBSCAN. Commonly used clustering, such as k-means variance, scale up to even cluster size and flat geometry. Our solution scales up to non-flat geometry and uneven cluster sizes that are physically realistic for the industry problems. In-house development has lifted the problem of limited size requirements of the original DBSCAN algorithm and ARTi-solutions offers usability for unlimited size of records.

Diagnosis

A suite of different approaches for fault / anomaly detection that can be used appropriately in a complementary (hybrid) manner to build case-specific rules. The first approach offers results based on the entire set of variables and records and automatically calculated thresholds that classify whether a record is a fault or not without user intervention. In addition, single variable anomalies are detected based on (linear and non-linear) predictions of what the upcoming value should be. If the measurement differs unexpectedly from the prediction, then this measurement is flagged as "anomaly".

Root Cause Analysis

Based on all variable anomalies for short, medium and long time-frames between anomaly events and how a preceding event is affecting the following ones. Once the calculations are completed, the correlations between variables with respect to outliers (anomalies) is produced. Then, the highly correlated variables are grouped. A graph (Causality Graph) is produced based on temporal precedence of events.

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