Mathematical Tools

PRANA is a predictive analytics system based on the MVSM algorithm (multivariate vector statistical model) with Hotteling maps, machine learning, and artificial intelligence.

When connecting equipment, the system creates a model of its digital model. It includes data on equipment operation in normal technical conditions. ROTEC experts point out the most relevant parameters, and the system automatically determines dependencies, trends, and reference values. The model in n-dimensional space describes the state of the equipment from a statistical point of view, i.e. it treats the equipment as a data set, not a physical object. Visually, the model can be represented as a plane, where the curvature reflects the value of the parameters and the dependencies between them.

When starting work, PRANA creates and constantly updates the second such model. Using advanced algorithms, PRANA in real time compares the state of equipment with the reference model and determines the differences between them. The degree of this difference forms the basis of the T2 indicator. This is an integral criterion for the technical condition of equipment. The deviation of the model from the standard is reflected in the T2 chart and alerts the ROTEC Situational Center experts of a negative trend. This is how PRANA reveals deviations 2-3 months before they can lead to equipment breakdown.

PRANA automatically calculates the effect of each parameter on the deviation from the reference model and displays in its interface 10 of the most significant causes of deviation. As such, the expert receives a complete picture of the status of the equipment: a general assessment of its technical condition, a forecast of its change, and a detailed description of possible assumptions.

Using this information and additional modules for data analysis, the expert identifies the cause of the deviation and makes recommendations for further actions for operational personnel.

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© PRANA: Predictive Analytics and Remote Monitoring System, 2023

15 Nikoloyamskaya St., Moscow,
109240, Russia