Telemetry Context Mapping for Faster Asset Model Onboarding
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Solution Overview
Problem
The generation of digital models for asset systems is hindered by the slow process of identifying and accurately representing individual assets, leading to delays in detecting faults and inefficiencies, as existing methods require manual intervention and are time-consuming.
Innovation Solution
A method and system that process telemetry data using context discovery operations, including token interpretation, context translation, and neural network operations, to determine definitive mappings of data points to assets with confidence, thereby facilitating faster onboarding and generation of context data for asset systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual intervention is used to identify and represent individual assets, then accuracy of asset representation is improved, but onboarding time and processing speed deteriorate
Solution Approach 1:
The patent replaces manual mechanical processes with automated computational systems. Specifically, it uses telemetry data processing, context discovery operations, token interpretation, and neural network operations to automatically identify assets and generate digital models, eliminating the need for manual intervention while maintaining high accuracy through confidence scoring and definitive mapping determination.
Solution Approach 2:
The system enables self-service by allowing the asset identification and digital model generation process to occur automatically without human intervention. The context discovery system autonomously processes telemetry data, determines mappings, and generates context data, making the system self-sufficient in performing tasks that previously required manual asset representation.
2Reliability
If multiple context discovery operations are performed to determine definitive mappings, then reliability of digital models is improved, but device complexity deteriorates
Solution Approach 1:
The patent segments the complex task of asset identification into multiple distinct context discovery operations: token interpretation operations, context translation operations, and neural network operations. Each operation processes telemetry data independently and produces mapping structures with confidence values, which are then merged to determine definitive mappings. This segmentation allows the system to achieve high reliability through multiple verification steps while managing complexity through modular, independent processing components.
Data Source
AI summary
Various embodiments described herein relate to efficient and accurate context discovery of an asset system. In this regard, telemetry data comprising a plurality of data points associated with an asset system is received. The telemetry data is then processed in accordance with one or more context discovery operations. Furthermore, based on the processing of the telemetry data, for each context discovery operation, output data is determined comprising one or more mapping structures indicative of a potential mapping for a respective data point of the plurality of data points. The output data is processed, including identifying one or more definitive mappings. Context data is then generated for the asset system comprising the one or more definitive mappings of respective data points.