Adaptive Telemetry for Predictive Utility Equipment Response
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems lack a centralized monitoring and action mechanism to effectively correlate and analyze data from diverse backend systems in utility establishments, leading to inefficiencies and missed opportunities for optimizing power generation and distribution.
Innovation Solution
A Telemetry Master (TM) system that processes data entries from various backend systems, employs predictive models to determine next best actions, and automatically initiates responses to alerts, utilizing machine learning and natural language processing to optimize operations and improve efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data from diverse backend systems is collected and stored in data centers, then the quantity of available data increases, but the complexity of correlating and analyzing this data increases
Solution Approach 1:
The patent introduces Telemetry Masters as intermediary components that mediate between diverse backend systems and analysis processes. Each Telemetry Master collects, correlates, and pre-processes data from multiple backend systems before forwarding to data centers, simplifying the overall data correlation complexity while maintaining data quantity.
Solution Approach 2:
The patent segments the centralized data correlation function into distributed Telemetry Masters at different levels of the system hierarchy. This segmentation allows local correlation of data subsets, reducing the complexity burden on central data centers while preserving comprehensive data analysis capabilities.
2Productivity
If manual monitoring and response mechanisms are used for backend systems, then system complexity remains low, but productivity and response time deteriorate
Solution Approach 1:
The patent implements self-service automation where Telemetry Masters autonomously monitor backend systems, detect anomalies, correlate data patterns, and initiate corrective actions without human intervention. This automated self-monitoring significantly improves productivity while the modular architecture manages system complexity through standardized interfaces.
Solution Approach 2:
The system performs preliminary data correlation, anomaly detection, and action planning automatically before human operators need to intervene. Telemetry Masters continuously analyze data patterns and prepare response strategies in advance, improving operational efficiency while maintaining manageable complexity through automated routines.
3Loss of time
If automated action initiation is implemented, then response time to alerts improves, but the complexity of the system increases
Solution Approach 1:
The system performs preliminary data correlation, anomaly detection, and action planning automatically before human operators need to intervene. Telemetry Masters continuously analyze data patterns and prepare response strategies in advance, improving operational efficiency while maintaining manageable complexity through automated routines.
Solution Approach 2:
The patent implements feedback loops where Telemetry Masters monitor the effects of automatically initiated actions and adjust future responses accordingly. This feedback mechanism improves response effectiveness over time while the standardized feedback interfaces keep system complexity manageable through consistent data exchange protocols.
Data Source
AI summary
A telemetry master (TM) system provides automatic monitoring and maintenance of equipment by automatically generating predictive actions of a certain confidence threshold to backend systems which maintain a full duplex communication channel with the TM system. Data entries including context information that are generated by applications corresponding to the backend systems during the functioning of the backend systems are collected, analyzed and actionable items such as alerts, alarms of particular messages cause the telemetry master system to generate a predictive action of a minimum confidence threshold using a pre-trained first data model. A second data model is employed to identify particular equipment from the backend systems that is to implement the action. Results from implementing the action are collected and again used to train the data models. Certain applications can include intelligent agents which enable automatic execution of the actions.