Automated Alert Diagnosis via Multi-Feed Correlation
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Solution Overview
Problem
Operators in various fields face challenges in detecting patterns and trends in high-frequency sensor data to diagnose faults and make timely decisions, often overwhelmed by false-positive alarms and lacking the training or time to interpret data effectively, leading to potential catastrophic failures.
Innovation Solution
A system that analyzes primary and confirmatory data feeds to identify features, validates alert conditions, and generates operator text through a decision tree, providing a concise and reliable linguistic diagnosis and recommendations to guide corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If operators manually analyze high-frequency sensor data to detect patterns and diagnose faults, then diagnostic accuracy may improve, but operator workload and response time deteriorate due to overwhelming data volume and false-positive alarms
Solution Approach 1:
The patent introduces an automated data analysis system that acts as an intermediary between sensor data and operators. This system processes high-frequency sensor data, applies correlation algorithms to validate alerts, and generates structured operator texts, thereby reducing the burden on operators while maintaining diagnostic accuracy.
Solution Approach 2:
The patent replaces manual operator analysis with automated computational systems. The system uses algorithms to detect patterns, correlate data from multiple sources, validate alerts, and generate diagnostic texts, substituting human cognitive processing with automated mechanical/computational processes.
2Reliability
If operators manually interpret sensor data to diagnose faults, then diagnostic quality may improve, but response time deteriorates due to lack of training and time constraints
Solution Approach 1:
The patent performs preliminary data processing, pattern detection, and alert validation automatically before presenting information to operators. The system pre-analyzes sensor data, identifies potential issues, and prepares structured diagnostic texts in advance, enabling operators to make faster decisions without compromising diagnostic quality.
Solution Approach 2:
The patent replaces manual diagnostic interpretation with automated analysis systems that process sensor data, apply diagnostic algorithms, and generate structured texts. This substitution eliminates training limitations and time constraints associated with human operators while maintaining or improving diagnostic quality.
3Loss of information
If the system generates detailed text descriptions for every alert condition, then information completeness improves, but operator overload worsens due to excessive information volume
Solution Approach 1:
The patent extracts only the most relevant and critical information from sensor data to generate operator texts. The system identifies key diagnostic features, validates alerts through correlation analysis, and presents only essential information in structured formats, eliminating redundant details while maintaining information completeness for decision-making.
Solution Approach 2:
The patent applies different levels of detail and formatting to different types of information based on their importance. Critical diagnostic information is highlighted with specific formatting and structure, while less important data is minimized or omitted, ensuring operators receive appropriately weighted information without overload.
4Reliability
If the system validates alert conditions through multiple data feeds and correlation analysis, then false-positive reduction improves, but processing complexity worsens
Solution Approach 1:
The patent implements a universal correlation analysis framework that can validate alert conditions using multiple data feeds and processing methods. The same correlation algorithms and validation logic are applied across different sensor types and alert conditions, managing complexity through standardized multi-functional processing rather than separate specialized systems.
Data Source
AI summary
Methods, apparatuses, and computer program products are described herein that are configured to generate an operator text in response to an alarm that is either received from an alarm or alert system or that is self-generated based on an analysis of one or more data feeds. The method of an example embodiment may include determining whether an operator text is to be generated in response to a received alert condition by performing data analysis operations comprising: analyzing, using a processor, a primary data feed and at least one confirmatory data feed to identify one or more features; and determining based on the detection of a feature in the primary data feed or the at least one confirmatory data feed satisfies at least one predetermined constraint. The method may further include generating an output text that is displayable in a user interface that describes at least a diagnosis for the feature that satisfied that at least one predetermined constraint.