Smart Exception Rules Engine for Machine Data Analysis
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Solution Overview
Problem
Current machine diagnostics rely on manual review of exceptions generated by condition monitoring tools, which is time-consuming and prone to errors, especially when dealing with large fleets of machines and redundant exception reports.
Innovation Solution
A system that includes a data store, an analyzer with multiple analytics engines, and a rules engine to select and combine exceptions into 'smart exceptions' based on machine data and predefined rules, reducing the need for manual review by generating hierarchical combinations of exceptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of exceptions is performed by service personnel, then diagnostic accuracy can be maintained through human judgment, but the process becomes time-consuming and costly
Solution Approach 1:
The patent introduces an automated analysis system with analytics engines and a rules engine as an intermediary between machine data collection and human service personnel. This intermediary automatically processes machine data, generates exceptions, and creates smart exceptions, filtering and organizing information before human review. This reduces the time service personnel spend on manual review while maintaining diagnostic accuracy through the structured automated analysis framework.
Solution Approach 2:
The system enables self-service through automated exception generation and smart exception creation. The analytics engines automatically analyze machine data and generate exceptions without human intervention. The rules engine then automatically processes these exceptions to create smart exceptions, allowing the system to serve itself in the initial diagnostic stages, freeing service personnel to focus only on complex cases requiring human judgment.
2Reliability
If multiple analytics engines are used to analyze machine data, then comprehensive exception detection is achieved, but redundant exceptions are generated requiring additional manual review
Solution Approach 1:
The patent merges multiple exceptions into smart exceptions through the rules engine. When multiple analytics engines generate related exceptions, the rules engine combines them into a single smart exception that represents the hierarchical combination of underlying exceptions. This reduces the total number of exceptions service personnel must review while maintaining comprehensive exception detection, as the smart exception preserves information about all contributing exceptions.
3Measurement precision
If comprehensive machine data is collected and analyzed, then diagnostic accuracy is improved, but the volume of exceptions generated increases requiring more manual review
Solution Approach 1:
The rules engine extracts and combines related exceptions into smart exceptions, pulling out the essential diagnostic information from multiple individual exceptions. This extraction process reduces the volume of exceptions service personnel must review by consolidating related findings into unified smart exceptions, while maintaining diagnostic accuracy through preservation of underlying exception details.
Data Source
AI summary
A system for remote monitoring of a machine is provided. The system includes a data store to store machine data associated with an operation of the machine. The system includes an analyzer comprising a plurality of analytics engines to analyze the machine data. The analyzer selects one or more analytics engines based at least on one of machine data and a type of the machine. The analyzer is further configured to analyze machine data using the selected one or more analytics engines and to determine a plurality of exceptions. The system includes a rules engine to process at least two of the plurality of exceptions and determine a smart exception, wherein the smart exception is a hierarchical combination of the at least two of the plurality of exceptions. The system includes an interface to display a notification to a user in the event of a smart exception.