Machine Diagnostic Logic Gate for Signal Validation
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Solution Overview
Problem
Current automatic diagnostic engines for machine condition monitoring, both model-based and statistics-based, face limitations in accurately identifying machine defects, particularly when encountering unfamiliar parameter conditions or failing to detect anomalies that do not match specific failure patterns.
Innovation Solution
A method involving a signal validation module, feature extraction module, and combinatory logic module that analyzes data from sensors to determine the accuracy and validity of machine operation signals, identifies anomalies, and generates outputs indicating potential defects through logic gates, with additional modules for likelihood determination and alarm settings to notify users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If model-based automatic diagnostic engines are used to analyze machine parameters, then specific machine failure conditions can be detected, but the system fails when encountering parameter conditions not part of the model's logic
Solution Approach 1:
The patent combines model-based diagnostic logic with statistics-based anomaly detection in a unified system. The model-based engine provides structured failure detection while the statistics-based engine provides flexibility for unfamiliar patterns, merging the strengths of both approaches to achieve both reliability and adaptability.
Solution Approach 2:
The diagnostic system is designed to perform multiple functions: detecting known failure modes through model-based logic, identifying unknown anomalies through statistics-based methods, and providing confidence scores for all detections. This multi-functionality allows the system to handle both familiar and unfamiliar conditions effectively.
2Adaptability or versatility
If statistics-based automatic diagnostic engines are used to analyze machine parameters, then deviation from norms can be identified, but the system cannot identify what the particular problem is
Solution Approach 1:
The system merges statistics-based anomaly detection with model-based failure mode analysis. While the statistics-based engine identifies deviations and unknown anomalies, the model-based engine provides structured interpretation of what those anomalies mean in terms of specific failure conditions, thus preventing information loss.
Solution Approach 2:
The system introduces an intermediary layer that translates statistical deviations into meaningful diagnostic conclusions. This intermediary component bridges the gap between raw statistical anomalies and actionable failure mode identifications, preserving problem identification capability while maintaining adaptability.
3Productivity
If automatic diagnostic capability is added to machine monitoring systems, then diagnostic efficiency is improved, but false positives and missed detections increase
Solution Approach 1:
The system implements feedback mechanisms where detection results are continuously evaluated and refined. Confidence scores are adjusted based on the consistency of findings across multiple detection methods, and false positives are fed back into the system to improve future detections, thereby maintaining high reliability while preserving efficiency.
Solution Approach 2:
The system performs preliminary actions by pre-establishing multiple diagnostic hypotheses and confidence thresholds before actual detection occurs. This preparation allows the system to quickly and accurately evaluate new data against pre-defined criteria, reducing false positives while maintaining high diagnostic efficiency.
Data Source
AI summary
A method for determining whether a defect exists in a machine. The method includes receiving a signal from a sensor that includes data related to operation of a machine. It is determined whether the data in the signal is accurate or valid and a first input is generated therefrom. It is determined whether a defect exists in the machine by analyzing the data in the signal and a second input is generated therefrom. The first input and the second input are introduced into one or more logic gates, which generate an output that indicates whether the defect exists in the machine. A user is notified when the output indicates that the defect exists in the machine.