AI Jury Maintenance Monitoring for Early Equipment Failure Prediction
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Solution Overview
Problem
Current methods for monitoring machine operations and predicting equipment failures lack efficiency and cost-effectiveness, as they often require specialized hardware and complex software implementations, which can be resource-intensive and expensive, especially when only a portion of the computer's capabilities are utilized.
Innovation Solution
The implementation of a digital electronic appliance that empanels multiple artificial intelligence classification technologies into a 'jury' using combinational digital logic to render verdicts on equipment service needs and impending failures, utilizing a network to forward signals for remote monitoring and maintenance, and employing smart agents with real-time and long-term profiling, neural networks, fuzzy logic, and business rules to generate composite prediction outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized hardware and complex software implementations are used for monitoring machine operations and predicting equipment failures, then measurement precision and reliability are improved, but device complexity and cost increase
Solution Approach 1:
The patent employs a general-purpose computer system that can perform multiple functions including monitoring machine operations, analyzing data from multiple sensors, running various classification algorithms (neural networks, fuzzy logic, decision trees), and generating maintenance predictions. This multi-functional approach eliminates the need for specialized hardware for each function while maintaining high prediction accuracy through software-based implementations.
Solution Approach 2:
The patent uses software models and algorithms that replicate the functionality of complex specialized systems. By implementing classification algorithms and analysis routines in software rather than hardware, the system achieves equivalent prediction capabilities without requiring dedicated hardware circuits, thereby reducing device complexity while maintaining measurement precision.
2Device complexity
If a general-purpose computer is used to monitor machine operations and predict failures, then device complexity is reduced, but resource utilization efficiency decreases
Solution Approach 1:
The patent implements a selective monitoring approach where the general-purpose computer focuses only on the specific parameters and sensors relevant to the particular machine being monitored. Rather than utilizing the full computational power of the computer for all possible analyses, the system activates only the necessary classification algorithms and data processing routines needed for the current monitoring task, thereby improving resource utilization efficiency while maintaining system simplicity.
Solution Approach 2:
The monitoring system is divided into modular components including separate sensor modules, data acquisition routines, classification algorithms (neural networks, fuzzy logic, decision trees), and output generation functions. This segmentation allows the general-purpose computer to execute only the specific modules needed for each monitoring task, reducing unnecessary resource consumption while keeping the overall system simple and flexible.
3Reliability
If multiple AI classification technologies are empaneled into a jury system, then prediction reliability is improved, but device complexity increases
Solution Approach 1:
The patent combines multiple AI classification technologies (neural networks, fuzzy logic, decision trees, statistical methods) into a unified software-based jury system. All these algorithms run on a single general-purpose computer platform, sharing common data structures, input/output interfaces, and control logic. This merging approach improves prediction reliability by leveraging the strengths of multiple algorithms while avoiding the complexity of implementing separate specialized hardware systems for each algorithm.
Data Source
AI summary
A method for operating digital electronic appliance that empanels several different artificial intelligence (AI) classification technologies into a “jury” uses combinational digital logic to render “verdicts” about the need for service and impending equipment failures of the machines they monitor. Networks can be used to forward signals from remote locations to a centralized appliance that may be plugged as a module into a server. The appliance outputs can also be communicated over networks to servers that will muster appropriate maintenance personnel who are forewarned as to the nature of the trouble.