Adaptive Equipment Performance Modeling for Faster Fault Diagnosis
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Solution Overview
Problem
Current sensor measurement and diagnostics tools require lengthy and expensive manual research and development to adapt to specific equipment, limiting their effectiveness in diagnosing faults or recognizing traits across various types of equipment.
Innovation Solution
A platform that collects and catalogs data using sensor measurements, statistical modeling, and a labeling mechanism to diagnose performance issues and maintenance conditions, integrating sensor coordinators, servers, and statistical models to provide adaptive diagnostics for multiple types of equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual research and development is used to adapt sensing solutions to specific equipment, then measurement precision and diagnostic accuracy are improved, but development time and cost increase significantly
Solution Approach 1:
The patent creates a universal sensing solution platform that can diagnose multiple equipment types without requiring separate manual R&D for each. The system uses a standardized sensor coordinator architecture that can be adapted to different equipment through configuration rather than custom development, enabling one sensing solution to serve multiple diagnostic purposes across various equipment types.
Solution Approach 2:
The patent employs template-based modeling where diagnostic solutions are created as reusable templates that can be copied and adapted to similar equipment. Instead of manually developing diagnostic logic for each equipment type, the system uses predefined templates that capture common diagnostic patterns, which can be quickly instantiated and customized for specific equipment instances.
2Reliability
If equipment-specific diagnostic tools are developed for each type of equipment, then diagnostic reliability is improved, but device complexity and manufacturing cost increase
Solution Approach 1:
The system implements a universal sensor coordinator platform that maintains diagnostic reliability across different equipment types through standardized measurement and evaluation methods. The same core diagnostic engine and sensor coordination architecture can reliably diagnose various equipment types without requiring separate complex systems for each equipment category.
Solution Approach 2:
The patent uses configurable parameters and settings that allow the same diagnostic system to adapt to different equipment types by changing operational parameters rather than altering the fundamental system architecture. This enables the system to maintain reliability across equipment types while avoiding the complexity of multiple specialized systems.
3Loss of information
If comprehensive sensor data collection is implemented for all equipment types, then measurement completeness is improved, but data processing time and computational resources increase
Solution Approach 1:
The sensor coordinator selectively extracts and processes only the specific sensor data relevant to the current diagnostic task and equipment type. Rather than collecting and processing all possible sensor data universally, the system identifies and extracts only the necessary measurements needed for the specific diagnostic evaluation being performed, reducing unnecessary computational overhead.
Solution Approach 2:
The patent segments the data processing workflow into distinct stages: data collection, relevant data filtering, and diagnostic evaluation. By segmenting the process and applying different processing intensities at each stage, the system maintains measurement completeness while optimizing processing efficiency through staged data handling.
Data Source
AI summary
An equipment performance modeling platform is disclosed. In certain embodiments, an adaptive sensing coordinator acquires sensor measurements, configures and processes the sensor measurements for a specific statistical model, and sends the measurements to a server. A server performs data processing, provides storage (e.g., local or in a database), and provides an interface for data extraction. Statistical models are used to interpreting sensor values for a type of equipment, and a labeling mechanism labels performance occurrences.