Automated Performance Analysis and Failure Remediation for Electromechanical Devices
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Solution Overview
Problem
Current systems for diagnosing and remediating electromechanical devices require real-time processing and are triggered by actual failures, lacking an integrated approach to optimize performance and reduce maintenance activities.
Innovation Solution
A system and method that access knowledge, device, and historical data sets to generate a prioritized item list for remedial steps, using priority rules to integrate performance and failure analysis, and incorporating systematic error analysis to automatically detect trends and patterns, thereby facilitating effective maintenance and diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time processing is used for failure diagnosis and remediation, then failure prediction and diagnosis accuracy is improved, but system complexity and processing time requirements increase
Solution Approach 1:
The system segments the complex diagnosis process into distinct functional modules: data acquisition module that collects device parameters, analysis module that processes the data using knowledge bases, and remediation module that generates repair recommendations. This modular segmentation reduces overall system complexity while maintaining real-time processing capability for accurate failure diagnosis.
Solution Approach 2:
The patent introduces a knowledge base as an intermediary component that stores pre-processed failure patterns, device specifications, and repair procedures. This intermediary layer simplifies the main processing system by offloading complex diagnostic logic to the knowledge base, thereby reducing system complexity while preserving diagnostic accuracy.
2Reliability
If real-time processing is implemented for failure diagnosis, then failure prediction capability is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing failure patterns, device configurations, and repair procedures in the knowledge base during off-line periods. This preliminary preparation enables the real-time system to quickly retrieve and match incoming device data against pre-analyzed patterns, improving failure prediction capability while minimizing real-time processing time.
Solution Approach 2:
The system implements periodic action by continuously monitoring device parameters at scheduled intervals and comparing them against the knowledge base. This periodic monitoring approach maintains up-to-date failure prediction capability without requiring constant high-intensity processing, thereby balancing reliability with processing time efficiency.
3Productivity
If comprehensive data analysis is performed for performance optimization, then maintenance efficiency is improved, but data processing complexity increases
Solution Approach 1:
The knowledge base serves as a universal repository that handles multiple functions: storing device specifications, failure patterns, repair procedures, and performance metrics. This multi-functional knowledge base consolidates diverse data processing requirements into a single system, improving maintenance efficiency through comprehensive data analysis while reducing overall data processing complexity through centralization.
4Productivity
If automated diagnosis systems are deployed, then service and maintenance activities are reduced, but system implementation complexity increases
Solution Approach 1:
The system implements self-service by enabling devices to automatically monitor their own parameters, compare them against the knowledge base, and generate their own diagnostic reports and repair recommendations. This automation reduces the need for manual service and maintenance activities while the modular knowledge base structure keeps implementation complexity manageable through standardized data formats and retrieval procedures.
Data Source
AI summary
A service diagnosis tool for performance analysis and failure remediation of electromechanical devices, e.g. printers or copiers. The tool communicates with an actual device of a specific type, and accesses data from a knowledge data set containing knowledge relating to properties of devices of the specific type, a device data set and a history data set. The device data set contains parameters of the actual device, including actual error data indicating errors that occurred in an actual period. The historical data includes historical error data of the actual device relating to errors, performance and/or remediation that occurred before the actual period. The tool has a calculation unit for analyzing and combining data from the data sets for generating a prioritized item list indicative of remedial steps to be executed. The items in the list relate to performance and/or failure of the actual device, and are organized according to priority rules.