ML Event Categorization for Equipment Maintenance Decisions
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Solution Overview
Problem
In equipment asset-intensive industries, predictive maintenance relies heavily on human expertise, which can be limited by the sheer volume of data and the experience of engineers, leading to missed opportunities for proactive maintenance and increased downtime due to the inability to recognize critical events indicative of impending faults.
Innovation Solution
The implementation of artificial intelligence and machine learning systems that analyze historical and real-time data from sensors to categorize events, determine equipment status, and recommend maintenance actions, utilizing a knowledge base generated from expert input and historical data to prioritize and automate maintenance decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If engineers manually analyze operating characteristics data to identify events indicative of equipment status, then maintenance decisions can be made based on expert knowledge, but the ability to recognize critical events degrades as the quantity of data and events increases
Solution Approach 1:
The patent replaces the mechanical system of manual engineer analysis with an automated machine learning system. The ML system processes operating characteristics data and identifies events indicative of equipment status without human intervention, thereby maintaining predictive maintenance accuracy while eliminating the degradation in analysis efficiency that occurs with increased data volume.
Solution Approach 2:
The patent introduces a machine learning system as an intermediary between the raw operating characteristics data and the maintenance decision-making process. This intermediary automatically identifies critical events and determines equipment status, bridging the gap between data volume and actionable insights without requiring direct human analysis of each event.
2Loss of information
If engineers manually monitor all events to identify those predictive of faults, then comprehensive coverage is achieved, but the sheer number of events overwhelms engineers and causes them to miss critical opportunities
Solution Approach 1:
The patent replaces manual engineer monitoring with an automated machine learning system that processes all events without fatigue or distraction. The ML system maintains complete event recognition by systematically analyzing all operating characteristics data while significantly reducing the time required to identify critical events through automated pattern recognition.
3Reliability
If experienced engineers with deep knowledge perform predictive maintenance analysis, then accurate fault prediction is achieved, but knowledge loss occurs when experienced engineers retire and are replaced by younger engineers
Solution Approach 1:
The patent creates a digital copy of expert knowledge by training machine learning models on historical data and expert inputs. This copied knowledge is embedded in the ML system, which then consistently applies the same analytical framework regardless of personnel changes, thereby maintaining fault prediction accuracy and ensuring knowledge continuity when engineers retire or are replaced.
Solution Approach 2:
The patent enables the system to self-improve and maintain its predictive capabilities through automated learning from new data. The ML system continuously refines its understanding of equipment behavior patterns without requiring constant retraining by human experts, thereby maintaining reliability while adapting to new conditions.
Data Source
AI summary
Aspects of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to analyze and categorize events associated with an equipment asset, such as industrial machinery, to determine a status (e.g., insight) associated with the equipment asset, and to determine maintenance actions to be performed with respect to the equipment asset to prevent, or reduce the likelihood or severity of, occurrence of a fault at the equipment asset. Machine learning (ML) models may be trained to categorize events that are detected based on operating characteristics data associated with the equipment asset, to determine a status of the equipment asset, and to recommend one or more maintenance actions (or other actions). Output that indicates the maintenance actions may be displayed to a user or used to automatically initiate performance of one or more of the maintenance actions.