Machine Operating Models for Adaptive Predictive Maintenance
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Solution Overview
Problem
Complex systems with multiple machines face inefficiencies due to degradation, failure, or sub-optimal use of components, which are difficult to detect, leading to uncertainty in monitoring and maintenance, especially in large and complex systems where few experts can accurately assess and verify the health and efficiency of these systems.
Innovation Solution
A data processing method that generates and updates machine operating models based on past operating data and generic definitions, using computing devices to analyze patterns in machine behavior across different operating states, enabling continuous prediction and adaptation of machine health management systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual monitoring and maintenance decisions are made by maintenance engineers, then system health can be assessed, but accuracy and reliability are limited by human expertise availability and variability
Solution Approach 1:
The system enables self-service by allowing machines to automatically monitor their own operational parameters and generate maintenance predictions without continuous human intervention. The machine learning models autonomously analyze sensor data, detect anomalies, and recommend maintenance actions, freeing the system from dependency on expert human assessment while improving consistency and accuracy.
Solution Approach 2:
The patent replaces the mechanical human decision-making process with an automated computational system. Instead of relying on maintenance engineers to manually analyze system health, the invention uses machine learning algorithms that process sensor data, identify patterns, and generate maintenance predictions automatically, substituting human cognitive processes with computational intelligence.
2Productivity
If generic maintenance schedules are used based on manuals or specifications, then maintenance operations can be planned, but they often grossly over-estimate or under-estimate the actual maintenance frequency needed
Solution Approach 1:
The system transitions from static, pre-defined maintenance schedules to dynamic, adaptive maintenance planning. The machine learning models continuously learn from actual machine operation data and sensor readings, automatically adjusting maintenance predictions based on real-time conditions, operational intensity, and detected degradation patterns, thereby optimizing both timing and resource allocation.
Solution Approach 2:
The invention implements feedback loops where sensor data from machine operation continuously feeds into the machine learning models, which then refine their predictions based on actual performance and degradation patterns observed. This closed-loop system allows the maintenance schedule to adapt and improve over time based on real-world feedback, rather than relying on static manufacturer recommendations.
3Loss of information
If sensors are used to monitor component behavior, then operational data can be collected, but sensor degradation and failure create uncertainty around measurements
Solution Approach 1:
The system merges multiple sensor readings and data sources to compensate for individual sensor degradation or failure. By aggregating data from multiple sensors and cross-validating measurements, the machine learning models can detect when a sensor is providing unreliable data and adjust accordingly, maintaining data quality without requiring complex individual sensor verification processes.
Solution Approach 2:
The machine learning models act as intermediaries between raw sensor data and maintenance decisions. These models process, validate, and interpret sensor readings, filtering out noise and detecting anomalies that indicate sensor degradation. The intermediary layer translates uncertain sensor measurements into reliable maintenance predictions, reducing the impact of sensor uncertainty on overall system reliability.
4Ease of operation
If customized triggers are set up by maintenance engineers to alert when measurements exceed boundaries, then alerts can be generated, but the system remains dependent on engineer knowledge and skill
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models on extensive historical data and generic machine definitions before deployment. This preliminary training enables the models to automatically establish appropriate thresholds and detection criteria without requiring manual configuration by maintenance engineers, eliminating the need for expert knowledge in setting up the monitoring system while maintaining high detection accuracy.
Solution Approach 2:
The monitoring system enables self-service by automatically learning optimal detection thresholds and anomaly criteria from data without human intervention. The machine learning models autonomously adapt to specific machine behaviors and conditions, generating accurate alerts based on learned patterns rather than static engineer-defined boundaries, thereby improving both ease of operation and detection precision.
Data Source
AI summary
In an embodiment, a data processing method comprises storing one or more generic machine operating definitions, wherein each of the generic machine operating definitions describes expected operational behavior of one or more types of machines during one or more operating states; analyzing operating data that describes past operation of a plurality of machines of a plurality of types; based at least in part on the operating data and the one or more generic machine operating definitions, generating and storing one or more machine operating models that describe expected operational behavior corresponding to a plurality of operating states of the plurality of machines; wherein the one or more machine operating models comprise a plurality of data patterns, wherein each of the data patterns is associated with a different set of one or more operating states of one or more machines; wherein the method is performed by one or more computing devices.