ML Segregation of Remote Asset Maintenance Recommendations
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Solution Overview
Problem
Current systems fail to accurately determine whether maintenance suggestions for industrial assets are remotely performable, leading to inefficient and costly maintenance processes, with a risk of human error and increased downtime.
Innovation Solution
A computer-implemented method using an intelligence machine learning model to segregate maintenance suggestions into remotely and non-remotely performable options based on data values, applying natural language processing and user configuration data to provide accurate notifications and automatically initiate suitable maintenance actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual determination of maintenance action performability is used, then human judgment can be applied, but time consumption and error risk increase
Solution Approach 1:
The patent replaces manual human determination of maintenance action performability with an automated machine learning model that processes service case recommendations and classifies them as remotely or non-remotely performable. This substitution eliminates human error and significantly reduces the time required for classification while maintaining high accuracy through trained algorithms.
Solution Approach 2:
The system enables self-service by automatically classifying maintenance suggestions without requiring human intervention. The machine learning model independently evaluates service case recommendations and determines performability, allowing the system to serve itself in the classification task and freeing human operators from this repetitive analytical work.
2Measurement precision
If all maintenance suggestions are processed manually, then accurate assessment is possible, but resource expenditure increases
Solution Approach 1:
The patent replaces manual processing of maintenance suggestions with an automated machine learning system that achieves both high accuracy in determining remote performability and high productivity in processing volume. The model can evaluate multiple service case recommendations simultaneously, maintaining precision while dramatically increasing throughput compared to manual methods.
Solution Approach 2:
The system changes the parameters of processing by using automated computational algorithms instead of human cognitive processes. This transformation enables the system to handle larger volumes of maintenance suggestions with consistent accuracy, as the machine learning model can process data at speeds and volumes beyond human capability while maintaining measurement precision through structured evaluation criteria.
3Productivity
If automated classification is implemented, then processing speed increases, but accuracy may decrease without proper training
Solution Approach 1:
The patent applies preliminary action by training the machine learning model with extensive service case recommendation data before deployment. This pre-training ensures the model learns accurate patterns and relationships in the data, establishing high classification accuracy before the system begins automated processing. The preliminary training phase is crucial for ensuring reliability before productivity gains are realized.
Solution Approach 2:
The system incorporates feedback mechanisms where classification results and outcomes are used to continuously improve the machine learning model. By analyzing actual maintenance outcomes and comparing them with predicted classifications, the system refines its algorithms over time, ensuring that automated classification maintains high accuracy while processing speed increases. This feedback loop prevents degradation of reliability as the system scales.
Data Source
AI summary
Embodiments of the present disclosure provide for improved determination of maintenance actions to be performed for a particular asset. Embodiments automatically segment maintenance suggestions into a remotely performable classification and a non-remotely performable classification, and/or enabling initiation of such maintenance actions accordingly. Some embodiments receiving at least one service case recommendation associated with an asset, applying the at least one service case recommendation to an intelligence machine learning model, the intelligence machine learning model configured to determine a data value indicating a likelihood that each service case recommendation of the at least one service case recommendation is remotely performable, determining, via the data value, at least one remotely performable maintenance suggestion from the at least one service case recommendation, and outputting at least one notification associated with the at least one remotely performable maintenance suggestion to a user associated with remote access of the asset.