Predictive Analytics System With Operator Feedback Loop
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Solution Overview
Problem
Existing predictive analytics methodologies in asset-intensive environments often lead to 'information overload,' and context-free actions based on historical data can result in unintended consequences, such as unnecessary maintenance disruptions.
Innovation Solution
The system presents generated predictions to operators, compares them with prediction thresholds, and incorporates operator-generated input as updated source data to refine subsequent predictions, allowing for real-time adjustments based on current operational knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predictive analytics generate multiple action reports based on historical data, then the system provides comprehensive predictive coverage, but the worker experiences information overload and cannot act on reports in real-time
Solution Approach 1:
The system extracts and prioritizes only the most critical predictive actions from the overwhelming amount of data, presenting workers with a manageable subset of high-priority action reports that require immediate attention, thereby reducing information overload while maintaining predictive accuracy
Solution Approach 2:
The system incorporates feedback loops where worker responses to action reports are fed back into the predictive analytics model, allowing continuous refinement of predictions based on actual operational context and worker decisions, improving both accuracy and usability over time
2Extent of automation
If the system generates action reports based solely on historical data, then the predictive model remains simple and automated, but it cannot account for future events like scheduled maintenance or training that affect asset operation
Solution Approach 1:
The system dynamically adjusts between automated predictive analytics and manual worker input based on the situation. When historical data indicates high confidence predictions, the system operates in automated mode; when future events or contextual factors are involved, the system transitions to a collaborative mode where workers provide additional context, allowing the system to adapt to varying operational conditions
Solution Approach 2:
The system introduces worker context input as an intermediary layer between historical predictive analytics and final maintenance decisions. Workers can provide additional context about future events, operational priorities, or asset-specific knowledge that bridges the gap between automated predictions and real-world complexity
3Reliability
If the worker follows action reports blindly, then the maintenance schedule is maintained, but production disruption occurs when assets need to remain operational during scheduled events
Solution Approach 1:
The system allows dynamic adjustment of maintenance parameters based on operational context. When workers identify that an asset should remain operational during a scheduled event, the system updates the maintenance timing parameter to reflect this exception, balancing schedule adherence with production continuity requirements
Data Source
AI summary
Various examples of methods and systems are provided for improved predictive analytics. In one example, a method of managing operation of an asset or group of assets of interest includes comparing a generated prediction with one or more prediction range associated with a risk profile assigned to an operational outcome of interest, presenting a notification to an operator in response to the comparison, and incorporating operator-generated input as updated source data for the generation of subsequent predictions. The operator-generated input can comprise an operator-defined selection such as, e.g., acceptance of the notification, or rejection of the notification. The operator-generated input can provide real time or near real time information based upon context-specific knowledge that the operator holds that is substantially independent of historical source data.