Machine Learning Failure Prediction Using Record-Size Metrics
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Solution Overview
Problem
Current systems fail to predict failures in a timely and cost-effective manner, often resulting in inefficient and ineffective restoration efforts due to the lack of early indicators and the potential for resource-intensive and risky remedial actions.
Innovation Solution
A computer-implemented method using a machine-learning model processes aggregate data, including electronic records and sensor data, to generate a failure probability, allowing for early alerting and preventive actions by determining record-size and physical attributes, and vital-sign metrics, thereby facilitating timely intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If failure prediction is performed using traditional monitoring methods, then system reliability can be maintained, but the ability to predict failures early is insufficient and resource costs increase
Solution Approach 1:
The system performs preliminary actions by continuously processing electronic records and sensor data before failures occur. The machine-learning model analyzes historical data and identifies patterns that indicate impending failures, enabling early prediction and preventive action before the actual failure event.
Solution Approach 2:
The patent introduces an intermediary machine-learning model that processes and analyzes electronic records and sensor data to predict failures. This intermediary system acts as a mediator between raw data and failure detection, transforming unstructured data into predictive insights that enable early warning.
2Reliability
If remedial actions are taken based on failure predictions, then system reliability improves, but false positives cause resource waste and potential harm
Solution Approach 1:
The system incorporates feedback mechanisms where predicted failures are monitored and validated against actual system behavior. The machine-learning model continuously learns from outcomes to refine its predictions, reducing false positives over time while maintaining high reliability in failure detection.
Solution Approach 2:
The patent changes parameters by using multiple data sources (electronic records, sensor data, vital signs) and transforming them into standardized metrics that the machine-learning model can process. This parameter transformation enables more accurate prediction and reduces false positives by providing a more comprehensive view of system state.
3Reliability
If comprehensive monitoring of all objects is performed, then failure detection capability improves, but system complexity and resource requirements increase
Solution Approach 1:
The system achieves universality by using a single machine-learning model that can process multiple types of data (electronic records, sensor data, vital signs) and apply to various objects and subjects. This multi-functional approach consolidates monitoring complexity into one unified system rather than requiring separate monitoring solutions for each object type.
Solution Approach 2:
The patent uses copying by creating standardized metrics and features from diverse data sources. Instead of directly monitoring every possible parameter, the system creates representative copies (metrics) that capture essential information, simplifying the monitoring process while maintaining detection capability.
Data Source
AI summary
Machine-learning processing of aggregate data including record-size data to predict failure probability is described herein. In an example, a system identifies electronic data that is longitudinal and includes a set of electronic records pertaining to a given subject or to a given object. The system generates a record-size metric that characterizes a size of the electronic data and determines a physical attribute of the given subject or the given object. The system generates a physical-attribute metric based on the physical attribute, generates an input data set that includes the record-size metric and the physical-attribute metric, and generates a failure probability across a given time period and for the given subject or the given object by processing the input data set using a trained machine-learning model. The system determines that an alert condition is satisfied based on the failure probability and outputs an alert representing the failure probability.