Automated Component Failure Prediction from Sensor Data Patterns
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Solution Overview
Problem
Electromechanical products with multiple components face challenges in predicting component failures, leading to downtime and inefficiencies, as existing sensor data is unstructured and difficult to analyze, requiring skilled labor and time-consuming manual processes.
Innovation Solution
An automated system that analyzes historical data from sensors to generate and update predictive models for component failures, selecting relevant features, generating and combining models to predict failures, and providing alerts for proactive maintenance, reducing manual input and increasing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of sensor data is used, then skilled labor can determine root cause of failure, but it is time-consuming and requires skilled labor
Solution Approach 1:
The system enables automated failure prediction by having the sensor data and processing system work together autonomously. The sensors continuously monitor component parameters, the processor automatically analyzes the data patterns, and the system generates failure predictions without requiring skilled labor intervention for each analysis, thus reducing both time and dependency on expert personnel
Solution Approach 2:
The patent replaces the manual mechanical process of skilled labor analysis with an automated electronic system. The processor electronically analyzes sensor data patterns and generates failure predictions, substituting the human expert's manual inspection and analysis process with an automated computational system that operates continuously without fatigue or delay
2Reliability
If sensors monitor component health, then component status data is obtained, but the data is unstructured and difficult to analyze
Solution Approach 1:
The processor acts as an intermediary between the sensors and the analysis system. It receives raw sensor data, processes and structures it by identifying patterns and correlations, and presents organized failure predictions. This intermediary processing layer transforms unstructured sensor readings into structured, actionable insights, reducing the complexity of subsequent analysis
Solution Approach 2:
The system transforms raw sensor parameters into meaningful failure prediction parameters through pattern recognition. By analyzing changes in sensor data patterns over time and identifying correlations between different parameters, the system converts complex multi-parameter sensor readings into simplified failure probability assessments, making the data easier to analyze and act upon
3Loss of energy
If component replacement is delayed until failure, then cost-effective replacement is possible, but product downtime increases
Solution Approach 1:
The system performs preliminary failure prediction by continuously analyzing sensor data patterns and identifying components that are likely to fail soon. By predicting failures before they occur, the system enables advance scheduling of maintenance activities, allowing replacement to be performed during planned downtime rather than causing unexpected product shutdowns, thus reducing overall product downtime
Data Source
AI summary
Example systems may relate to component failure prediction. A non-transitory computer readable medium may contain instructions to analyze a plurality of features corresponding to a component of a system. The non-transitory computer readable medium may further contain instructions to determine which of the plurality of features to use to model a failure of the component. The non-transitory computer readable medium may contain instructions to generate a plurality of models to model the failure of the component and assemble the plurality of models into a single model for predicting component failure. The non-transitory computer readable medium may further contain instructions to extract data associated with a component failure predicted by the single model and correlate the data associated with the predicted component failure with the single model.