Machine Fault Risk Modeling from Sensor and Maintenance Logs
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Solution Overview
Problem
Existing machinery fault detection systems rely on local sensor data and assumptions, lacking a comprehensive and predictive approach to identify fault probabilities and prioritize maintenance tasks effectively.
Innovation Solution
A method using machine learning models, such as random forests or neural networks, to analyze sensor and maintenance logs to determine fault probabilities and prioritize maintenance tasks based on historical data, enabling proactive maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If local sensor data and assumptions are used for fault detection, then the system is simple to operate, but the measurement precision and reliability of fault detection are insufficient
Solution Approach 1:
The patent merges multiple data sources including sensor data, maintenance logs, and operational records into a unified predictive analytics platform. This integration combines heterogeneous data types to improve fault detection precision while managing system complexity through centralized processing architecture.
Solution Approach 2:
The system performs preliminary analysis by continuously processing historical data and building predictive models in advance. Fault probabilities are calculated proactively before actual failures occur, enabling early intervention and improving detection precision through pre-computed risk assessments.
2Reliability
If comprehensive predictive analysis is implemented, then the reliability of fault detection improves, but the loss of time for data processing increases
Solution Approach 1:
The system implements periodic processing of maintenance logs and sensor data at scheduled intervals rather than continuous real-time analysis. This periodic action maintains prediction reliability by regularly updating fault probability models while reducing overall data processing time through batch processing efficiency.
Solution Approach 2:
Historical data is pre-processed and stored in optimized formats beforehand, with predictive models trained in advance on historical datasets. This preliminary preparation reduces real-time processing requirements while maintaining high reliability in fault predictions during operational phases.
3Reliability
If more maintenance tasks are performed, then the reliability of preventing faults improves, but the productivity of the machine decreases due to excessive maintenance
Solution Approach 1:
The system applies partial maintenance actions by prioritizing tasks based on calculated fault probabilities. Instead of performing all possible maintenance tasks, it selectively schedules only those with highest predicted risk, achieving sufficient fault prevention reliability while minimizing productivity loss from unnecessary maintenance activities.
Solution Approach 2:
The system dynamically changes maintenance scheduling parameters based on actual machine condition and predicted fault risks. Maintenance intervals and task selections are adjusted according to real-time fault probability assessments, optimizing the balance between prevention reliability and productivity by performing maintenance only when statistically necessary.
Data Source
AI summary
Systems, methods, non-transitory computer readable media can be configured to access a plurality of sensor logs corresponding to a first machine, each sensor log spanning at least a first period; access first computer readable logs corresponding to the first machine, each computer readable log spanning at least the first period, the computer readable logs comprising a maintenance log comprising a plurality of maintenance task objects, each maintenance task object comprising a time and a maintenance task type; determine a set of statistical metrics derived from the sensor logs; determine a set of log metrics derived from the computer readable logs; and determine, using a risk model that receives the statistical metrics and log metrics as inputs, fault probabilities or risk scores indicative of one or more fault types occurring in the first machine within a second period.