Machine Fault Prediction Using Sensor and Maintenance Logs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machinery monitoring systems rely on local sensor data and assumptions, lacking a comprehensive method to predict and prevent faults 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, reducing the likelihood and severity of future faults.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If local sensor data and assumptions are used for monitoring, then the monitoring system is simple to implement, but the fault prediction accuracy is insufficient
Solution Approach 1:
The patent combines multiple data sources including sensor data, maintenance logs, and operational data into a unified predictive analytics platform. This merging of previously separate information sources enables comprehensive fault prediction while maintaining implementation feasibility through integrated processing.
Solution Approach 2:
The patent introduces a predictive analytics platform as an intermediary layer between raw sensor data and fault prediction outcomes. This intermediary processes and synthesizes multiple data types using machine learning models, transforming simple sensor inputs into accurate fault predictions without requiring direct complex analysis of each individual data source.
2Measurement precision
If comprehensive data analysis is performed to improve fault prediction, then the prediction accuracy is enhanced, but the system complexity increases
Solution Approach 1:
The predictive analytics platform performs self-service by automatically collecting, processing, and analyzing data from multiple sources without requiring manual intervention. The system autonomously trains machine learning models on historical data and continuously updates predictions, reducing the operational complexity despite the comprehensive analysis performed.
Solution Approach 2:
The patent implements preliminary action by pre-processing and storing maintenance logs and operational data before they are needed for prediction. Historical data is pre-analyzed and structured in advance, allowing the system to perform comprehensive analysis during operation without real-time complexity, as the heavy lifting of data preparation is completed beforehand.
3Reliability
If frequent maintenance is performed to prevent faults, then the reliability is improved, but the productivity decreases due to increased downtime
Solution Approach 1:
The patent implements dynamic maintenance scheduling where maintenance frequency and timing are adjusted based on real-time predictive analytics. Instead of fixed frequent maintenance intervals, the system dynamically optimizes maintenance schedules according to actual machine condition and predicted fault risks, maintaining high reliability while minimizing unnecessary downtime and preserving productivity.
Solution Approach 2:
The system performs preliminary fault detection and prediction before actual failures occur, enabling planned maintenance during convenient downtime rather than reactive maintenance after failures. This preliminary action allows maintenance to be scheduled optimally, preventing faults before they impact productivity while avoiding unnecessary maintenance interruptions.
Data Source
AI summary
Sensor logs corresponding to a first machine are accessed. Each sensor log spans at least a first period. First computer readable logs corresponding to the first machine are accessed. Each computer readable log spans at least the first period, the computer readable logs include a maintenance log including maintenance task objects, each maintenance task object includes a time and a maintenance task type. A set of statistical metrics are derived from the sensor logs. A set of log metrics are derived from the computer readable logs. 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 are determined.