Equipment Operating Envelope Control for Longer Time Between Failures
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Equipment failures occur frequently despite regular maintenance due to suboptimal operating conditions, leading to costly repairs and production losses.
Innovation Solution
A computer system analyzes historical sensor and failure data using machine learning to determine a prescribed operating envelope, which includes optimal operating parameter values that reduce the frequency of equipment failures by controlling the equipment within specific operational parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If equipment operates outside optimal conditions, then productivity may increase, but failure frequency increases and costs rise
Solution Approach 1:
The operating envelope is not static but dynamically determined based on historical sensor data and failure patterns. The system adapts the optimal parameter ranges to changing operating conditions and equipment states, allowing productivity optimization while maintaining reliability boundaries that prevent failures.
Solution Approach 2:
The system proactively determines the operating envelope before failures occur by analyzing historical data patterns. This preliminary establishment of optimal boundaries allows the system to prevent failures before they happen by keeping operations within proven safe parameter ranges, rather than reacting after failures occur.
2Measurement precision
If multiple sensor parameters are monitored, then operating conditions are better understood, but system complexity increases
Solution Approach 1:
The system segments the complex multi-parameter monitoring task into manageable components: individual sensor data collection, separate determination of optimal range for each sensor, and then integration into a comprehensive operating envelope. This segmentation makes the complex system easier to implement and maintain while achieving precise multi-parameter monitoring.
Data Source
AI summary
In some examples, a computer system may receive sensor data and failure data for equipment. The system may determine, for the equipment, a plurality of time between failure (TBF) durations that are longer than other TBF durations for the equipment. The system may determine, from the sensor data corresponding to operation of the equipment during the plurality of TBF durations, a plurality of measured sensor values for the equipment. Additionally, the system may determine a subset of the measured sensor values corresponding to a largest number of the TBF durations of the plurality of TBF durations. The system may further determine at least one operating parameter value for the equipment based on the subset of the measured sensor values. The system may send a control signal for operating the equipment based on the operating parameter value and/or a communication based on the operating parameter value.