Equipment Failure Prediction Using Survivability Threshold Models
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Solution Overview
Problem
Industrial equipment in large operations, such as oil and gas production, experiences unexpected failures leading to significant production downtime and financial losses due to inadequate prediction and monitoring of equipment performance.
Innovation Solution
A method and system that processes historical sensor data from benchmark equipment to generate survivability data, predicting equipment failure by creating a model that relates parameter values to operational states, and communicating this data to a user interface for visualization and decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If constant monitoring of equipment parameters is implemented, then equipment safety and performance verification are improved, but unexpected equipment failure still occurs leading to production downtime
Solution Approach 1:
The system performs preliminary actions by continuously analyzing equipment parameter trends and predicting potential failures before they occur. The predictive analytics engine processes sensor data to identify patterns indicating future equipment failure, enabling maintenance to be scheduled in advance rather than reacting to unexpected breakdowns.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring equipment parameters and comparing them against predicted failure thresholds. The system provides real-time feedback to operators through alerts and notifications when equipment parameters indicate potential failure, enabling proactive intervention to prevent downtime.
2Productivity
If equipment is operated continuously to maximize production capacity, then productivity is improved, but equipment failure risk increases
Solution Approach 1:
The system provides continuous feedback on equipment health status by analyzing sensor data from continuously operating equipment. This enables production to continue at maximum capacity while simultaneously monitoring for early signs of equipment degradation, allowing maintenance to be scheduled during planned downtime rather than causing unplanned interruptions.
Solution Approach 2:
The predictive analytics engine performs preliminary failure predictions on continuously operating equipment, identifying potential failures before they occur. This allows production to maintain maximum capacity while preparing maintenance schedules in advance, preventing unexpected stoppages that would reduce overall productivity.
3Measurement precision
If comprehensive sensor monitoring is deployed to track multiple equipment parameters, then equipment performance verification is improved, but system complexity increases
Solution Approach 1:
The system extracts and focuses on the most critical equipment parameters for predictive analytics. Rather than analyzing all sensor data equally, the system identifies and prioritizes key parameters that most strongly correlate with equipment failure, simplifying the monitoring system while maintaining high measurement precision for the most important metrics.
Solution Approach 2:
The predictive analytics engine serves multiple functions simultaneously: it monitors equipment parameters, predicts failures, generates maintenance schedules, and provides alerts. This multi-functionality reduces overall system complexity by consolidating multiple monitoring and analysis functions into a single integrated platform.
Data Source
AI summary
A method for predicting equipment failure includes receiving parameter sample values associated with parameters of benchmark equipment and operational status information associated with the benchmark equipment. The parameter sample values and operational status information are periodically acquired. A model is generated for relating one or more of the parameters to benchmark equipment failure. For each parameter, a threshold value at which an output of the model indicates benchmark equipment failure is determined. Next, parameters of an equipment under test having parameter sample values that match the determined threshold values are determined. For each determined parameter, benchmark equipment having parameter sample values that match the parameter sample values of the equipment under test that match the determine threshold values are determined. Survivability data for the equipment under test is generated based on survivability data associated with the determined benchmark equipment. The generated survivability data is communicated to a user interface.