Failure Prediction System Using Dynamic Model Selection
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Solution Overview
Problem
Current maintenance strategies, such as corrective and preventive maintenance, are inefficient and costly due to their reliance on timing rather than equipment condition, and lack the ability to predict and prevent unexpected failures effectively.
Innovation Solution
A data-driven system that uses sensor measurements and historical failure data to learn classification models, predicting equipment failures and optimizing maintenance schedules based on cost functions related to false alarm rates and failure costs, thereby selecting the most appropriate models for real-time application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If preventive maintenance is performed on a regular basis regardless of equipment condition, then equipment availability is improved and unexpected failures are reduced, but maintenance costs increase significantly as most maintenance actions are done while equipment is in good condition
Solution Approach 1:
The system changes the maintenance approach from fixed time intervals to dynamic condition-based intervals. By continuously monitoring equipment parameters (vibration, temperature, pressure, etc.) and analyzing them with machine learning models, the system adjusts maintenance timing based on actual equipment state, performing maintenance only when predicted failure probability exceeds a threshold, thus avoiding unnecessary maintenance while preventing failures.
Solution Approach 2:
The patent replaces traditional mechanical/time-based maintenance scheduling with an intelligent data-driven system. Instead of following predetermined maintenance calendars, the system uses sensor data, machine learning models, and probability calculations to dynamically determine when maintenance is actually needed, substituting mechanical routines with intelligent decision-making.
2Loss of energy
If corrective maintenance is performed after equipment failure, then maintenance costs and time are reduced compared to preventive maintenance, but equipment availability decreases due to unexpected downtime
Solution Approach 1:
The system performs preliminary maintenance actions before equipment failure occurs by continuously monitoring equipment condition and predicting failures using machine learning models. When the predicted failure probability exceeds a predetermined threshold, the system triggers maintenance alerts, allowing maintenance personnel to service the equipment proactively before failure, thus avoiding unexpected downtime while performing maintenance only when necessary.
3Measurement precision
If multiple classification models are evaluated and selected based on cost functions, then prediction accuracy and cost optimization are improved, but system complexity increases
Solution Approach 1:
The system implements dynamic model selection where multiple classification models are trained and evaluated, but only the model with the optimal cost function (balancing false alarm rate, recall rate, and maintenance costs) is deployed for prediction. The system dynamically adapts to different equipment types and failure modes by selecting the most appropriate model, rather than using a single complex ensemble for all cases.
Solution Approach 2:
The system changes model parameters (thresholds, cost weights, evaluation metrics) based on specific equipment characteristics and operational requirements. By adjusting these parameters, the system optimizes the balance between false alarms and missed detections for different equipment types, achieving high accuracy without requiring universally complex model architectures.
Data Source
AI summary
Example implementations described herein involve a system for maintenance recommendation based on data-driven failure prediction. The example implementations can involve estimating the probability of having a failure event in the near future given sensor measurements and events from the equipment, and then alerts the system user or maintenance staff if the probability of failure exceeds a certain threshold. The example implementations utilize historical failure cases along with the associated sensor measurements and events to learn a group of classification models that differentiate between failure and non-failure cases. In example implementations, the system then chooses the optimal model for failure prediction such that the overall cost of the maintenance process is minimized.