Building Equipment Maintenance Control Using Cost Prediction
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Solution Overview
Problem
Existing maintenance strategies for building equipment, such as run-to-fail, preventative, and predictive maintenance, face challenges in accurately predicting the costs and benefits of maintenance tasks, leading to suboptimal maintenance decisions.
Innovation Solution
A Model Predictive Maintenance (MPM) system that uses operational and maintenance cost predictors, along with an objective function optimizer, to determine an optimal maintenance strategy for building equipment by predicting costs over a specified optimization period.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predictive maintenance strategy is used to perform diagnostics and predict maintenance tasks, then maintenance effectiveness is improved, but difficulty in predicting costs and benefits increases
Solution Approach 1:
The system performs preliminary actions by predicting future equipment states and maintenance needs before they actually occur. The predictive maintenance module analyzes current equipment data to forecast future conditions, allowing the system to plan and optimize maintenance tasks in advance, thereby improving maintenance effectiveness while providing cost estimates before maintenance is performed.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring equipment performance data and comparing actual maintenance outcomes with predicted costs and benefits. This closed-loop feedback allows the system to refine its predictive algorithms and cost models over time, gradually improving cost prediction accuracy while maintaining high maintenance effectiveness.
2Productivity
If maintenance tasks are performed based on predictive maintenance, then equipment performance improvement is achieved, but uncertainty in cost-benefit analysis increases
Solution Approach 1:
The system applies parameter changes by dynamically adjusting maintenance timing, task selection, and resource allocation based on real-time equipment condition data. The optimization module varies multiple parameters simultaneously to find the optimal maintenance strategy that maximizes equipment performance improvement while minimizing costs, thereby reducing uncertainty in cost-benefit analysis.
Solution Approach 2:
The system transitions from static, schedule-based maintenance to dynamic, condition-based maintenance optimization. The predictive maintenance module continuously updates equipment state predictions and cost estimates in real-time, allowing the system to adapt maintenance strategies dynamically based on actual equipment performance and changing operational conditions, thus improving both productivity and cost-benefit information accuracy.
Data Source
AI summary
A model predictive maintenance (MPM) system generates operating decisions and maintenance decisions for building equipment by performing a MPM process. The MPM process determines a specific type of maintenance activity to be performed at a service time from a set of multiple different types of maintenance activities based on (1) first costs of operating the building equipment predicted to result from the multiple different types of the maintenance activities and (2) second costs of performing maintenance on the building equipment predicted to result from the multiple different types of the maintenance activities. The MPM system causes the specific type of maintenance activity to be performed on the building equipment at the service time and controls the building equipment by generating electronic control signals based on the operating decisions and causing the building equipment to affect a variable state or condition in a building using the electronic control signals.