Building Equipment Predictive Maintenance for Cost-Reliability Tradeoffs
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Solution Overview
Problem
Current maintenance strategies for building equipment, such as run-to-fail and preventative maintenance, lack accuracy in predicting costs and benefits, making it difficult to determine optimal maintenance tasks that balance operating and maintenance costs.
Innovation Solution
A Model Predictive Maintenance (MPM) system that predicts operational and maintenance costs over a defined optimization period using an objective function optimizer, considering decision variables like maintenance and equipment purchase, to determine optimal maintenance and equipment purchase decisions based on real-time feedback and efficiency degradation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If preventative maintenance tasks are performed at regular intervals, then equipment reliability is improved, but operating costs increase due to unnecessary maintenance
Solution Approach 1:
The maintenance system transitions from static scheduled intervals to dynamic, condition-based timing. The optimizer continuously adjusts maintenance schedules based on real-time equipment state, efficiency degradation rates, and predictive cost models, allowing maintenance to be performed only when and where actually needed rather than following fixed calendars
Solution Approach 2:
The system implements closed-loop feedback by continuously monitoring equipment performance, comparing actual efficiency degradation against predictive models, and using this information to adjust future maintenance decisions. The objective function incorporates feedback from operational costs, maintenance costs, and equipment state to optimize subsequent maintenance scheduling
2Loss of energy
If maintenance tasks are delayed to reduce costs, then operating costs decrease, but equipment failure risk increases
Solution Approach 1:
The system performs preliminary predictive analysis by modeling future equipment degradation trajectories and estimating both operational costs and maintenance costs over an optimization horizon. This allows the system to proactively identify optimal maintenance timing before failures occur, balancing cost savings with reliability requirements through forward-looking optimization
Solution Approach 2:
The objective function dynamically adjusts decision variables including maintenance timing, maintenance type, and equipment operation parameters. By changing these parameters based on real-time conditions and predictive models, the system optimizes the trade-off between delaying maintenance for cost savings and maintaining reliability to prevent failures
3Measurement precision
If predictive maintenance uses detailed diagnostics, then maintenance accuracy is improved, but system complexity increases
Solution Approach 1:
The system performs self-service through automated predictive modeling and optimization. The objective function automatically processes equipment data, predicts degradation trajectories, calculates cost implications, and generates optimized maintenance schedules without requiring complex external diagnostic systems or extensive manual analysis
Solution Approach 2:
The objective function serves multiple functions simultaneously: it predicts equipment degradation, estimates operational costs, calculates maintenance costs, optimizes maintenance scheduling, and adjusts operation parameters. This multi-functionality consolidates what would otherwise require separate complex systems into a single integrated optimization framework
Data Source
AI summary
A model predictive maintenance (MPM) system for building equipment includes an operational cost predictor configured to predict a cost of operating the building equipment over a duration of an optimization period, a maintenance cost predictor configured to predict a cost of performing maintenance on the building equipment over the duration of the optimization period, and an objective function optimizer configured to optimize an objective function to predict a total cost associated with the building equipment over the duration of the optimization period. The objective function includes the predicted cost of operating the building equipment and the predicted cost of performing maintenance on the building equipment. The MPM system includes an equipment controller configured to operate the building equipment to affect a variable state or condition in a building in accordance with values of one or more decision variables obtained by optimizing the objective function.