Predictive Building Equipment Maintenance With Incentive-Aware Cost Control
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Solution Overview
Problem
Current maintenance strategies for building equipment, such as run-to-fail and preventative maintenance, lack optimization in terms of cost and efficiency, as they do not consider real-time operational conditions and incentives, leading to suboptimal maintenance and equipment replacement decisions.
Innovation Solution
A model predictive maintenance system that includes an equipment controller, operational and maintenance cost predictors, a cost incentive manager, and an objective function optimizer to determine an optimal maintenance strategy by predicting costs and incentives, optimizing maintenance and equipment replacement decisions based on real-time data and feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If run-to-fail maintenance strategy is used, then operational flexibility is improved, but equipment reliability deteriorates due to unexpected failures
Solution Approach 1:
The system performs preliminary actions by predicting future equipment failures and maintenance needs before they occur. The predictive maintenance model analyzes current equipment state and operational conditions to forecast when maintenance should be performed, enabling proactive scheduling that prevents unexpected failures while maintaining operational flexibility.
Solution Approach 2:
The system implements continuous feedback loops where equipment performance data is constantly monitored, analyzed, and fed back into the predictive maintenance model. This feedback mechanism allows the system to adjust maintenance recommendations based on actual equipment behavior, improving reliability predictions while preserving operational autonomy.
2Reliability
If preventative maintenance is performed at regular intervals, then equipment reliability is improved, but operational efficiency deteriorates due to unnecessary maintenance tasks
Solution Approach 1:
The system transitions from static, fixed-interval maintenance schedules to dynamic, condition-based maintenance timing. The predictive maintenance model continuously adjusts maintenance recommendations based on real-time equipment state, operational conditions, and predicted failure risks, allowing maintenance to be performed only when truly necessary rather than at predetermined intervals.
Solution Approach 2:
The system changes the parameter of maintenance timing from fixed calendar intervals to variable, prediction-based intervals. By monitoring equipment parameters such as vibration, temperature, and performance degradation, the system dynamically determines optimal maintenance moments, eliminating unnecessary maintenance tasks while maintaining reliability.
3Device complexity
If maintenance decisions are made without considering incentives, then decision simplicity is improved, but total cost deteriorates due to missed cost-saving opportunities
Solution Approach 1:
The system introduces an intermediary layer that bridges maintenance decision-making and incentive programs. The predictive maintenance model incorporates incentive information as an additional input parameter, allowing the system to automatically evaluate how available incentives affect maintenance timing and cost, thereby capturing cost-saving opportunities without significantly increasing decision complexity.
4Productivity
If real-time predictive optimization is implemented, then maintenance optimization is improved, but system complexity deteriorates due to multiple predictors and optimizers
Solution Approach 1:
The system merges multiple functional components into an integrated predictive maintenance platform. The operational cost predictor, maintenance cost predictor, incentive evaluator, and optimization engine are combined into a unified system that processes data and generates maintenance recommendations as a single coordinated output, managing complexity through integration rather than separate standalone components.
Data Source
AI summary
A model predictive maintenance system for building equipment including an equipment controller to operate the building equipment to affect a variable state or condition in a building. The system includes an operational cost predictor to predict a cost of operating the building equipment over a duration of an optimization period, a maintenance cost predictor to predict a cost of performing maintenance on the building equipment, and a cost incentive manager to determine whether any cost incentives are available and, in response to a determination that cost incentives are available, identify the cost incentives. The system includes an objective function optimizer 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, the predicted cost of performing maintenance on the building equipment, and, if available, the cost incentives.