HVAC Actuator Failure Prediction Using Internal Operational Data
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Solution Overview
Problem
Conventional HVAC actuators lack the ability to predict equipment failure, leading to unexpected breakdowns and inefficient operation, as they only provide feedback on actuator position without reporting operational data.
Innovation Solution
A system that includes an actuator with a processing circuit to collect internal data characterizing its operation and a communications circuit to transmit this data, coupled with a controller that uses this data to predict equipment failure through a failure predictor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional actuators only output position feedback signals, then the device complexity is reduced, but the ability to predict equipment failure is lost
Solution Approach 1:
The actuator collects operational data (current, torque, position) and transmits it to the controller before equipment failure occurs. The failure predictor analyzes this data to forecast when equipment will fail, enabling proactive maintenance before the actual failure happens.
Solution Approach 2:
The system implements a feedback loop where the actuator continuously monitors its own operational parameters and transmits this data to the controller. The failure predictor uses this feedback information to assess equipment health and predict future failures, creating a closed-loop monitoring system.
2Loss of time
If actuators collect and transmit internal operational data, then equipment failure can be predicted, but the device complexity increases
Solution Approach 1:
The processing circuit collects operational data and the communications circuit transmits it to the controller before failure occurs. This preliminary data collection and transmission enables the failure predictor to forecast equipment failure, allowing maintenance to be scheduled before downtime occurs.
Solution Approach 2:
The actuator performs self-diagnosis by collecting its own operational data (current, torque, position) through the processing circuit. This self-monitoring capability allows the system to assess its own health status without external intervention, reducing the need for manual inspections.
3Force
If the actuator increases current to compensate for friction, then the torque increases, but equipment failure occurs sooner due to accelerated wear
Solution Approach 1:
The failure predictor analyzes operational data to predict when equipment will fail due to wear from increased torque. By forecasting the remaining useful life of the equipment, the system can schedule maintenance before catastrophic failure occurs, optimizing the balance between maintaining sufficient torque and extending equipment lifespan.
Solution Approach 2:
The system dynamically adjusts the interpretation of operational data based on equipment age and usage patterns. As equipment accumulates wear from increased torque, the failure predictor updates its forecasts to account for accelerated degradation, allowing for dynamic maintenance scheduling that adapts to the changing condition of the equipment.
Data Source
AI summary
A system for predicting HVAC equipment failure includes an actuator and a controller. The actuator is coupled to the HVAC equipment and configured to drive the HVAC equipment between multiple positions. The actuator includes a processing circuit configured to collect internal actuator data characterizing an operation of the actuator and a communications circuit coupled to the processing circuit. The communications circuit is configured to transmit the internal actuator data outside the actuator. The controller is configured to provide control signals to the actuator and receive the internal actuator data from the actuator. The controller includes a failure predictor configured to use the internal actuator data to predict a time at which the HVAC equipment failure will occur.