HVAC Predictive Maintenance Using Time-Series Failure Forecasting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Operating and maintaining large HVAC systems is a time-consuming and labor-intensive process, often requiring manual confirmation by facility engineers, which can lead to system inoperability when engineers are unavailable due to device failures.
Innovation Solution
A controller system that collects performance data, generates electronic models based on auto-correlation and partial auto-correlation functions, predicts future events such as device failures or maintenance needs, and automatically configures devices to prevent downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual confirmation by facility engineers is used to monitor HVAC devices, then system reliability is maintained through human expertise, but labor time and operational costs increase significantly
Solution Approach 1:
The system enables self-service by allowing HVAC devices to automatically monitor their own performance parameters, detect anomalies, and trigger maintenance alerts without human intervention. The embedded sensors and processors continuously assess device health and autonomously initiate diagnostic routines, freeing facility engineers from routine monitoring tasks while maintaining system reliability through automated self-diagnosis and self-reporting capabilities.
Solution Approach 2:
The patent replaces the mechanical system of manual human inspection with an automated electronic monitoring system. Sensors, processors, and communication modules substitute for human engineers physically checking devices, transforming the monitoring function from a labor-intensive manual process to an automated electronic system that continuously collects and analyzes performance data without requiring human presence.
2Reliability
If facility engineers manually monitor HVAC devices, then device failures can be detected, but system downtime increases when engineers are unavailable
Solution Approach 1:
The system ensures continuity of useful action by implementing 24/7 automated monitoring that never sleeps or requires breaks. Sensors continuously track performance parameters, and processors constantly analyze data streams, ensuring uninterrupted detection of device failures regardless of engineer availability. This continuous automated surveillance eliminates gaps in monitoring that occur during off-hours or when engineers are occupied with other tasks.
Solution Approach 2:
The patent introduces an intermediary automated monitoring system between the HVAC devices and human engineers. This intermediary layer of sensors, processors, and communication modules acts as a buffer that detects and reports failures immediately, allowing engineers to respond to alerts rather than continuously monitor devices. This intermediary system ensures failure detection capability is maintained while reducing dependency on engineer availability.
3Productivity
If automated predictive maintenance systems are implemented, then system availability and productivity improve through reduced downtime, but device complexity and initial costs increase
Solution Approach 1:
The system applies segmentation by dividing the predictive maintenance functionality into modular components: sensor modules for data collection, processing modules for analysis, communication modules for alert transmission, and actuation modules for automated responses. Each module performs a specific function and can be independently configured or replaced, reducing overall system complexity through functional decomposition while maintaining high availability through coordinated operation of these segmented components.
Data Source
AI summary
Systems and methods for operating an energy plant are disclosed herein. A time series of performance variable associated with a device in the energy plant is obtained. An auto-correlation function data of the device is obtained based on the time series of the performance variable associated with the device. An electronic model of the device is generated based on the auto-correlation function data. Time, at which a future event of the device is predicted to occur, is predicted based on the electronic model. A report indicating the future event of the device and the predicted time may be generated. The device may be automatically configured, according to the future event and the predicted time.