Self-learning fault detection for HVAC systems
US10592821B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 20, 2016 |
| Grant date | Mar 17, 2020 |
| Priority date | — |
| Expiry date | Nov 21, 2037 |
Classification
- Technology area (CPC F)Mechanical Engineering; Lighting; Heating
- CPC primaryF24F11/64
- WIPO fieldThermal processes and apparatus
- WIPO sectorMechanical engineering
Abstract
Systems and methods for self-learning fault detection and diagnosis in an HVAC system include a server identifying a fault and one or more predicted causes of the fault based on measurements of operational parameters received from sensors associated with the HVAC system. The operational parameters are compared to evaluation criteria, such as predetermined thresholds, to identify a potential fault. Parameters may be weighted, and optionally scaled to a standardized range to facilitate the diagnosis of HVAC systems of disparate configurations and capacities. Evaluation criteria for each fault are periodically analyzed in view of operational parameter history to identify new criteria having a lower probability of misdiagnosis. Fault detection criteria which are determined to have an unacceptable error rate may be deactivated or flagged for review.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.