EAGER: Collaborative: Predictive Maintenance of HVAC Systems using Audio Sensing

EAGER：协作：使用音频传感对 HVAC 系统进行预测性维护

• 批准号: 1619955
• 负责人: Ravi Srinivasan
• 金额: $ 2.98万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-03-01 至 2018-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1619955&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Predictive; Maintenance; HVAC

Acoustic sensing-based preventive maintenance approach focuses on mapping auditory information, captured from mechanical systems in buildings, to their health status and probability of impending failures. An important application of this methodology is reducing energy waste in commercial heating, ventilating, and air-conditioning (HVAC) systems, which accounts for over 42% of the total U.S. commercial building energy usage. The outcome of this project is a robust acoustic sensing technology that has a high accuracy in predicting actual failures of HVAC systems. This research will be integrated with new user interfaces that will allow building managers to virtually navigate the equipment and appliances in large buildings (or collections of buildings), and to quickly identify potential failures.This EArly-Concept Grants for Exploratory Research (EAGER) project addresses the following technology gaps as it translates from research discovery toward commercial applications: (a) ensuring privacy, and (b) minimizing false positives in predicting equipment failure. This project develops acoustic signal acquisition and processing techniques that preserve the privacy of everyone and everything that is susceptible to privacy violations due to continuous acoustic monitoring. The proposed collaborative research enables buildings to be retrofitted with a low-cost, acoustic sensing solution to monitor its HVAC systems to predict their impending failures. A major goal of this project is to reduce false positives when making these predictions that are primarily caused by inadequate modeling of sounds from a faulty component, inadequate modeling of different types of faults, and errors in sound source recognition. Furthermore, this project creates a foundation for the next generation of intelligent systems that autonomously monitor equipment and predict failure.The project engages University of Florida and University of North Carolina at Chapel Hill to augment research capability in conducting visualization-based dynamic assessment of HVAC systems, and building low-cost, embedded device-based centralized HVAC monitoring systems. With a cloud-connected network of embedded audio monitoring devices deployed in the University of Florida campus buildings for running acoustic processing and classification tasks, this novel and transformative technology is aimed at identifying and solving challenges in large-scale, commercial-grade deployment of such systems in real world scenarios. This project will engage an industrial partner to develop privacy-preserving algorithms, build test environments, and guide commercialization aspects of this technology.

基于声学传感的预防性维护方法的重点是将从建筑物中的机械系统捕获的听觉信息映射到其健康状态和即将发生的故障的概率。这种方法的一个重要应用是减少商业供暖、通风和空调（HVAC）系统中的能源浪费，该系统占美国商业建筑能源使用总量的42%以上。该项目的成果是一种强大的声学传感技术，在预测HVAC系统的实际故障方面具有很高的准确性。这项研究将与新的用户界面相结合，使建筑管理人员能够虚拟地导航大型建筑物中的设备和电器EARLY概念探索性研究赠款（EAGER）项目解决了以下技术差距，因为它从研究发现转化为商业应用：（a）确保隐私，以及（B）最小化在预测设备故障中的误报。该项目开发声学信号采集和处理技术，保护每个人的隐私，以及由于持续的声学监测而容易受到隐私侵犯的一切。拟议的合作研究使建筑物能够采用低成本的声学传感解决方案进行改造，以监测其HVAC系统，预测即将发生的故障。该项目的一个主要目标是在进行这些预测时减少误报，这些预测主要是由故障组件的声音建模不足，不同类型故障的建模不足以及声源识别错误引起的。此外，该项目还为下一代自主监控设备和预测故障的智能系统奠定了基础。该项目与佛罗里达大学和查佩尔山的北卡罗来纳州大学合作，以增强对HVAC系统进行基于可视化的动态评估的研究能力，并构建低成本的基于嵌入式设备的集中式HVAC监控系统。通过在佛罗里达大学校园建筑中部署的嵌入式音频监控设备的云连接网络，用于运行声学处理和分类任务，这种新颖的变革性技术旨在识别和解决真实的世界场景中大规模商业级部署此类系统的挑战。该项目将与工业合作伙伴合作开发隐私保护算法，构建测试环境，并指导该技术的商业化方面。