I-Corps: Predicting and Preventing Mold Growth and Unforeseen HVAC Equipment Failures with an Intelligent Monitoring and Alerting System

I-Corps项目将提高客户在霉菌和暖通空调问题发生之前识别和缓解问题的能力，使他们能够更好地分配资源，最终节省资金，保持健康的环境，减少浪费。核心技术围绕室内空气质量（IAQ）和暖通空调设备维护的整体方法。超过75%的室内空气质量问题与暖通空调有关，糟糕的室内空气质量导致生产力下降，每年损失约1450万个工作日，美国每年损失超过1680亿美元。由于室内空气质量的改善，这项技术的广泛采用将有助于公司提高生产率。几项研究得出结论，室内空气质量差会导致性能下降。一些研究计算出，仅与霉菌相关的哮喘和鼻窦问题相关的总成本就高达320亿美元的医药费和缺勤费。该方法侧重于暖通空调设备和预防性维护，每年可为制造业节省超过2000亿美元。I-Corps项目的重点是开发一种智能监测和预测系统，该系统能够1)预测未来的霉菌生长，2)预测未来HVAC系统的健康状况，3)向最终用户生成警报、工作指令和维护建议。传统的楼宇自动化系统目前还不存在这样的服务，传统的楼宇自动化系统只监控以能源优化为重点的机械系统，而不是以居住者舒适度为重点的环境。设施管理人员继续对暖通空调系统的维护和霉菌修复采取被动的方法，而不是积极主动的预防方法。目标是评估几种方法，在假设无法访问专有数据的情况下，使用有限的数据量化HVAC系统的健康状态。时间序列分析方法，如矢量化自回归，在较短的预测范围内提供准确的异常检测结果，但不适用于故障隔离。利用文献中的霉菌生长和速率图表，已经开发出一种机器学习算法来准确分类给定温度和湿度读数的风险水平，但需要进一步的工作来考虑累积暴露率或测量波动。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This I-Corps project will increase a customer’s ability to identify and mitigate mold and HVAC issues before they happen, allowing them to better allocate their resources and ultimately save money and maintain a healthy atmosphere and reduce waste. The core technology is centered around a holistic approach to indoor air quality (IAQ) and HVAC equipment maintenance. More than 75% of IAQ problems are HVAC related, and poor IAQ results in reduced productivity of approximately 14.5 million missed workdays a year costing the US more than $168 B annually. Widespread adoption of this technology would help companies increase their productivity due to better indoor air quality. Several studies have concluded that poor indoor air quality results in reduced performance. Some of the studies have calculated the total costs associated with mold-related asthma and sinus issues alone to be up to $32 B in medical bills and absenteeism. The approach focuses on HVAC equipment and enabling preventive maintenance, which may save the manufacturing industry over $200 B annually. This I-Corps project is focused on developing an intelligent monitoring and predictive system that is capable of 1) predicting future mold growth, 2) making predictions of future HVAC system health, and 3) generating alerts, work orders, and maintenance recommendations to the end user. Such a service does not currently exist within traditional building automation systems, which only monitor the mechanical systems with a focus on energy optimization, not the environment with a focus on occupant comfort. Facility managers continue to take a reactive approach to HVAC system maintenance and mold remediation, rather than a proactive, preventative approach. The goal is to evaluate several approaches to quantify the state of health of HVAC systems with limited data under the assumption that access to proprietary data is not possible. Time series analysis methods, such as vectorized auto regression, provide accurate anomaly detection results under a short prediction horizon, but are not suitable for fault isolation. Using mold growth and rate charts from the literature, a machine learning algorithm has been developed to accurately classify the risk level of a given temperature and humidity reading, but further work is needed to account for a cumulative exposure rate or measurement fluctuations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.