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

这个I-Corps项目将提高客户在霉菌和HVAC问题发生之前识别和缓解这些问题的能力，使他们能够更好地分配资源，最终节省资金，保持健康的氛围并减少浪费。核心技术围绕室内空气质量（IAQ）和HVAC设备维护的整体方法。超过75%的IAQ问题与HVAC有关，而较差的IAQ导致每年约1450万个工作日的生产率降低，每年给美国造成超过1680亿B美元的损失。这项技术的广泛采用将有助于公司提高生产力，因为室内空气质量更好。一些研究得出结论，室内空气质量差会导致性能下降。一些研究已经计算出与霉菌相关的哮喘和鼻窦问题的总成本高达32 B美元的医疗费用和缺勤。该方法侧重于HVAC设备和实现预防性维护，这可以每年为制造业节省超过200 B美元。该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）预测未来的HVAC系统健康状况，3）向最终用户生成警报，工作订单和维护建议。这种服务目前不存在于传统的楼宇自动化系统中，传统的楼宇自动化系统仅监控关注能量优化的机械系统，而不是关注居住者舒适度的环境。设施管理人员继续采取被动的方法来进行HVAC系统维护和模具修复，而不是积极主动的预防方法。我们的目标是评估几种方法来量化的健康状态的HVAC系统有限的数据下，访问专有数据是不可能的假设。时间序列分析方法，如向量化自回归，提供准确的异常检测结果在一个短的预测范围内，但不适合故障隔离。利用文献中的霉菌生长和速率图表，开发了一种机器学习算法，可以准确地对给定温度和湿度阅读的风险级别进行分类，但需要进一步的工作来解释累积暴露率或测量波动。该奖项反映了NSF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.