Towards proactive maintenance of buried infrastructure with cloud-based sensing and predictive analytics

通过基于云的传感和预测分析来主动维护埋地基础设施

• 批准号: RGPIN-2017-04408
• 负责人: Liu, Zheng
• 金额: $ 1.53万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=678538
• 关键词: Towards; proactive; maintenance; buried; infrastructure

The condition of buried infrastructure including water, wastewater, and storm water networks is of great importance to people's quality of life and has strong environmental, economic and social implications. A recent municipal buried infrastructure survey across Canada identified water pollution, safety and state of water supply systems as the greatest issues to this country. City water leaks are wasting millions of tax dollars. Utilities are addressing these issues primarily with a reactive approach. A performance-based approach is needed to identify if individual water pipelines meet the required level of service. However, performance-based management of these buried assets has historically not been performed due to technical and financial limitations. A proactive, uniform, and systematic approach for maintenance of buried infrastructure would be more efficient and preferred.******To achieve the proactive maintenance of buried infrastructure, it is essential to collect all the relevant data and information from the pipelines to determine future pipe condition. Currently, in-situ monitoring as well as non-destructive inspection are performed to collect the data. However, these data are saved in different formats and stored in varied places. As these data are not properly matched and aligned to each other, predictive models can only be built with isolated data. A comprehensive prediction based on accumulated inspection data can be used to develop next-generation proactive maintenance. This research program will fill the technical gaps by incorporating modern information and communications technologies, e.g. Internet of Things, cloud computing, and advanced machine learning, into the buried infrastructure management. ******The long-term objective of the research program is to advance condition assessment technologies and predictive analytics to achieve the sustainability of buried infrastructure through proactive maintenance. In the short-term, the research program will develop a cloud-based sensing framework, which will enable centralized data collection, storage, and management, and develop predictive analytics to derive actionable information from big inspection data. ******Proactive maintenance allows preventive and corrective actions from water utilities to be properly and efficiently scheduled. Thus, the impacts brought to Canada's buried infrastructure include higher system reliability, reduced costs of pipe failures, minimized time on maintenance, and optimized maintenance interval. The proposed research will also offer a unique cross-disciplinary training opportunity for HQP. The HQP will gain in-depth knowledge and understanding of more than one discipline, e.g. electrical, civil, and mechanical engineering, and be able to solve practical problems without regard to disciplinary boundaries.

地下基础设施的状况，包括供水、废水和雨水网络，对人们的生活质量至关重要，并具有很强的环境、经济和社会影响。最近在加拿大进行的一项市政地下基础设施调查发现，水污染、安全和供水系统状况是该国最大的问题。城市漏水浪费了数百万的税款。公用事业公司主要采用被动的方法来解决这些问题。需要一种基于性能的方法来确定单个水管是否满足所需的服务水平。然而，由于技术和财政限制，这些埋藏资产的绩效管理历来没有得到执行。一个积极的，统一的，系统的方法来维护埋在地下的基础设施将是更有效和首选。为了实现对埋地基础设施的主动维护，必须从管道中收集所有相关数据和信息，以确定未来的管道状况。目前，进行现场监测以及非破坏性检查来收集数据。然而，这些数据以不同的格式保存，并存储在不同的地方。由于这些数据没有正确匹配和相互对齐，预测模型只能用孤立的数据来构建。基于累积的检测数据的综合预测可用于开发下一代主动维护。该研究计划将通过将现代信息和通信技术（例如物联网，云计算和先进的机器学习）纳入地下基础设施管理来填补技术空白。** 该研究计划的长期目标是推进状态评估技术和预测分析，通过主动维护实现地下基础设施的可持续性。在短期内，该研究计划将开发一个基于云的传感框架，该框架将实现集中的数据收集，存储和管理，并开发预测分析，以从大检查数据中获取可操作的信息。** 主动维护允许自来水公司采取适当和有效的预防和纠正措施。因此，对加拿大地下基础设施的影响包括更高的系统可靠性，降低管道故障的成本，最大限度地减少维护时间，优化维护间隔。 拟议的研究还将为HQP提供一个独特的跨学科培训机会。HQP将获得深入的知识和了解一个以上的学科，如电气，土木和机械工程，并能够解决实际问题，而不考虑学科界限。