Integrating Predictive Maintenance Analytics into a Cloud-based CMMS for Smart Work Order Management and Resource Allocation

在维护实践中会产生大量与建筑物、设施和基础设施组件相关的数据。这些数据大多作为维护记录被动地存储在数据库中。数据分析技术提供了一个机会，可以将大量数据转化为有用的信息，并生成知识，以提高未来维护实践的性能、可靠性和效率。在这方面，预测分析不仅可以用于故障预测和诊断（确保物理系统的可靠性），还可以用于维护活动的智能（智能和预测性）管理，以提高资源效率。拟议项目的主要目标是将人工智能技术的研发整合到基于云的维护管理流程中，重点是维护工单管理和资源分配。使用一组精选的聚类方法，识别出一组可区分的属性（特征）和最优（最小）数量的聚类，从而对工单管理模式进行分类。此外，还设计了一个基于智能资源分配规则的系统，以智能的方式分配工作人员，以减少资源强度和提高成本效率。使用决策树分类器，从历史数据中识别出的模式被用来为未来的维护项目生成一组决策规则，从而建立一种资源分配的预测方法。最后，将开发一个基于kpi的评估系统，作为对上述基于人工智能的算法提供反馈的一种手段，以进行验证和改进。

There is a massive amount of data generated in the maintenance practices as related to components of buildings, facilities, and infrastructure. This data is mostly passively stored in databases as maintenance records. Data analytics technologies present an opportunity to turn this large volume of data into useful information and generate knowledge to enhance the performance, reliability, and efficiency of future maintenance practices. In this regard, predictive analytics can be utilized beyond failure prediction and diagnosis (ensuring reliability of physical systems) and towards an intelligent (smart and predictive) management of maintenance activities to enhance resource efficiency. The main objective of the proposed project is to establish R&D into the integration of AI technologies into a cloud-based maintenance administration process with a focus on maintenance work-order management and resource allocation. Using a select set of clustering methods, the set of differentiating attributes (features) and an optimal (minimum) number of clusters are identified to classify work-order management patterns. In addition, a smart resource allocation rule-based systems is designed to assign the staff to the jobs in an intelligent manner such that to reduce resource intensity and improve the cost efficiency. Using decision tree classifiers, the identified patterns from historical data are utilized to generate a set of decision rules for future maintenance projects, establishing a predictive approach toward resource allocation. Finally, a KPIs-based assessment system will be developed as a means of providing feedback to the above AI-based algorithms for validation and improvement purposes.