SBIR Phase I: A Fleet-based Fault Detection System Based on Clustering Approach

小型企业创新研究(SBIR)第一阶段项目的更广泛影响/商业潜力将是提高多个目标行业的效率和降低运营成本。提出了一种新的基于簇的方法，用于分析相似机器组的性能和预测维护需求。采用这种方法将扩大现有设备监测方法的范围，增加在关键问题发生之前捕获它们的可能性，从而提高安全性和正常运行时间，同时还降低维护成本。通过增加正常运行时间和降低维护成本来提高竞争力--特别是在制造业--对国家工业的生存至关重要。这种方法的广泛适用性--可以在使用类似机器或设备组的各种行业中实施--增加了它产生真正技术和经济影响的潜力。该小型企业创新研究(SBIR)第一阶段项目旨在通过利用从一组类似机器获得的数据流来开发用于预测性能和维护需求的分析模型，从而推进数据驱动的故障检测技术。拟议技术的一个优点是，它适用于新投入使用或最近维修的设备，其历史数据可能有限或根本不存在。使用基于集群的方法来汇总在类似条件下运行的类似机器的数据，从而开发准确的本地化健康评估模型，使机器操作员能够对机器性能进行基准测试，并优化维护活动的优先顺序。该项目将主要包括故障检测和维护，但所提出的方法可以发展到其他基于状态的监测任务，如性能预测和故障诊断。技术原型将通过分立设备的车队进行演示，例如用于汽车制造的工业机器人。该方法也可应用于包含多个类似部件的设备。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be to improve efficiency and reduce operating costs across a number of target industries. An innovative cluster-based approach is proposed for analyzing the performance of groups of similar machines and predicting maintenance requirements. Adopting this approach will broaden the scope of existing equipment monitoring methods, increasing the potential for capturing critical issues before they occur, thereby improving safety and uptime while also reducing maintenance costs. Increased competitiveness - especially in manufacturing - through increased uptime and lower maintenance costs is critical to the viability of the nation's industries. The broad applicability of this approach - which can be implemented in a wide range of industries that utilize groups of similar machines or devices - increases its potential for making real technological and economic impact. This Small Business Innovation Research (SBIR) Phase I project aims to advance data-driven fault detection technology by leveraging data streams obtained from a collection of similar machines to develop analytic models for predicting performance and maintenance requirements. An advantage of the proposed technology is that it is applicable to newly commissioned or recently repaired equipment, for which historical data may be limited or non-existent. A cluster-based approach is utilized to aggregate data from similar machines operating under similar conditions, allowing the development of accurate, localized health assessment models that will enable machine operators to benchmark machine performance and optimally prioritize maintenance activities. This project will primarily cover fault detection and maintenance, but the proposed approach can evolve to other condition based monitoring tasks such as performance prediction and fault diagnosis. A technology prototype will be demonstrated with fleets of discrete equipment, such as industrial robots used in automotive manufacturing. The approach can also be applied to equipment containing a multiplicity of similar components.