Data-driven condition-based maintenance models

数据驱动的基于状态的维护模型

• 批准号: 499283-2016
• 负责人: Lee, ChiGuhn
• 金额: $ 10.77万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=656999
• 关键词: Data; driven; condition; based; maintenance

Manufacturing, service, and process-oriented companies can spend 25% to 50% of their total cost of operations on maintenance of critical equipment. Examples of these include mining companies, steel producers, airlines, pulp mills, oil producers, as well as many others. The unavailability of a critical piece of equipment when it is needed can result in severe financial loss to the company. Condition-based maintenance (CBM) is one of the key maintenance tactics employed within organizations with the goal of obtaining as much life out of the equipment as is economically justifiable before making the decision to replace or repair it.****Once CBM is identified as appropriate for a given maintenance situation, making optimal maintenance decisions depends on an understanding of the equipment's condition, the use of appropriate failure and decision models, the selection of a suitable optimization methodology, and a powerful implementation tool. The Centre for Maintenance Optimization and Reliability Engineering (C-MORE) at the University of Toronto has generated significant research outputs in all of these areas. C-MORE now seeks to extend and enhance these achievements in several directions.****We will advance the state of the art in the statistical interpretation of reliability data, mainly to deal with problems with missing information in the early parts of long-lived asset histories. We will investigate degradation models as they fit into frameworks for remaining life optimal decision calculations, along with further advances specifically for remaining life estimation. We will also expand our previous work on CBM with limited data using expert knowledge elicitation and Bayesian statistics.

制造、服务和流程型公司可以将其运营总成本的25%到50%用于关键设备的维护。这些例子包括矿业公司、钢铁生产商、航空公司、纸浆厂、石油生产商以及许多其他公司。关键设备在需要时无法使用，可能会给公司造成严重的财务损失。基于状态的维护(CBM)是组织内部采用的关键维护策略之一，其目标是在做出更换或维修决定之前尽可能延长设备的寿命。*一旦确定CBM适合于给定的维护情况，做出最佳维护决策取决于对设备状况的了解、使用适当的故障和决策模型、选择合适的优化方法和强大的实施工具。多伦多大学维修优化和可靠性工程中心(C-MORE)在所有这些领域都产生了重要的研究成果。C-MORE现在寻求在几个方向上扩展和加强这些成就。*我们将在可靠性数据的统计解释方面推进最先进的技术，主要是处理长期资产历史的早期部分缺少信息的问题。我们将研究退化模型，因为它们适合剩余寿命最优决策计算的框架，以及专门用于剩余寿命估计的进一步进展。我们还将利用专家知识启发和贝叶斯统计，利用有限的数据扩大我们以前在煤层气方面的工作。