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Bayesian Networks-based Association Rules and Knowledge Reuse in Maintenance Decision-Making of Industrial Product-Service Systems

机译：基于贝叶斯网络的关联规则和知识重用在工业产品 - 服务系统的维护决策中

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摘要

Equipment manufacturing firms nowadays increasingly provide Industrial Product-Service Systems (IPS 2 ) to improve productivity and service capacity, particularly in the current age of big data. Vast amounts of data are collected using database management systems from areas of product design, manufacturing, marketing, fault detection and maintenance service of IPS 2 . An urgent challenge in context of IPS 2 is how to form reusable knowledge taking advantage of these data records for the sake of guiding subsequent maintenance decision-making. To handle this issue, data mining technology has been used in knowledge acquisition from different databases. However, it needs further investigation how to represent and reuse knowledge mining from these databases in IPS 2 in relation to maintenance decision-making. Given this observation, this study first presents association rules in the form of Bayesian Networks that are mined from different databases of IPS 2 and can be used to represent knowledge acquired. It then establishes a knowledge reuse framework based on Bayesian inference, which is used to support related decision-making in maintenance operations. Lastly, the proposed methodology is applied to a real-world case in an agricultural equipment manufacturing enterprise. The experimental results using real-time data sets illustrate the effectiveness of the proposed methodology in handling maintenance decision-making associated with related fault phenomena.
机译：现在，设备制造公司越来越多地提供工业产品 - 服务系统（IPS 2），以提高生产力和服务能力，特别是在当前大数据的年龄。使用来自产品设计，制造，营销，故障检测和IPS 2的维护服务的数据库管理系统来收集大量数据。 IPS 2背景下的紧急挑战是如何形成可重复使用的知识，以利用这些数据记录，以引导后续的维护决策。要处理此问题，数据挖掘技术已被用于来自不同数据库的知识获取。但是，它需要进一步调查如何在IPS 2中与维护决策相关的这些数据库中的知识挖掘。鉴于此观察，本研究首先呈现出于从IPS 2的不同数据库中开采的贝叶斯网络形式的关联规则，并且可用于代表所获取的知识。然后，它建立了基于贝叶斯推论的知识重用框架，该框架用于支持维护操作中的相关决策。最后，拟议的方法适用于农业设备制造企业的真实案例。使用实时数据集的实验结果说明了所提出的方法在处理与相关故障现象相关的维护决策方面的有效性。

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