Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park, PA 16802, USA.
Sensors (Basel). 2021 Nov 17;21(22):7633. doi: 10.3390/s21227633.
Bayesian Network (BN) models are being successfully applied to improve fault diagnosis, which in turn can improve equipment uptime and customer service. Most of these BN models are essentially trained using quantitative data obtained from sensors. However, sensors may not be able to cover all faults and therefore such BN models would be incomplete. Furthermore, many systems have maintenance logs that can serve as qualitative data, potentially containing historic causation information in unstructured natural language replete with technical terms. The motivation of this paper is to leverage all of the data available to improve BN learning. Specifically, we propose a method for fusion-learning of BNs: for quantitative data obtained from sensors, metrology data and qualitative data from maintenance logs, corrective and preventive action reports, and then follow by fusing these two BNs. Furthermore, we propose a human-in-the-loop approach for expert knowledge elicitation of the BN structure aided by logged natural language data instead of relying exclusively on their anecdotal memory. The resulting fused BN model can be expected to provide improved diagnostics as it has a wider fault coverage than the individual BNs. We demonstrate the efficacy of our proposed method using real world data from uninterruptible power supply (UPS) fault diagnostics.
贝叶斯网络 (BN) 模型正被成功应用于提高故障诊断能力，这反过来又可以提高设备的正常运行时间和客户服务水平。这些 BN 模型大多数都是基于从传感器获得的定量数据进行训练的。然而，传感器可能无法覆盖所有的故障，因此这样的 BN 模型将是不完整的。此外，许多系统都有维护日志，这些日志可以作为定性数据，可能包含以非结构化自然语言形式出现的历史因果关系信息，其中充满了技术术语。本文的动机是利用所有可用的数据来改进 BN 学习。具体来说，我们提出了一种融合学习 BN 的方法：对于从传感器获得的定量数据、计量数据以及从维护日志、纠正和预防措施报告中获得的定性数据，然后融合这两个 BN。此外，我们提出了一种人机交互的方法，通过记录的自然语言数据来辅助专家知识的启发式获取，而不是仅仅依赖他们的轶事记忆。融合后的 BN 模型有望提供更好的诊断效果，因为它比单个 BN 具有更广泛的故障覆盖范围。我们使用不间断电源 (UPS) 故障诊断的实际数据证明了我们提出的方法的有效性。
