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An Expert System Based on Parametric Net to Support Motor Pump Multi-Failure Diagnostic

机译：基于参数网的电动泵多故障诊断专家系统

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摘要

Early failure detection in motor pumps is an important issue in prediction maintenance. An efficient condition-monitoring scheme is capable of providing warning and predicting the faults at early stages. Usually, this task is executed by humans. The logical progression of the condition-monitoring technologies is the automation of the diagnostic process. To automate the diagnostic process, intelligent diagnostic systems are used. Many researchers have explored artificial intelligence techniques to diagnose failures in general. However, all papers found in literature are related to a specific problem that can appear in many different machines. In real applications, when the expert analyzes a machine, not only one problem appears, but more than one problem may appear together. So, it is necessary to propose new methods to assist diagnosis looking for a set of occurring fails. For some failures, there are not sufficient instances that can ensure good classifiers induced by available machine learning algorithms. In this work, we propose a method to assist fault diagnoses in motor pumps, based on vibration signal analysis, using expert systems. To attend the problems related to motor pump analyses, we propose a parametric net model for multi-label problems. We also show a case study in this work, showing the applicability of our proposed method.
机译：电动泵的早期故障检测是预测维护中的重要问题。一个有效的状态监视方案能够在早期阶段提供警告并预测故障。通常，此任务由人执行。状态监视技术的逻辑进展是诊断过程的自动化。为了使诊断过程自动化，使用了智能诊断系统。许多研究人员已经探索了人工智能技术来诊断故障。但是，文献中发现的所有论文都与一个特定的问题相关，该问题可能出现在许多不同的机器中。在实际应用中，当专家分析机器时，不仅会出现一个问题，而且可能会同时出现多个问题。因此，有必要提出新的方法来帮助诊断寻找一组发生的故障。对于某些故障，没有足够的实例可以确保由可用的机器学习算法引起的良好分类器。在这项工作中，我们提出了一种基于振动信号分析的，使用专家系统的辅助电机泵故障诊断的方法。为了解决与电动泵分析有关的问题，我们提出了用于多标签问题的参数网络模型。我们还在这项工作中展示了一个案例研究，展示了我们提出的方法的适用性。

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  • 来源
  • 会议地点 Thessaloniki(GR);Thessaloniki(GR)
  • 作者

    Flavia Cristina Bernardini; Ana Cristina Bicharra Garcia; Inhauma Neves Ferraz;

  • 作者单位

    ADDLabs - Active Documentation Design Laboratory, UFF - Universidade Federal Fluminense Av. Gal. Milton Tavares de Souza, s° - Campus da Praia Vermelha, Boa Viagem, Niteroi, RJ, Brazil;

    ADDLabs - Active Documentation Design Laboratory IC - Instituto de Computacao UFF - Universidade Federal Fluminense Av. Gal. Milton Tavares de Souza, s° - Campus da Praia Vermelha, Boa Viagem, Niteroi, RJ, Brazil;

    ADDLabs - Active Documentation Design Laboratory IC - Instituto de Computacao UFF - Universidade Federal Fluminense Av. Gal. Milton Tavares de Souza, s° - Campus da Praia Vermelha, Boa Viagem, Niteroi, RJ, Brazil;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 人工智能理论;
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