A Systematic Semi-Supervised Self-adaptable Fault Diagnostics approach in an evolving environment

Yang Hu; Piero Baraldi; Francesco Di Maio; Enrico Zio

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A Systematic Semi-Supervised Self-adaptable Fault Diagnostics approach in an evolving environment

机译：不断变化的环境中的系统性半监督自适应故障诊断方法

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摘要

Fault diagnostic methods are challenged by their applications to industrial components operating in evolving environments of their working conditions. To overcome this problem, we propose a Systematic Semi-Supervised Self-adaptable Fault Diagnostics approach (4SFD), which allows dynamically selecting the features to be used for performing the diagnosis, detecting the necessity of updating the diagnostic model and automatically updating it. Within the proposed approach, the main novelty is the semi-supervised feature selection method developed to dynamically select the set of features in response to the evolving environment. An artificial Gaussian and a real world bearing dataset are considered for the verification of the proposed approach.

机译：故障诊断方法因其在不断变化的工作环境中运行的工业组件的应用而受到挑战。为解决此问题，我们提出了一种系统的半监督自适应故障诊断方法（4SFD），该方法可动态选择要用于执行诊断的功能，检测更新诊断模型并自动对其进行更新的必要性。在提出的方法中，主要新颖之处在于半监督特征选择方法，该方法被开发为响应不断变化的环境动态选择特征集。考虑使用人工高斯和真实世界的轴承数据集来验证所提出的方法。

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《Mechanical systems and signal processing》 |2017年第5期|413-427|共15页
作者
Yang Hu; Piero Baraldi; Francesco Di Maio; Enrico Zio;
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作者单位

Politecnico di Milano, Department of Energy, Via La Masa, 20156 Milan, Italy;

Politecnico di Milano, Department of Energy, Via La Masa, 20156 Milan, Italy;

Politecnico di Milano, Department of Energy, Via La Masa, 20156 Milan, Italy;

Politecnico di Milano, Department of Energy, Via La Masa, 20156 Milan, Italy,Chair System Science and the Energy Challenge, Fondation Electricite de France (EDF), CentraleSupelec, Universite Paris Saclay,Grande Voie des Vignes, 92290 Chatenay-Malabry, France;

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正文语种 eng
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关键词
Evolving environment; Feature selection; Concept drift; Drift detection; Fault diagnostics; Bearing faults;

机译：不断变化的环境;功能选择;概念漂移;漂移检测;故障诊断;轴承故障;

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1. A Never-Ending Learning Method for Fault Diagnostics in Energy Systems Operating in Evolving Environments (12, 4802, 2019) [J] . Industrial and organizational psychology . 2020,第2期

机译：在不断发展环境中运行的能量系统中的故障诊断的永无止境学习方法（12,4802,2019）
2. Deep semi-supervised generative adversarial fault diagnostics of rolling element bearings [J] . Verstraete David Benjamin, Lopez Droguett Enrique, Meruane Viviana, Structural health monitoring . 2020,第2期

机译：滚动轴承的深半监督生成对抗故障诊断
3. A Semi-Supervised Diagnostic Framework Based on the Surface Estimation of Faulty Distributions [J] . Razavi-Far Roozbeh, Hallaji Ehsan, Farajzadeh-Zanjani Maryam, IEEE transactions on industrial informatics . 2019,第3期

机译：基于故障分布的表面估计的半监督诊断框架
4. A Compacted Object Sample Extraction (COMPOSE)-based method for fault diagnostics in evolving environment [C] . Yang Hu, Piero Baraldi, Francesco Di Maio, IEEE Prognostics and System Health Management Conference . 2015

机译：基于压缩对象样本提取（COMPOSE）的方法，用于不断发展的环境中的故障诊断
5. Improved methods of vibration measurement, gear fault detection and bearing fault detection for gearbox diagnostics. [D] . Limmer, Joel David. 1997

机译：振动测量，齿轮故障检测和轴承故障检测的改进方法，用于齿轮箱诊断。
6. A Semi-Supervised Approach to Bearing Fault Diagnosis under Variable Conditions towards Imbalanced Unlabeled Data [O] . Xinan Chen, Zhipeng Wang, Zhe Zhang, 2018

机译：可变条件下针对不平衡未标记数据的轴承故障诊断的半监督方法
7. A Systematic Semi-Supervised Self-adaptable Fault Diagnostics approach in an evolving environment [O] . Hu, Yang, Baraldi, Piero, Maio, Francesco,, 2017

机译：不断变化的环境中的系统性半监督自适应故障诊断方法
8. Integrated Approach for Aircraft Engine Performance Estimation and Fault Diagnostics. [R] . Imon, D. L., Armstrong, J. B. 2012

机译：飞机发动机性能评估与故障诊断的集成方法。

A Systematic Semi-Supervised Self-adaptable Fault Diagnostics approach in an evolving environment

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