Harnessing fuzzy neural network for gear fault diagnosis with limited data labels
Harnessing fuzzy neural network for gear fault diagnosis with limited data labels
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DOI：
10.1007/s00170-021-07253-6
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发表时间：
2021-05
期刊：
影响因子：
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通讯作者：
K. Zhou;Jiong Tang
中科院分区：
文献类型：
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作者：
K. Zhou;Jiong Tang
齿轮系统的故障诊断与预测在现代制造业中起着重要的作用。虽然基于第一原理的逆分析受到各种限制，但近年来，数据驱动的方法（如许多机器学习技术）显示出了巨大的前景。然而，重大挑战依然存在。机器学习通常需要大量高质量的训练数据，这可能不适用于许多工业系统。特别地，虽然齿轮故障本质上是连续的并且呈现出许多不同的场景，但是在实际情况下，由于数据采集的高成本，特别是对于故障场景，只有少量的离散故障类别，即，故障类型和严重程度，可以记录并用于培训。因此，经过训练的神经网络在实际实现时需要处理看不见的故障。为了应对这一挑战，在这项研究中，我们开发了一种模糊分类方法，能够处理不包括在训练数据集中的故障场景。通过一个模糊化过程的集成，这种模糊神经网络（FNN）可以产生分类结果的概率和置信度。一个看不见的故障场景将被分类到最近的故障类的概率，有效地在有限的数据产生的诊断结果。而齿轮振动信号中的故障特征是隐藏的，并具有复杂的非线性关系的故障情况下，它被发现，核主元分析（KPCA）可以使模糊神经网络，以促进故障特征的相关性。系统的案例研究，使用实验室规模的齿轮系统获得的实验数据进行验证的新方法。
Diagnosis and prognosis of gear systems play an important role in modern manufacturing. While first-principle-based inverse analysis is subject to various limitations, data-driven approaches such as many machine learning techniques have shown great promise in recent years. Nevertheless, major challenges remain. Machine learning generally requires large amount of high-quality training data which may not be available for many industrial systems. In particular, while gear faults are continuous in nature and exhibit many different scenarios, in practical situations owing to the high cost in data acquisition especially for fault scenarios, only a small number of discrete classes of faults, i.e., fault types and severities, can be recorded and employed in training. As such, the neural networks trained will need to deal with unseen faults when they are actually implemented. To tackle this challenge, in this research, we develop a fuzzy classification approach capable of handling fault scenarios that are not included in the training dataset. Through the integration of a fuzzification procedure, this fuzzy neural network (FNN) can produce classification outcome with probability and confidence level. An unseen fault scenario will be classified into the nearest fault class with probability, effectively yielding the diagnosis result under limited data. While fault features in gear vibration signals are hidden and have complex nonlinear relations with respect to fault scenarios, it is found that the kernel principal component analysis (KPCA) can enable the FNN to facilitate the correlation of fault features. Systematic case studies using experimental data acquired from a lab-scale gear system are carried out to validate the new approach.