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Condition monitoring and intelligent diagnosis of rolling element bearings under constant/variable load and speed conditions

机译：恒定/可变负载和速度条件下滚动元件轴承的状态监测和智能诊断

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摘要

Extensive research has been conducted for intelligent fault diagnosis and prognosis of rolling element bearings, a vital component in every rotating machinery, and many robust and reliable techniques have been developed thus far. The majority of the proposed approaches, however, are established for constant operational conditions and therefore encounter difficulties when working conditions vary, which is common in industrial applications. The reason is that many characteristics of a normal state of a system in one working condition might be similar to the characteristics of a defected one in another working condition. The aim of this paper is to develop a method that can differentiate between different health states of machinery, regardless of load and speed conditions. For this purpose, a newly proposed approach, namely spectral amplitude modulation (SAM), is employed to highlight various components of a signal with different energy levels. Subsequently, the impulsivity of these extracted signals' envelope spectrum is computed to quantify their cyclostationarity level. These quantities could be further utilized as the input variables of machine learning algorithms for automated and intelligent diagnosis of bearings. In this paper, two methods for data classification, namely support vector machine (SVM) and sub-space k-nearest neighbors, are employed. Moreover, the computed impulsiveness of signals contains information about the health state of machinery and therefore could be employed as a health indicator for online condition monitoring of machines. To thoroughly assess the potential of the proposed method for condition monitoring and intelligent diagnosis of machinery in constant and highly variable working conditions, it is implemented on data collected from three distinct test rigs, namely the IMS, PoliTo and FEMTO data sets. The damages on bearings in those experiments have different severity levels, types, and they are located on different components of the bearings. In addition to localized defects, distributed faults, which are advanced and critical stages of defects, are also studied in this research. This type of defect is more difficult to detect and has been largely overlooked due to the fact that the characteristics of its signals are different from localized ones and similar to other modulation sources.
机译：为滚动元件轴承的智能故障诊断和预后进行了广泛的研究，因此迄今为止已经开发了各种旋转机械中的重要组成部分，以及许多稳健且可靠的技术。但是，大多数拟议方法都是为了持续运营条件而建立，因此当工作条件不同时，遇到困难，这在工业应用中很常见。原因是在一个工作条件下系统的正常状态的许多特征可能类似于在另一个工作条件中的缺差的特征。本文的目的是开发一种可以区分不同健康状态的方法，无论负载和速度条件如何。为此目的，采用新提出的方法，即光谱幅度调制（SAM），以突出显示具有不同能量水平的信号的各种组件。随后，计算这些提取的信号的脉冲频谱的冲击以量化其循环棘轮性水平。这些数量可以进一步使用作为机器学习算法的输入变量，用于轴承的自动化和智能诊断。在本文中，采用了两种数据分类方法，即支持向量机（SVM）和子空间k最近邻居。此外，信号的脉冲脉冲包含有关机械健康状况的信息，因此可以用作机器在线状态监测的健康指标。为了彻底评估恒定和高度可变的工作条件中提出的机械状况监测和智能诊断方法的潜力，它是从三个不同的试验台收集的数据，即IMS，POLITO和毫微微数据集。这些实验中的轴承的损坏具有不同的严重程度，类型，以及它们位于轴承的不同部件上。除了局部缺陷之外，还研究了本研究的分布式故障，这些故障是先进的缺陷阶段。由于其信号的特性与局部化并且类似于其他调制来源，这种类型的缺陷更难以检测并且由于其信号的特性以及与其他调制来源而言而言，因此很大程度上被忽略了。

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