Bearings are essential components in rotating machines. They ensure the rotation and power transmission. So, these components are essential elements for industrial machines. Thus, real-time monitoring is required to detect a possible anomaly, diagnose the failure of rolling bearing and follow its evolution. This paper presents a methodology for automatic online implementation of fault diagnosis of rolling bearings, by AOC-OPTICS (automatic online classification monitoring based on ordering points to identify clustering structure, OPTICS). The algorithm consists of three phases namely: initialization, detection and follow-up. These phases use the combination of features extraction methods, smart ranking, features weighting and classification by the OPTICS method. Two methods have been integrated in the dimension reduction step to improve the efficiency of detection and the followed of the defect (relief method and t-distributed stochastic neighbor embedding method). Thus, the determination of the internal parameters of the OPTICS method is improved. A regression model and exponential model are used to track the fault. The analytical simulations discuss the influence of parameters automation. Experimental validation shows detection with 100% accuracy and regression models of monitoring reaching R2=0.992.

中文翻译：

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AOC-OPTICS：滚动轴承状态监测的自动在线分类

轴承是旋转机械中必不可少的组件。它们确保旋转和动力传递。因此，这些组件是工业机械必不可少的元素。因此，需要实时监视以检测可能的异常，诊断滚动轴承的故障并跟踪其发展。本文提出了一种通过AOC-OPTICS（基于订购点的自动在线分类监控以识别聚类结构的自动在线分类监控系统，OPTICS）自动进行滚动轴承故障诊断的方法。该算法包括三个阶段：初始化，检测和跟进。这些阶段结合了特征提取方法，智能排名，特征加权和OPTICS方法分类。降维步骤中集成了两种方法，以提高检测效率和缺陷的跟踪效率（浮雕方法和t分布随机邻居嵌入方法）。因此，改进了OPTICS方法的内部参数的确定。回归模型和指数模型用于跟踪故障。解析仿真讨论了参数自动化的影响。实验验证表明检测具有100％的准确性，并且监测的回归模型达到R2 = 0.992。解析仿真讨论了参数自动化的影响。实验验证表明检测具有100％的准确性，并且监测的回归模型达到R2 = 0.992。解析仿真讨论了参数自动化的影响。实验验证表明检测具有100％的准确性，并且监测的回归模型达到R2 = 0.992。