Effectiveness of online bearing status monitoring (OBSM) depends deeply on the online data processing ability and the sensitivity of data features used to recognize the mechanical-system dynamic response change. Focusing on these, we present a novel method of OBSM based on singular spectrum analysis (SSA) and adaptive neuro-fuzzy inference system (ANFIS) with the highlights as follows. A sensitive and stable multi-feature is discovered to better the ability to distill the valuable information in noisy and massive databases (NMDs) and process impulse-noise in them. The SSA-based high-frequency noise removal solution, the ANFIS’ interpolating and identifying capability, and the dual function of the proposed multi-feature are combined in a new algorithm named AfOBSM for building a system of OBSM through two phases, offline and online. The offline is to identify the mechanical-system in the presence of the typical kinds of bearing faults. The ANFIS is trained in this phase using a training dataset. Meanwhile, the online is to estimate online the real status of the bearing(s) based on the trained ANFIS and a monitoring dataset. Surveys from an experimental-system were performed. The obtained results showed the positive effects of the AfOBSM.

中文翻译：

---

基于ANFIS的轴承故障在线识别

在线轴承状态监测（OBSM）的有效性在很大程度上取决于在线数据处理能力和用于识别机械系统动态响应变化的数据特征的敏感性。围绕这些，我们提出了一种基于奇异谱分析（SSA）和自适应神经模糊推理系统（ANFIS）的OBSM新方法，重点如下。发现了一种敏感且稳定的多特征，可以更好地提取嘈杂和海量数据库 (NMD) 中的有价值信息并处理其中的脉冲噪声。将基于 SSA 的高频噪声去除解决方案、ANFIS 的插值和识别能力以及所提出的多特征的双重功能结合在名为 AfOBSM 的新算法中，通过离线和在线两个阶段构建 OBSM 系统. 离线是在存在典型类型的轴承故障时识别机械系统。在此阶段使用训练数据集训练 ANFIS。同时，在线是基于经过训练的 ANFIS 和监控数据集在线估计轴承的真实状态。进行了来自实验系统的调查。获得的结果显示了 AfOBSM 的积极作用。