Abstract Although researchers have made substantial progress in bearing fault detection and diagnosis recently, incipient fault detection, especially online detection, is still at an initial stage. Generally speaking, online detection of incipient faults is still subject to the following challenges: (1) improving discriminative ability of incipient fault features; (2) adaptive recognition of the distribution inconsistency that exists in online sequential data; (3) achieving automatic detections with avoiding manual adjustment of detection criterion; and (4) reducing false alarm rate. To address these challenges, this paper presents a new approach for bearing incipient fault online detection using semi-supervised architecture and deep feature representation. This approach is simple and effective. First, we extract deep features using stacked denoising auto-encoder from the target bearing's normal state data and an auxiliary bearing's fault state data. Second, we introduce safe semi-supervised support vector machine (S4VM), a kind of semi-supervised classifier, to identify the sequentially arrived data of the target bearing as normal or anomalous. To update the classifier effectively, we use the principal curve to generate synthetic fault data for keeping data classes balanced during online condition monitoring. Finally, we propose a new fault alarm criterion based on S4VM generalization error upper bound to adaptively recognize the occurrence of an incipient fault. The experimental results on three datasets (IEEE PHM Challenge 2012, IMS and XJTU-SY) demonstrate the effectiveness and high reliability of the proposed approach.

中文翻译：

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基于半监督架构和深度特征表示的轴承早期故障在线检测

摘要 尽管近年来研究人员在轴承故障检测与诊断方面取得了长足的进步，但早期故障检测，尤其是在线检测，仍处于起步阶段。一般来说，早期故障的在线检测还面临以下挑战：（1）提高早期故障特征的判别能力；(2)自适应识别在线序列数据中存在的分布不一致；(3)实现自动检测，避免人工调整检测标准；(4)降低误报率。为了应对这些挑战，本文提出了一种使用半监督架构和深度特征表示进行轴承初期故障在线检测的新方法。这种方法简单而有效。第一的，我们使用堆叠去噪自动编码器从目标轴承的正常状态数据和辅助轴承的故障状态数据中提取深度特征。其次，我们引入安全半监督支持向量机（S4VM），一种半监督分类器，将目标轴承的顺序到达数据识别为正常或异常。为了有效地更新分类器，我们使用主曲线生成合成故障数据，以在在线状态监测期间保持数据类别的平衡。最后，我们提出了一种新的基于 S4VM 泛化误差上限的故障报警准则，以自适应地识别早期故障的发生。在三个数据集（IEEE PHM Challenge 2012、IMS 和 XJTU-SY）上的实验结果证明了所提出方法的有效性和高可靠性。