The monitoring of rotating machinery condition has been a critical component of the Industry 4.0 revolution in enhancing machine reliability and facilitating intelligent manufacturing. The introduction of condition-based monitoring has effectively reduced the catastrophic events and maintenance cost across various industries. One of the major challenges of the diagnosis remains as majority of the diagnostic model requires off-line analysis and human intervention. The offline analysis, which is normally done by previous experience, involves tuning model parameters to improve the performance of the diagnostic model. However, for newly developed models, the knowledge of the unknown parameters does not exist. One way to resolve this issue is through learning using adaptation. The adaptation algorithm adjusts itself by newly acquired data. Hence, improvement of the model performance is achieved. In this paper, a nonlinear adaptive dictionary learning algorithm is proposed to achieve early fault detection of bearing elements without using the conventional computation heavy algorithm to update the dictionary. Deterministic and random data separation is implemented using the autoregressive model to reduce the background noise. The filtered data is further analyzed by the Infogram to reveal the impulsiveness and cyclostationary signature of the vibration signal. The dictionary is initialized using random parameters. Instead of using the k means singular value decomposition algorithm to compute the dictionary for adaptation, the unscented Kalman filter (UKF) is implemented to update the dictionaries using the filtered signal from the Infogram. The updating algorithm does not require computation of the dictionary, and no previous knowledge of the dictionary's parameters is needed. The updated dictionary contains the detected fault signature from the Infogram and, therefore, is used for further fault analysis. The proposed algorithm has the advantage of self-adaptation, the capability to map the non-linear relationship of the signal and dictionary weights. The algorithm can be used in the various condition-based monitoring of rotating machineries to avoid additional human efforts and improve the performance of the diagnostic model.

中文翻译：

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非线性自适应字典学习的轴承故障诊断。


旋转机械状态监测一直是工业 4.0 革命在提高机器可靠性和促进智能制造方面的关键组成部分。状态监测的引入有效减少了各行业的灾难性事件和维护成本。诊断的主要挑战之一仍然是，因为大多数诊断模型需要离线分析和人工干预。离线分析通常根据以前的经验来完成，涉及调整模型参数以提高诊断模型的性能。然而，对于新开发的模型，不存在未知参数的知识。解决这个问题的一种方法是通过适应来学习。自适应算法通过新获取的数据进行自我调整。因此，实现了模型性能的提高。本文提出了一种非线性自适应字典学习算法，无需使用传统计算量大的算法来更新字典，即可实现轴承元件的早期故障检测。使用自回归模型实现确定性和随机数据分离，以减少背景噪声。信息图进一步分析过滤后的数据，以揭示振动信号的脉冲性和循环平稳特征。字典使用随机参数初始化。不是使用 k 均值奇异值分解算法来计算适应字典，而是使用无迹卡尔曼滤波器 (UKF) 来使用来自 Infogram 的滤波信号来更新字典。更新算法不需要计算字典，并且不需要预先知道字典的参数。 更新后的字典包含从信息图中检测到的故障签名，因此可用于进一步的故障分析。该算法具有自适应的优点，能够映射信号与字典权重的非线性关系。该算法可用于旋转机械的各种基于状态的监测，以避免额外的人力工作并提高诊断模型的性能。