In modern industrial systems, the processes often operate under different modes. Potential fault diagnosis and mode identification are extremely vital to maintain the system safe and reliable. Recently, many methods have been proposed to address these problems separately. Moreover, many of them make an assumption that the data from industrial site only contain the Gaussian noise. However, this assumption is not held in practice, which further reduces their performances. Considering the complicated noise feature of industrial data, we came up with an improved dictionary learning method to settle these problems simultaneously. First, the measurement data were decomposed into three parts: clean data, mode-based noise, and dense Gaussian noise. Then, the dictionary learning method was proposed to characterize each part separately. Inspired by the framework of label-consistent K-SVD, the mode information was incorporated into the dictionary learning method, and we developed a solution to settle the multiple dictionary learning optimization problem. Finally, when new samples arrive, we reconstruct them under the learned dictionary so that each sample's mode and abnormal data and can be determined. The experiments on two different types of simulated process and aluminum electrolysis process show the strength and reliability of our method, which indicates the engineering application value of the proposed method.

中文翻译：

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基于多词典学习的多模式过程监控与模式识别


在现代工业系统中，流程通常在不同的模式下运行。潜在故障诊断和模式识别对于维护系统安全可靠至关重要。最近，已经提出了许多方法来分别解决这些问题。此外，他们中的许多人假设来自工业现场的数据仅包含高斯噪声。然而，这种假设在实践中并不成立，这进一步降低了它们的性能。考虑到工业数据复杂的噪声特征，我们提出了一种改进的字典学习方法来同时解决这些问题。首先，测量数据被分解为三部分：干净数据、基于模式的噪声和密集高斯噪声。然后，提出了字典学习方法来分别表征每个部分。受标签一致K-SVD框架的启发，将模式信息纳入字典学习方法中，我们开发了一种解决多字典学习优化问题的解决方案。最后，当新样本到达时，我们在学习的字典下重建它们，以便可以确定每个样本的模式和异常数据。对两种不同类型的模拟过程和铝电解过程的实验表明了该方法的强度和可靠性，表明了该方法的工程应用价值。