Robust process monitoring and reliable fault isolation in industrial processes usually encounter different challenges, including process nonlinearity and noise interference. In this brief, a novel method denoising autoencoder and elastic net (DAE–EN) is proposed to solve the aforementioned issues by effectively integrating DAE and EN. The DAE is first trained to robustly capture the nonlinear structure of the industrial data. Then, the encoder network is updated into a sparse model using EN, so that the key variables associated with each neuron can be selected. After that two statistics are developed based on the extracted systematic structure and the retained residual information. In addition, another statistic is also constructed by combining the aforementioned two statistics to provide an overall measurement for the process sample. In this way, a robust monitoring model can be constructed to monitor the abnormal status in industrial processes. After the fault is detected, the faulty neurons are identified by the sparse exponential discriminant analysis, so that the associated faulty variables along each faulty neuron can thus be isolated. Two real industrial processes are used to validate the performance of the proposed method. Experimental results show that the proposed method can effectively detect the abnormal samples in industrial processes and accurately isolate the faulty variables from the normal ones.

中文翻译：

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利用去噪自动编码器和弹性网对非线性工业过程进行鲁棒监测和故障隔离

工业过程中强大的过程监控和可靠的故障隔离通常会遇到不同的挑战，包括过程非线性和噪声干扰。在本文中，提出了一种通过有效集成DAE和EN来解决自动编码器和弹性网降噪的新方法（DAE-EN）。首先对DAE进行培训，以可靠地捕获工业数据的非线性结构。然后，使用EN将编码器网络更新为稀疏模型，以便可以选择与每个神经元相关的关键变量。之后，根据提取的系统结构和保留的残差信息开发两个统计量。此外，还可以通过将上述两个统计数据组合起来，构成另一个统计数据，以提供对过程样本的整体测量。通过这种方式，可以构建强大的监视模型来监视工业过程中的异常状态。在检测到故障之后，通过稀疏指数判别分析来识别故障神经元，从而可以隔离沿每个故障神经元的相关故障变量。使用两个实际的工业过程来验证所提出方法的性能。实验结果表明，该方法可以有效地检测工业过程中的异常样本，并准确地将故障变量与正常变量区分开。这样就可以隔离沿着每个故障神经元的相关故障变量。使用两个实际的工业过程来验证所提出方法的性能。实验结果表明，该方法可以有效地检测工业过程中的异常样本，并准确地将故障变量与正常变量区分开。这样就可以隔离沿着每个故障神经元的相关故障变量。使用两个实际的工业过程来验证所提出方法的性能。实验结果表明，该方法可以有效地检测工业过程中的异常样本，并准确地将故障变量与正常变量区分开。