In this study, a new approach for time series based condition monitoring and fault diagnosis based on bidirectional recurrent neural networks is presented. The application of bidirectional recurrent neural networks essentially provide a viewpoint change on the fault diagnosis task, which allows to handle fault relations over longer time horizons helping in avoiding critical process breakdowns and increasing the overall productivity of the system. To further enhance the capability, we propose a novel procedure of data preprocessing and restructuring which enforces the generalization and a more efficient data utilization and consequently yields more efficient network training, especially for sequential fault classification task. The proposed Bidirectional Long Short Term Memory network outperforms standard recurrent architectures including vanilla recurrent neural networks, Long Short Term Memories and Gated Recurrent Units. We apply the proposed approach to the Tennessee Eastman benchmark process to test the effectiveness of the mentioned deep architectures and provide a detailed comparative analysis. The experimental results for binary as well as multi-class classification show the superior average fault detection capability of the bidirectional Long Short Term Memory Networks compared to the other architectures and to results from other state-of-the-art architectures found in the literature.

在这项研究中，提出了一种基于双向递归神经网络的基于时间序列的状态监测和故障诊断的新方法。双向循环神经网络的应用本质上提供了故障诊断任务的观点变化，它允许在更长的时间范围内处理故障关系，有助于避免关键过程故障并提高系统的整体生产力。为了进一步增强能力，我们提出了一种新的数据预处理和重构程序，它强制泛化和更有效的数据利用，从而产生更有效的网络训练，特别是对于顺序故障分类任务。提出的双向长短期记忆网络优于标准循环架构，包括普通循环神经网络、长短期记忆和门控循环单元。我们将所提出的方法应用于田纳西州伊士曼基准测试过程，以测试上述深层架构的有效性并提供详细的比较分析。二进制和多类分类的实验结果表明，与其他架构相比，双向长短期记忆网络具有卓越的平均故障检测能力，并且与文献中发现的其他最先进架构的结果相比。我们将所提出的方法应用于田纳西州伊士曼基准测试过程，以测试上述深层架构的有效性并提供详细的比较分析。二进制和多类分类的实验结果表明，与其他架构相比，双向长短期记忆网络具有卓越的平均故障检测能力，并且与文献中发现的其他最先进架构的结果相比。我们将所提出的方法应用于田纳西州伊士曼基准测试过程，以测试上述深层架构的有效性并提供详细的比较分析。二进制和多类分类的实验结果表明，与其他架构相比，双向长短期记忆网络具有卓越的平均故障检测能力，并且与文献中发现的其他最先进架构的结果相比。