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A novel data-temporal attention network based strategy for fault diagnosis of chiller sensors

机译：基于数据的新型数据 - 时间关注网络的冷却器传感器的故障诊断策略

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摘要

In the air-cooled chiller system, sensor fault diagnosis has great significance for ensuring normal operation. However, according to the operating mechanism of the chiller system, sensors' readings exhibit dynamical data-temporal dependencies and are easily affected by external factors and control parameters. To further capture the data characteristics existed in the sensors time series, in this paper, we propose a novel data-temporal attention network (DAN) for the chiller sensor fault diagnosis. Based on the conventional encoder-decoder network (EDN), the proposed DAN model is firstly built by adding three novel parts: the first one is a data attention mechanism embedded in the encoder, which is used to capture the dynamic data correlation between different sensors; the second part is a temporal attention mechanism, which is employed in the decoder to model the dynamic time-dependencies among the sensors time series; considering the influence of external factors and control parameters, the third part is a fusion module to incorporate these influential factors from different domains. Thereafter, we design a specific chiller sensor fault diagnosis strategy using the proposed DAN model. The sensor fault diagnosis strategy uses only normal sequences for training and learns to reconstruct normal time series behaviors, and then determines the fault threshold of each chiller sensor, and finally identifies the specific sensor fault by comparing the absolute reconstruction error vector with the fault threshold vector. In the end, the experiments which adopt data sets from a real air-cooled chiller platform are conducted, and detailed comparisons are made. Various magnitudes of fixed biases are introduced into eleven sensors for validation. Experimental results reveal that the sensor fault diagnosis strategy with the proposed DAN model achieves the best training and fault diagnosis performance compared with its variants and the traditional EDN model. Especially for the sensor fault diagnosis performance, comparison results demonstrate that the proposed DAN model is more sensitive to the small biases than the other contrast models and has better robustness for impacts on fault sensor in the reconstruction of the fault-free sensors. (C) 2019 Elsevier B.V. All rights reserved.
机译：在风冷的冷却器系统中，传感器故障诊断具有重要意义，可确保正常运行。然而，根据冷却器系统的操作机制，传感器的读数表现出动态数据 - 时间依赖性，并且很容易受到外部因素和控制参数的影响。为了进一步捕获传感器时间序列中存在的数据特性，本文提出了一种新的数据临时注意力网络（DAN），用于冷却器传感器故障诊断。基于传统的编码器 - 解码器网络（EDN），通过添加三个新颖部分首先构建所提出的DAN模型：第一个是嵌入在编码器中的数据注意机制，用于捕获不同传感器之间的动态数据相关性;第二部分是一个时间关注机制，它在解码器中采用，以模拟传感器时间序列之间的动态时间依赖性;考虑到外部因素和控制参数的影响，第三部分是融合模块，用于包含来自不同域的这些影响因素。此后，我们使用所提出的DAN模型设计特定的冷却器传感器故障诊断策略。传感器故障诊断策略仅使用正常序列进行培训，并学习重建正常时间序列行为，然后通过比较具有故障阈值向量的绝对重建误差向量来确定每个冷却器传感器的故障阈值，最后识别特定的传感器故障。最后，进行了从真正的风冷冷却器平台采用数据集的实验，并进行了详细的比较。将各种固定偏差大小引入11个传感器中以进行验证。实验结果表明，与其变体和传统的EDN模型相比，具有所提出的DAN模型的传感器故障诊断策略实现了最佳的培训和故障诊断性能。特别是对于传感器故障诊断性能，比较结果表明，所提出的DAN模型对小偏差比另一个对比模型更敏感，并且对故障传感器的重建中对故障传感器的影响更好的鲁棒性。 （c）2019 Elsevier B.v.保留所有权利。

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