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Sequential DTC Vector Embedding Using Deep Neural Networks for Industry 4.0

机译：使用深度神经网络的工业4.0顺序DTC矢量嵌入

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DTC (Diagnostic Trouble Code) is the fundamental diagnostic metric through which a system will gauge the function of a module (mechanical component) of a machine. Every machine is made up of several hundreds of modules, in this paper our research tries to understand how a module is related with other modules in a machine through which we tried to understand how effectively a system can understand the relationships between DTC (diagnostic trouble codes) in terms of mechanical components of a machine using Deep Neural Networks especially sequential models such as RNN and non-sequential models such as CNN. This analysis also helps to understand the mechanics of DTC generation on dynamic conditions of a vehicle. To overcome the problems, such as DTC vector sparsity, normalize the geo-graphical conditions, irrational driving etc., we are proposing a novel mechanism called Sequential based DTC Embedding simply called SDVE (Sequential DTC Vector Embedding). SDVE is the novel technique which comprises of different techniques called component relation-based vector, which is vectorized among the various DTC (Diagnostic Trouble Code) through sequential and frequency categorization technique. We see multiple applications of this DTC relational vector such as, vehicle diagnostic system accuracy improvement, identification of relations between parts, understanding the mechanics between parts etc., To prove the proposed algorithm empirically we have built a sample deep learning model which embedded the SVDE as a layer in a part failure prediction deep neural network architecture. Empirically results of the deeply learned model using Gated recurrent unit network for module diagnosis system shown that the SDVE (separate latent space of DTC built using Convolutions) use as an embedded layer, boosted the accuracy in part failure prediction. Empirically proven results are shown in below, which supports our research in terms of novelty and accuracy.
机译：DTC（诊断故障代码）是基本的诊断指标，系统将通过该指标评估机器模块（机械组件）的功能。每台机器都由数百个模块组成，在本文中，我们的研究试图了解模块与机器中其他模块的关系，从而试图了解系统如何有效地理解DTC（诊断故障代码）之间的关系。 ）（使用深度神经网络的机器的机械组件），尤其是顺序模型（例如RNN）和非顺序模型（例如CNN）。此分析还有助于了解在车辆动态条件下生成DTC的机制。为了克服诸如DTC向量稀疏性，使地理条件标准化，不合理驾驶等问题，我们提出了一种新的机制，称为基于序列的DTC嵌入，简称为SDVE（序列DTC矢量嵌入）。 SDVE是一种新颖的技术，它包含称为基于组件关系的向量的不同技术，该技术通过顺序和频率分类技术在各种DTC（诊断故障代码）之间进行向量化。我们看到了这种DTC关系向量的多种应用，例如车辆诊断系统的精度提高，零件之间的关系识别，零件之间的力学理解等。为了从经验上证明该算法，我们建立了一个样本深度学习模型，该模型嵌入了SVDE。作为零件故障预测深度神经网络体系结构中的一层。使用门控递归单元网络进行模块诊断系统的深度学习模型的经验结果表明，SDVE（使用卷积构建的DTC的单独潜在空间）用作嵌入式层，提高了零件故障预测的准确性。经验证明的结果如下所示，这在新颖性和准确性方面为我们的研究提供了支持。

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