Department of Engineering, University of Messina, 98166 Messina, Italy.
Sensors (Basel). 2022 Apr 8;22(8):2858. doi: 10.3390/s22082858.
The pervasive use of sensors and actuators in the Industry 4.0 paradigm has changed the way we interact with industrial systems. In such a context, modern frameworks are not only limited to the system telemetry but also include the detection of potentially harmful conditions. However, when the number of signals generated by a system is large, it becomes challenging to properly correlate the information for an effective diagnosis. The combination of Artificial Intelligence and sensor data fusion techniques is a valid solution to address this problem, implementing models capable of extracting information from a set of heterogeneous sources. On the other hand, the constrained resources of Edge devices, where these algorithms are usually executed, pose strict limitations in terms of memory occupation and models complexity. To overcome this problem, in this paper we propose an Echo State Network architecture which exploits sensor data fusion to detect the faults on a scale replica industrial plant. Thanks to its sparse weights structure, Echo State Networks are Recurrent Neural Networks models, which exhibit a low complexity and memory footprint, which makes them suitable to be deployed on an Edge device. Through the analysis of vibration and current signals, the proposed model is able to correctly detect the majority of the faults occurring in the industrial plant. Experimental results demonstrate the feasibility of the proposed approach and present a comparison with other approaches, where we show that our methodology is the best trade-off in terms of precision, recall, F1-score and inference time.
在工业 4.0 范式中，传感器和执行器的广泛应用改变了我们与工业系统交互的方式。在这种情况下，现代框架不仅限于系统遥测，还包括潜在有害条件的检测。然而，当系统生成的信号数量很大时，就很难正确关联信息以进行有效的诊断。人工智能和传感器数据融合技术的结合是解决这个问题的有效方法，实现了从一组异构源中提取信息的模型。另一方面，这些算法通常在边缘设备上执行，边缘设备的资源受到限制，在内存占用和模型复杂度方面存在严格的限制。为了解决这个问题，本文提出了一种利用传感器数据融合来检测工业规模复制工厂故障的回声状态网络架构。由于其稀疏权重结构，回声状态网络是递归神经网络模型，具有低复杂度和内存占用，这使得它们适合部署在边缘设备上。通过对振动和电流信号的分析，所提出的模型能够正确检测到工业工厂中发生的大多数故障。实验结果证明了所提出方法的可行性，并与其他方法进行了比较，结果表明我们的方法在精度、召回率、F1 分数和推理时间方面是最佳的权衡。
