Mennilli Riccardo, Mazza Luigi, Mura Andrea
Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, Italy.
Sensors (Basel). 2025 Jan 17;25(2):537. doi: 10.3390/s25020537.
This study investigates the potential of deploying a neural network model on an advanced programmable logic controller (PLC), specifically the Finder Opta™, for real-time inference within the predictive maintenance framework. In the context of Industry 4.0, edge computing aims to process data directly on local devices rather than relying on a cloud infrastructure. This approach minimizes latency, enhances data security, and reduces the bandwidth required for data transmission, making it ideal for industrial applications that demand immediate response times. Despite the limited memory and processing power inherent to many edge devices, this proof-of-concept demonstrates the suitability of the Finder Opta™ for such applications. Using acoustic data, a convolutional neural network (CNN) is deployed to infer the rotational speed of a mechanical test bench. The findings underscore the potential of the Finder Opta™ to support scalable and efficient predictive maintenance solutions, laying the groundwork for future research in real-time anomaly detection. By enabling machine learning capabilities on compact, resource-constrained hardware, this approach promises a cost-effective, adaptable solution for diverse industrial environments.
本研究探讨了在先进的可编程逻辑控制器（PLC），特别是Finder Opta™上部署神经网络模型，以在预测性维护框架内进行实时推理的潜力。在工业4.0的背景下，边缘计算旨在直接在本地设备上处理数据，而不是依赖云基础设施。这种方法可将延迟降至最低，增强数据安全性，并减少数据传输所需的带宽，使其非常适合需要即时响应时间的工业应用。尽管许多边缘设备固有的内存和处理能力有限，但这个概念验证证明了Finder Opta™适用于此类应用。利用声学数据，部署了卷积神经网络（CNN）来推断机械测试台的转速。研究结果强调了Finder Opta™支持可扩展且高效的预测性维护解决方案的潜力，为实时异常检测的未来研究奠定了基础。通过在紧凑的、资源受限的硬件上实现机器学习功能，这种方法有望为各种工业环境提供经济高效、适应性强的解决方案。
