The textile industry in Bangladesh is one of the most energy-intensive sectors, yet its monitoring practices remain largely outdated, resulting in inefficient power usage and high operational costs. To address this, we propose a real-time Non-Intrusive Load Monitoring (NILM)-based framework tailored for industrial applications, with a focus on identical motor-driven loads representing textile cutting machines. A hardware setup comprising voltage and current sensors, Arduino Mega and ESP8266 was developed to capture aggregate and individual load data, which was stored and processed on cloud platforms. A new dataset was created from three identical induction motors and auxiliary loads, totaling over 180,000 samples, to evaluate the state-of-the-art MATNILM model under challenging industrial conditions. Results indicate that while aggregate energy estimation was reasonably accurate, per-appliance disaggregation faced difficulties, particularly when multiple identical machines operated simultaneously. Despite these challenges, the integrated system demonstrated practical real-time monitoring with remote accessibility through the Blynk application. This work highlights both the potential and limitations of NILM in industrial contexts, offering insights into future improvements such as higher-frequency data collection, larger-scale datasets and advanced deep learning approaches for handling identical loads.

翻译：孟加拉国的纺织工业是能源最为密集的行业之一，但其监控实践在很大程度上仍较为落后，导致电力使用效率低下和运营成本高昂。为解决这一问题，我们提出了一种专为工业应用定制的、基于实时非侵入式负载监测（NILM）的框架，重点关注代表纺织切割机的同型电机驱动负载。开发了一套包含电压和电流传感器、Arduino Mega和ESP8266的硬件装置，用于采集总负载和单个负载数据，这些数据在云平台上进行存储和处理。我们利用三台同型感应电机及辅助负载创建了一个新数据集，总计超过18万个样本，以评估先进的MATNILM模型在具有挑战性的工业条件下的性能。结果表明，虽然总能耗估计相当准确，但针对单个电器的负荷分解面临困难，尤其是在多台同型机器同时运行时。尽管存在这些挑战，该集成系统通过Blynk应用程序实现了具有远程访问功能的实用实时监控。这项工作既凸显了NILM在工业环境中的潜力，也揭示了其局限性，并为未来的改进方向提供了见解，例如更高频率的数据采集、更大规模的数据集以及用于处理同型负载的先进深度学习方法。