Real Time NILM Based Power Monitoring of Identical Induction Motors Representing Cutting Machines in Textile Industry

• URL: http://arxiv.org/abs/2601.01616v1
• Date: Sun, 04 Jan 2026 17:51:19 GMT
• Title: Real Time NILM Based Power Monitoring of Identical Induction Motors Representing Cutting Machines in Textile Industry
• Abstract summary: The textile industry in Bangladesh is one of the most energy-intensive sectors.<n>Its monitoring practices remain largely outdated, resulting in inefficient power usage and high operational costs.<n>We propose a real-time Non-Intrusive Load Monitoring framework tailored for industrial applications.
• Score: 1.1087735229999816
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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textile industry in Bangladesh is one of the most energy-intensive sectors, yet its monitoring practices remain largely outdated, resulting in inefficient power usage and high operational costs.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.

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