Real Time NILM Based Power Monitoring of Identical Induction Motors Representing Cutting Machines in Textile Industry
This work addresses inefficient power usage and high costs in the energy-intensive textile industry of Bangladesh, though it is incremental as it applies an existing method to a new industrial context with identical loads.
The authors tackled real-time power monitoring for identical induction motors in the textile industry using a Non-Intrusive Load Monitoring (NILM) framework, achieving reasonably accurate aggregate energy estimation but facing difficulties in per-appliance disaggregation, particularly with multiple identical machines operating simultaneously.
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textile industry in Bangladesh is one of the most energy-intensive sectors, yet its monitoring practices remain largely outdated, resulting in inefficient power usage and high operational costs.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.