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

arXiv: https://arxiv.org/abs/2601.01616 · HTML: https://mlsanity.com/paper/2601.01616/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 41 references that are in the map.
- First posted on arXiv 4 Jan 2026.

## Builds on (most cited first, 7 of 7)

- [Neural NILM: Deep Neural Networks Applied to Energy Disaggregation](https://mlsanity.com/paper/1507.06594/) (2015)
- [Sequence-to-point learning with neural networks for nonintrusive load monitoring](https://mlsanity.com/paper/1612.09106/) (2016)
- [COLD: Concurrent Loads Disaggregator for Non-Intrusive Load Monitoring](https://mlsanity.com/paper/2106.02352/) (2021)
- [MATNilm: Multi-appliance-task Non-intrusive Load Monitoring with Limited Labeled Data](https://mlsanity.com/paper/2307.14778/) (2023)
- [A Novel Event-based Non-intrusive Load Monitoring Algorithm](https://mlsanity.com/paper/2009.02656/) (2020)
- [Scalable Hybrid Classification-Regression Solution for High-Frequency Nonintrusive Load Monitoring](https://mlsanity.com/paper/2208.10638/) (2022)
- [Non-Intrusive Load Monitoring (NILM) using Deep Neural Networks: A Review](https://mlsanity.com/paper/2306.05017/) (2023)

## Abstract (arXiv)

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textile industry in Bangladesh is one of the most energy-intensive sectors, yet its monitoring practices remain largely outdated, resulting in inefficient power usage and high operational costs.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.