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

TL;DR

This paper presents a real-time NILM framework for monitoring identical induction motors in textile industry, demonstrating practical remote power monitoring but highlighting challenges in disaggregating similar loads.

Contribution

It introduces a novel industrial NILM system with a custom dataset and evaluates deep learning models for load disaggregation in challenging conditions.

Findings

Aggregate energy estimation was reasonably accurate.

Disaggregation of identical loads remains challenging.

System enabled practical real-time remote monitoring.

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industry in Bangladesh is one of the most energy-intensive sectors, yet its monitoring practices remain largely outdated, resulting in inefficient power usage and high operational costs.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