Multihop Wireless Network Based Smart Textile Monitoring System

This project deal with the core idea of industry 4.0. Industry 4.0 takes the emphasis on digital technology from recent decades to a whole new level with the help of interconnectivity through the Internet of Things (IoT), access to real-time data, and the introduction of cyber-physical systems. Indu

Project Title

Multihop Wireless Network Based Smart Textile Monitoring System

Project Area of Specialization

Electrical/Electronic Engineering

Project Summary

This project deal with the core idea of industry 4.0. Industry 4.0 takes the emphasis on digital technology from recent decades to a whole new level with the help of interconnectivity through the Internet of Things (IoT), access to real-time data, and the introduction of cyber-physical systems. Industry 4.0 offers a more comprehensive, interlinked, and holistic approach to manufacturing. For making the industry smart enough to make its own decision without human intervention by machine to machine and product to machine communication.

In this project the data is acquired from different sensors. This data is sent to the cloud where the data is stored and is available for analysis and control. An android application is developed that will act as human interface and use the data from cloud. Textile industry is targeted in this project. Textile industries are affected by environmental factors especially by temperature and relative humidity. These factors effect each department in different way according to its process and material. Initial step of textile industry so called spinning is also affected and environmental factors are controlled in each stage of spinning, otherwise quality is deteriorated and results in unbalanced production. These two parameters effect spinning from bale storage to yarn winding section. Each process from bale storage to winding section is affected by temperature and humidity. Lower level of humidity causes dryness in the fiber and tends to create static electricity due to friction with various machine parts which also causes the fibers to stick to machines thus slowing the processing. Weight is also an important factor. Weight of the cones is considered at the end of spinning processes to check the amount of yarn wrapped on the cone before packaging. These parameters are taken under consideration in this project.

Project Objectives

• To take the industrial manufacturing system to customized and flexible mass production technologies.
• To make the machines independent entity that can collect data, analyze it and advise upon it.
• To introduce self-optimization and self-customization into industry.
• To introduce predictive maintenance in machines.
• To create a social network of machine in which machines can communicate with each other.

Project Implementation Method

In the implementation of this project two types of Particle are used as the controller.

The Particle Argon is a powerful Wi-Fi development kit that can use on Wi-Fi networks.

The Xenon is a mesh-enabled development kit that act as an endpoint within a Particle Mesh network.

1.Data Acquisition: Data is acquired from 3 Industrial sensors humidity, temperature and weight sensor connected to Xenon and is sent to cloud through Argon. The data can be visualized through Things Speak.

Mesh Networking: Particle Mesh is a wireless mesh network technology built on the Thread networking. The nodes of mesh network can communicate with each other and with the super node. Super node is capable to take the decisions in emergency and communicates to the cloud at which the data of sensors is stored.

Offline Communication: Sending the messages to other hops or data of the sensors to trigger the action on another device is done by a simple mesh publish and subscribe API to form a local network. The devices can subscribe to receive these messages even when the connection with the internet is lost.

Cloud Computation: The Particle Cloud is centerpiece of the Particle platform, handling most complex pieces of creating an IoT product.

Secure

• Encryption: Messages between Particle devices and the   Cloud are encrypted, including firmware files.
• Sender verification: Every OTA update attempt is first   verified to ensure the sender is an approveddevice manager.

Customized Production: Customers will be able to create accounts on the Particle platform that are registered to Product.

• Simple authentication: in which customers can create account that is registered to product and will hit the Particle API directly.
• Two-legged authentication: Particle account servers using its own authentication system and web servers  create an account with Particle for each customer that is  paired to that customer. Servers request a scoped  access token for each customer to interact with their device.
• Login with Particle: Customers will create a Particle  account and a separate account on website and link the two together.

2. Machine Learning: Machine learning is applied to the data set acquired from sensors for self learning of machines.Classification algorithm of supervised learning K Nearest Neighbor (KNN) is being used that stores all the available cases and classifies the new data or case based on a similarity measure..

3.Predictive maintenance: It is done by calculating the age of the sensor.On and off time duration of sensor is calculated and depending on that time up to which sensor has been used is calculated and maintenance of sensor is arranged accordingly before complete damage of sensor.

4.User Interface: Android application will be developed to check the status and to control the functionality of machines remotely.

Benefits of the Project

• Labor cost will be decreased.
• Machines communicating with each other can identify the problem in humidity or temperature and can resolve by suitable solution on their own without generating alarms or human intervention.
• Direct communication between machine  in a mesh without intervention of cloud when internet connectivity is not available.
• Due to customize production the weight or any other parameter of the cone can be changed online.
• The data of the sensors and functionality of the machines can be analyzed from particle cloud depending upon the authorization.

Technical Details of Final Deliverable

1.Hardware

 Development of hardware prototypes of two types of nodes: Wireless sensing and Wireless control.

• First node containing Xenon Particle controller with humidity and temperature sensors.
• Second node containing another Xenon Particle controller with weight sensor.
• Super node with Argon Particle controller maintaining the mesh of sub nodes and connectivity with cloud.

2.Software

• Programming of Particle for data acquisition from sensors, cloud computation, Predictive maintenance and offline communication using Web IDE and Workbench Software of Particle.
• Programming on Pycharm software using python language for Machine Learning. 
• Android application using Android Studio Software.

Final Deliverable of the Project

Hardware System

Core Industry

Manufacturing

Other Industries

Others

Core Technology

Internet of Things (IoT)

Other Technologies

Big Data

Sustainable Development Goals

Industry, Innovation and Infrastructure

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com