Picture Source – fingent.com
IoT, or the Internet of Things, has been in existence for a long time. It has become one of the most important technologies of the 21st century. It refers to the billions of physical devices around the world that are now connected to the internet, all collecting and sharing data.
IoT has fundamentally changed the way of doing business. We are now in the Fourth Industrial Revolution, aka Industry 4.0, which takes the automation and computerization we saw in the Third Industrial Revolution into the future. Industry 4.0 is powered by the Industrial Internet of Things (IIoT) and cyber-physical systems – smart, autonomous systems that use computer-based algorithms to monitor and control physical things like machinery, robots, and vehicles. Sliding into the age of data analytics, connectivity and automation, businesses that understand the use case and potential of IoT are the businesses that drive innovation.
IoT Streaming Analytics
With IoT data collection, devices and technologies connected over IoT can now be used to monitor and measure data in real time. The data is transmitted, stored and retrieved all in real time. This data can also be used for IoT analytics which assesses the data collected from IoT devices. This variant of analytics is particularly well-suited to analyze IoT data because the devices typically generate a huge amount of information in a relatively short time. Statistics show that IoT devices produce 2.5 quintillion bytes of data on a daily basis.
Industrial Applications of IoT Streaming Analytics
IoT data and analytics is leveraged across all industries in a multitude of ways. Some examples include:
1. Predictive Maintenance
Continuous monitoring of machine and sensor data helps equipment manufacturers and service providers predict and address maintenance issues before they occur.
2. Product Monitoring
Data collected on how customers are interacting with physical features enables product management and engineering teams to analyze user behavior and experience for incorporation into future product development.
3. Smart Devices and Wearables
Product developers are embedding sensors into consumer electronics to uncover user-specific insights and value-added services across a wide range of needs—everything from smart appliances to fitness trackers.
Defect Detection with Deep Learning in Smart Factory
Smart factories use IoT techniques to manage automated manufacturing and automated defect detection equipment for improvement in product quality.
Defect detection equipment is mainly used in product manufacturing, packaging, and functional testing processes. The process involves the establishment of checkpoints, phased detection of semi-finished products to identify defective products, and identifying defective products. Various deep learning algorithms such as classification and computer vision can be employed for intelligent defect detection.
Many companies experience quality-related costs of 15%-20% of sales revenue. In some cases, defects can be found in up to 40% of the total number of operations being performed at the factory. The European Commission estimates that in some industries, 50% of production can be scrapped due to defects. The defect rate can reach a startling 90% in complex manufacturing lines.
This calls for a movement from manual to automated optical inspection. Powered by machine vision, automated optical inspection (AOI) emerged as a replacement for the error-prone manual inspection. The new types of AOI systems are equipped with multi-cameras ranging from simple XGA units to high-resolution, multiple-megapixel video sensors. Depending on the camera type, an AOI system can either provide monochrome or color images of the inspected items, and the captured images can span a wide range, from mere thousands of data points to millions of data points. The benefits of automated optical inspection are multifold:
A. AOI supports early error detection in manufacturing processes and ensures high quality of the item before it progresses to the next manufacturing step.
B. AOI gathers historical and current production statistics that can be used to improve manufacturing lines. As a result, it will help reduce material waste and cut costs from added manufacturing labor time and expenses.
IoT Analytics at Kavi Global
Kavi Global has successfully leveraged IoT analytics to solve business problems for clients. Starting from designing the architecture to deploying the defined architecture across the organization, we have expertise in implementing solutions such as remote monitoring and diagnostics, reliability analytics, asset downtime reduction and more using state-of-the-art cloud computing technologies such as Azure IoT Hub, Azure Time Series Insights, Azure IoT Central, AWS IoT, Apache Kafka, Cosmos DB, etc.
Kavi Global has leveraged these tools to develop a flexible, scalable and comprehensive Distributed Sensor Solutions Platform that can be deployed either on the cloud or on premise, thus reducing the amount of time it takes a service personnel to respond to an event by 20%.
Client Success Story－
The Sensor Solutions group within the Business Development and Corp Strategy team was looking to enter the IIoT market with new products and solutions. The goal was to provide a comprehensive solution to their customers that included devices, middleware, software platform and analytics. The client’s team had deep expertise in building out the sensor devices, and needed a partner with services, software and solutions capabilities.
A flexible, scalable and comprehensive Distributed Sensor Solutions Platform that can be deployed either on the cloud or on premise. The platform provides streaming data ingestion and analytics on critical assets to ensure high standards of safety, availability and cost optimization.
Accelerated the development and implementation of the software – both middleware and platform components. Kavi Global complemented the client’s team by providing the software strategy, architecture, design and implementation. This in turn created capacity to focus on hardware, business use case and commercialization.
< About the Author >
Trishla Mishra is a Consultant, Business Analytics at Kavi Global. She has a Masters degree in Business Analytics from Oklahoma State University. She has been working in the field of analytics for 5+ years, primarily in data engineering, predictive analytics and deep learning.