How an Midian Manufacturer get started with IoT?

Abstract: 

In an industrial environment, these devices are the source of data that provide useful information in manufacturing processes. The framework contains five basic layers like physical, network, middleware, database, and application layers to provide a service-oriented architecture for the end-users. 

Introduction:

The Industrial Internet of Things (IIoT) mainly focuses on low cost, smart, and interconnected factory equipment. It can define IoT automation as a set of machines, objects, people that has artificial intelligence, advanced analytics, and cloud computing to improve productivity by improving machine health. IIoT is not just a unique technology, but a combination of different techniques such as the Internet of Things (IoT), big data, cyber-physical systems (CPS), machine learning (ML), and simulation, to organize intelligent operations in industrial environments. 

The IIoT produces data that can be used to predict and control the manufacturing system. Information obtained from raw industrial data allows the manufacturer to recognize the unconventional applications, provides a valuable source of strategic opportunities, and helps organize smarter business decisions.

In knowledge-based sensor failure identification and prevention model. The IoT device is enabling real-time data transmission through the industrial network to create manufacturing intelligence. Thus, eliminates redundant production interruptions, increases profits through a monitoring system, predicts the health status of machines, and reduces maintenance costs and decision-making complexity. 

What is Industrial data?

Technological advancements, particularly in ICT, automation, and production are on the rise in the number of devices connected to the Internet. In the presence of these technologies, machines can communicate with each other as well as with end products using IoT applications. The factory devices in workshops continuously generate massive amounts of data. The data generated from these devices would be complex, scalable, and large. The data can be in the form of sets of key values ​​or image/audio/video content which is typically geo-stamped and time-stamped. So, this huge amount of data as Big Data and IoT the environment as a source of data. In the age of modern smart factories, data generated by industrial devices have reached more than the total volume per year and will increase steadily.

Characteristics of Industrial data:

The IIoT saw a great revolution that profoundly changed the industrial face and transformed the traditional manufacturing system into a digital ecosystem. Innovations in information and operational technologies allow an enormous exchange of data between factory peripherals, thereby improving business performance, responsiveness, and flexibility. The IIoT saw as a revolution that completely profound, changed the industrial face, and transformed the traditional manufacturing system into a digital ecosystem. 

Innovation and operating technologies allow enormous data exchange between plant devices. IoT in Healthcare also improves its performance, responsiveness, and flexibility of the company. Industries can also use the data collected to create benefits for companies and improve their competitiveness by predictive analysis. Also, the extraction of useful information from industrial data is a difficult task. 

The available IoT data management systems focus on collecting data quickly to make early and smart decisions, but with a permanent storage capacity for later use. The industrial environment also contains a variety of data resources such as integrated and intelligent, archival and real-time, mobile, and stationary. 

Industrial data lifecycle:

Industrial data is an important resource that will be more crucial for global manufacturing business prospects and helps in managing enormous wealth source. This kind of data management requires processing and storage capacities due to its huge, complex, and unstructured nature. The life cycle of industrial data can be defined using three phases such as physical, middleware, and application. The whole industrial environment has been divided into two subsections like the real and digital world. 

In real environments, raw industrial data with data types, formats, and various dimensions are generated by many physical component smart factories. Some valid data sources such as sensors, web generated data, databases, and third-party applications. This component is also called data discovery. After the implementation of the digitization and aggregation processes, this data becomes a part digital world in binary form, where middleware and application components offer many services to manage it. The middleware component addresses interoperability between various factories, devices, device discovery, scalability, big data management, context awareness, and security characteristics of the IoT jobs. 

IoT projects:

As we all know, the Internet is no longer just limited to our desktops. It's not even limited to our smartphones or laptops anymore. As of today, every smart device is connected to the Internet. Devices like home alarms, fire alarms, home appliances, even our cars, and wristwatches are connected to it. In such cases, Python comes in handy for building apps and gain connectivity for these devices. It allows them to connect to the internet and offers functions like controlling auto-updates remotely. 

Results & Analysis:

The Intelligence distribution field allows connected devices to publish their data in a standardized format. Smart brokers make this data transparently accessible to end-users. This approach is useful for identifying the location of data sources without custom programming. Because of IoT systems, industries rely on the network of interconnected physical objects to connect devices, places, and people. 

Meanwhile, the proposed framework supports the wireless network structure such as WSN for providing a scalable and high-performance network platform for strong bonds with employees and end-users. Also, ERP-level manufacturers can take advantage of this facility via mobile.  Access production line data from the floor manager's smartphones and PCs. This advanced level of connectivity keeps the production manager and top floor management up to date and ensures high-quality products and prompt delivery. 

They know when production needs to be slow or fast to meet every hour, daily and weekly goals, and the efficiency with which workers complete their respective production phases. With such visibility into production operations, manufacturers have a better understanding to meet challenges and achieve greater efficiency. 

Conclusion:

The Industrial IoT for beginners is a complex topic that includes aspects of information technology, operation technology, statistics, and engineering. The industrial data management system with five basic layers like physical, network, middleware, database, and application layers.  The different modules of the middleware layer support the retrieving and collection of huge industrial data generated by thousands of factory devices on the shop floor and extract useful information by applying a context-aware approach. Therefore, this study improved the industrial production scale and drive the development of smart manufacturing for a more secure, sustainable, and efficient business.