Fogwing Industrial IoT platform provides IoT foundation for your Smart Industry journey. Fogwing is engineered with No-Code development feature to deploy IoT solutions for various industrial use cases rapidly. This Fogwing IIoT Platform Release Notes November 2022 covers details on the new features and functionalities released available from November 2022.
AIoT in Smart Manufacturing
What is AIoT?
AIoT or Artificial Intelligence of Things is a combination of Artificial Intelligence (AI) and Internet of Things (IoT) to deliver superior technology value to business. IoT connects any devices or things via internet to monitor real time data while AI analyses such large volumes of IoT data through various algorithms and deliver actionable intelligence without human interventions. Hence, AIoT is a comprehensive technology solution which helps to connect devices to the network, collects the data from the devices and performs AI service and finally sends only the insightful data to the consumers for actionable metrics.
In recent years, IoT and AI have become the most advanced technologies in the industries aiding digital automation. Now convergence of AI and IoT called “AIoT” elevates the industry to a new level of automation converting the traditional manufacturing to smart manufacturing.
What is AI?
Artificial Intelligence refers to replication of human cognitive abilities into machines which is specially programmed to think like human at any given circumstance. With AI capabilities, machines are given the supremacy to think, act and execute without depending on human intelligence. AI improves the thought processing and conditional reasoning of a machine which helps the system to predict any unplanned operational downtime, unforeseen breakdown of the system and predictive maintenance from the data provided by the sensors or devices.
By collecting more and more data over time, machines learn from these data, adjust to the new inputs and execute human-like tasks. Hence, industrial applications become highly efficient, leading to reduction in costs with less waste and reduced downtimes.
What is AI Analytics?
As the name indicates, AI Analytics is a combination of Artificial Intelligence and Data Analytics. AI Analytics uses machine learning techniques, performs the job of a Data Analyst by collecting the data, processing it, analyzing it, giving better understanding and drawing insights to the performance of the business much faster with high accuracy compared to human efforts.
There are 4 types of Analytics in practice. They are:
- Descriptive Analytics: Which tells us “What has happened to the data”.
- Diagnostic Analytics: Which tells us “Why there is change in data”.
- Predictive Analytics: Which tells us “What will happen next”.
- Prescriptive Analytics: Which tells us “What actions should be taken to get the desired output”.
Few notable advantages of AI Analytics are:
- Since AI Analytics is performed by a machine, various combinations of data can be handled well and provides meaningful solutions to different scenarios and crises.
- It takes seconds to perform the given operations.
- AI Analytics gives insights to scientists, data engineers, manufacturers and business analysts simultaneously.
- This process is well known for its speed, accuracy, efficiency and granularity.
IoT connected machineries produce vast amount of data and passes it to the edge servers thereby stemming to heavy traffic of data in the cloud. With AIoT systems, each data collected undergoes AI analysis, which processes the data turning them to useful information giving the users insights on the performance and efficiency of the devices connected. In simple words, IoT collects the data and AI analyses it locally.
How AI helps to improve IoT system?
Generally, AI process can be done in 2 different locations like:
- Implementing AI at the center of IoT system
- Implementing AI at the edge of an IoT network
Implementing AI at the center of IoT system:
When AI is located at the center of an IoT system, various tasks like predictive analysis and abnormalities in the machineries which may lead to machine breakdown can be detected. From the data collected from the devices, data analytics can be drawn and through this machine operations can be predicted. This helps the user take certain measures to avoid unforeseen situations and therefore contribute to smooth operations. In this approach, the processed data and insights are accessible from anywhere.
Implementing AI at the edge of an IoT network:
AI at the edge (close to device nodes) of IoT network allows the data to be processed at the source point rather than at the cloud point. Basic idea of implementing AI at the edge level of IoT system is that rather than spending bandwidth and energy to collect all the device data into the cloud, AI analysis is performed on all the collected data, analyzed and sends only the data required (for actionable business decision making) to the cloud. This method can help reduce the bandwidth, reduce the cost to deploy and maintain huge data at the cloud, latency of transmitting huge IoT data and achieve real-time response with AI analyzing this huge data. This method also enhances privacy and security. It empowers the device run smoothly even when the internet access is intermittent or weak (as it requires less bandwidth).
Is Cloud Computing easier with AIoT?
Cloud computing geared up with AIoT has given rise to a new era of digital automation in industrial applications. Let us get in-depth knowledge on what happens without AIoT and with AIoT.
Cloud computing without AIoT:
Cloud computing provides three key components to connected devices – Connectivity, Storage and Compute which is the basis of IoT devices too. Cloud computing enables multiple devices to connect seamlessly and transfer data (machine-to-machine or telemetry data) with each other. Device data is stored centrally in the cloud. Compute service deployed in the cloud process these large data sets to derive insights and track the system performance metrics. Then the outcome is presented in insightful visualization, graphs or charts for the engineers to take appropriate decisions.
Now let us see how cloud computing becomes easier with AIoT.
Cloud computing with AIoT:
The main component of AI added to the above process is “Act”. AI goes beyond visualizations by “acting” on the patterns and correlations from the data collected. It takes appropriate actions based on the data collected. Instead of just showing the data to the user in any IoT platform, AI takes decisions automatically without human interventions. Hence AI is called the brain of IoT system – Data driven decision making.
The current IoT systems are designed to respond to an occurrence whereas AIoT systems can proactively detect faults and failures at the source level, hence IoT Data Analytics is “reactive” and AIoT is “proactive”. The convergence of AI and IoT systems provide predictive maintenance thereby helping the industries save money by optimizing maintenance strategies, improving the quality of assets and manufacturing operations.
AIoT – Future of Industry 4.0?
When computing power was introduced to the manufacturing industries, it gave a new wing to all the machines in industries leverage to a new level of automation. Industry 4.0 is the current trend of intelligent automation and data exchange in manufacturing industries. The amalgamation of Artificial Intelligence, IoT and cloud computing with Industry 4.0 principles, create a new aeon for building smart factory floor with smart machines.
These smart machines get smarter when they are digitally connected to each other and share tremendous volume of data that has all the information regarding the performance, maintenance and downtime issues. AI processing is performed on the collected data to identify the patterns and insights and take appropriate decision without human interferences because faster decision-making leads to better control. Hence, the factories become more efficient, with increased productivity with less waste improving the industry business.
Industry 4.0 gives an opportunity for the manufacturers to optimize their operations quickly and efficiently comprehending to the need of the hour in the plant.
Applications of AIoT
In recent years, many industrial applications have deployed AIoT. Few of them listed blow as reference:
- Optimize Logistics and Supply Chains
- Robotics Automation
- Autonomous equipment and vehicles
- Manufacturing Industries
Benefits of AIoT
AIoT can offer many potential benefits in manufacturing industries. Few of which are listed below.
- Building smart factories.
- Helps in achieving efficiency, optimization and safe manufacturing conditions.
- Getting insights about the system performance.
- Predictive analysis and maintenance.
- Increase the productivity of the machines thereby improving the industry’s business.
- Smooth operations even with limited network connections.
- Saves operational costs with less waste and reduced downtimes.
- Controls Engineering operations with limited workforce.
AIoT is the unified technology that brings the machineries or devices work together, makes them user-friendly by connecting them to the cloud and giving them intelligence capabilities. There has been considerable number of growth opportunities in building smart factories. This helps achieve the goal for greater efficiency in complex operations.
Industry 4.0 along with AIoT gives a new way of approach to use digital technologies to revamp the practices of the manufacturing
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Fogwing IIoT platform and Fogwing Analytic studio are considered to provide the best foundation to implement IoT solution for Industrial use cases. Fogwing IIoT provides everything required to build connected IoT infrastructure and provides the best visibility of things. We bring you another update about our latest releases of Fogwing
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