Edge analytics is a sophisticated data analysis technique that allows users to access real-time processing and extraction of unstructured data collected and stored on the network’s edge devices. Edge analytics enables the automated analytical processing of produced data in real-time.

The substantial rise in edge analytics applications is due to the widespread use of the internet of things (IoT), widely acknowledged by industries as the most important tech trend due to the broad range of IoT services and domain capabilities.

How does edge analytics work?

When used as a data-driven approach to extracting tangible and measurable metrics from the IoT, edge analytics enables businesses to obtain data faster by deploying advanced analytics and machine learning at the point of data collection via connected devices and real-time intelligence.

In terms of the industrial IoT industry, it is expected that industries using edge computing will grow further owing to the edge analytics advantages of dispersed aggregation and offering shop-floor data gathering across all industrial sectors.

The idea of edge analytics allows for developing an optimum model for managing data transmission from the edge and ‘smart’ data storage in data centers. Advanced analytics techniques, independent of their application area, operate behind the scenes to anticipate system component failure, keep devices operational, and ensure a continuous workflow.

Overall, edge analytics skills are actively used to develop smart machines that perform self-responsive activities and anticipate consequences.

Edge AI video-based analytics have found widespread use in a variety of areas, most notably education. Indeed, it can revolutionize the teaching-learning process by providing innovative methods for delivering instructional films in novel ways. Sony, for example, demonstrated its AI edge analytics-based solution for producing video content: its primary unit, in conjunction with different licensed appliances, demonstrates how video streaming may be elevated to a new level.

Why do edge analytics matter in 2021?

Industry 4.0 is now facing significant obstacles that are impeding its development. Computing at the edge is the answer to many of these problems. Here are some of the most significant benefits that the edge offers to Industry 4.0.

Increased interoperability

IoT systems may be a hurdle to smart manufacturing’s growth. However, an IoT network is just as good as its interoperability.Concerns about interoperability are among the most significant obstacles to its adoption since there is no standard protocol. By relocating computer operations to the edge, part of the requirement for a universal standard is eliminated. When devices can transform signals on their own, they will operate with a wider range of systems.

The edge also acts as a link between information and operational technologies.

Reduced latency

When sending data to a distant cloud data center for analysis, the latency is lengthy and unpredictable. For time-sensitive use-cases, the chance to act on the data may be lost.

Edge computing provides predictable and ultra-low latency, making it suitable for time-critical scenarios such as any use-case where the process is mission-critical or in motion. Delays in moving cars, equipment, components, people, or fluids may result in lost opportunities, excessive expenses, or security concerns.

Analytics-enabled technologies

Companies decide to include edge computing technology in their network architecture due to the number of benefits it offers. There is something for everyone, from real-time AI data analysis and improved application performance to significantly reduced operational expenses and planned downtime.

Technologies enabling Edge Analytics:

Edge Computing with Multiple Access

Multi-access Edge Computing or MEC is an architecture that allows for computing and storage resources inside a radio access network (RAN). The MEC contributes to increased network efficiency and content delivery to end-users. This device can adjust the load on the radio connection to enhance network efficiency and reduce the need for long-distance backhauling.

Internet of Things

The idea of the Cloud of Things (CoT) is still in its early stages, but it has many potentials. All computing power in a Cloud of Things is obtained from the extreme edge at the end-user.

We all have powerful gadgets in our hands, such as industrial tablets, smartphones and Internet of Things (IoT) devices in our plants. These gadgets are often underused. Although IoT devices currently have limited computing capacity, many modern devices are very powerful.

Using mobile or IoT enabled devices, cloud services may be provided directly at the edge. These devices may be coordinated to provide edge cloud services. For example, a vehicle traveling through traffic may transmit traffic warning alerts to others and re-arrange alternative routes without user involvement.

The idea of the Cloud of Things is akin to that of fog computing. All IoT devices in a CoT form a virtualized cloud infrastructure. All computing in the CoT is done by the IoT devices themselves, which are shared resources.

Fog Computing

Fog computing, fog networking, or simply “fogging” refers to dispersed computer architecture. It brings cloud computing (data centers) to the network’s edge while also locating data, storage, and applications in a logical and efficient location. This location occurs between the cloud and the data source and is often referred to as being “out in the fog.”

Future of edge analytics

Edge computing has gained traction because it provides an efficient answer to growing network issues connected to transferring massive amounts of data that today’s businesses generate and consume. It’s not simply a matter of quantity. It’s also an issue of time; applications rely on processing and responses that are becoming more time-critical.

According to projections, there will be 21.5 billion linked IoT devices globally by 2025. Consider if half of them could do computational work for other devices and services. This massive, linked computer network would be especially useful for smart manufacturing.

Conclusion

Edge analytics is a burgeoning area, with companies in the Industrial Internet of Things (IIOT) industry increasing their investments every year. Leading OEM and suppliers are actively spending in this rapidly growing industry. Edge analytics offers real business benefits by decreasing decision latency, scaling out analytics resources, addressing bandwidth problems, and cutting expenditures in industries such as retail, manufacturing, energy, and logistics.

Utthunga’s expertise in edge analytics and edge computing along with IIoT technologies enables organizations to scale their processing and analytics capabilities exponentially. Contact us for your edge analytics requirements to leverage the real-time business insights from the edge.

What is Industrial Asset Management?

An industrial asset management system helps manage the physical assets of your plant such as machines, devices, equipment, network and operations. An industrial asset management system is essential for all the industries, discrete or process, where any minor fault in the assets may lead to expensive delays, shutdowns, plant operation interruptions, quality issues, security threats, data loss, and a lot more.

By deploying a smart industrial asset management system, the plant operators can be assured of uninterrupted plant operations with greater asset throughput.

Industrial asset management consists of services, systems, and software programs that enable you to monitor and control the operational cycle. Artificial intelligence systems and IoT solutions can be efficiently used in setting up an enterprise asset management system. The various features of an asset management system include planning, scheduling, work management, asset maintenance, supply chain management, financial management, environment management, materials management, and information management.

Types of Industrial Asset Management

The 3 major types of asset management are:

1. Traditional Asset Management: The assets are monitored and managed by an expert. He/she identifies the deviations in the asset performance based on their experience and takes corrective measures to resolve the issues. This kind of asset management requires immediate corrective action.
2. Periodic Inspection: This type of asset management is based on various predetermined parameters. Plant maintenance personnel conduct this assessment at frequent intervals based on the performance and maintenance requirements.
3. Online Asset Management: The latest trend in the asset management process is Online Asset Management. Here, the sensors and other devices are used to capture real-time data from the assets deployed on the plant floor. All the data is sent to a central system, where it is processed to identify the deviations from the defined indicators and corrective actions are taken to resolve the issues using a blend of automation and manual intervention.

These three types of asset management processes look at the current condition of the asset or process. They don’t evaluate the future performance and lifespan of the asset. A systematic enterprise asset management system provides a comprehensive approach to evaluating the assets and enables the stakeholders with the ability to make informed decisions.

Why Industries Need Asset Management?

There are various reasons why industries must deploy a smart asset management system in the plants:

• To meet the stringent safety requirements. This is especially important for process industries as the plants need to meet intrinsic safety requirements without fail.
• To setup up preventive and regular maintenance procedures to ensure optimum performance of the equipment
• To track the lifecycle of the assets
• To allocate a budget based on future predictions made from the available data
• To enhance performance reporting and compliance
• To keep pace with the dynamic customer requirements and demands

Various Ways to Implement the Asset Management

You will need the following to set up an efficient industrial asset management system in your plant:

• Data Collection: Reliable data procured from sensors, manual inspections, as well as IIoT devices.
• Edge Devices: Edge devices are required to gather data from the sensors and process it partly on its own and pass it to the cloud when necessary.
• Remote Asset Manager/Edge Manager: A remote asset management system with abilities to monitor and manage the health of the assets over the air.
• HMI (Human-Machine Interface): An HMI (human-machine interface) can be used to gather data in one place, connect the system with the correct database, oversee KPIs, monitor machines, track production times, track tags, and visually display data.
• Efficient Analytical System: An appropriate analytical system can be implemented to track and analyze the device performance at various times and define indicators to detect deviations in near real-time.

Challenges in Implementing an Asset Management System

Implementing any new process will definitely see many challenges with respect to people, technology, and process:

• Acceptance by the Employees: The biggest challenge regarding people is the resistance to new implementations and changes. Employees need to be elaborated on the benefits of implementing the enterprise asset management system and how it will help make their work more efficient.
• Not Considering Sub-processes in Addition to the Main Processes: Companies may face challenges in the process if the asset management policy was drafted without considering the entire process along with the sub-processes. If the policy was drafted only from the perspective of the asset manager, it may be challenging to integrate it into the process. Therefore, the processes and subprocesses need to be considered while building an asset management policy and plan.
• Learning Curve: The use of an automated asset management system can be challenging to people who have been following manual methods for years. Employees need to be provided proper training and ample time to adapt to the new model to overcome this challenge.

Trending Tools and Technologies Used in Asset Management

Asset managers in the manufacturing and processing industries will be expected to be more tech-savvy in the future. Some of the technologies to be seen in upcoming years for managing assets are:

• Data-driven Asset Management: Managing assets in modern plants is more data-driven. With smart sensors, edge computing, and tinyML in place, gathering plant floor data and processing it on the edge will yield better results. On the other hand, cloud computing will allow more dynamic implementations and allow processing even larger and distributed datasets providing a comprehensive view to the entire data.
• Robots and Automated Bots: Automation of most processes of asset management can be enabled by the use of advanced robots and automated bots. These AI bots are used in places that are not easily accessible or are potentially harmful to humans.
• High Definition Cameras: Image sensors are widely used in machine vision cameras. These image sensors are used for a wide range of applications in robotics, pattern recognition, information detection, etc. The need for high performance, low-footprint, and reliability has paved the way for integrated sensors that combine all the image sensing functions. For example, an Infrared Imaging sensor can operate irrespective of the light conditions. Due to this ability, IR imaging sensors are used in marine, military, air forces, etc., to capture thermal images of the objects.
• Artificial Intelligence (AI): Modern manufacturers are paying attention to introducing the latest technologies to improve the production processes. Using AI techniques in process modeling, optimization, debottlenecking, troubleshooting, etc. has become quite common nowadays. For instance, plant owners can use AI models to learn the normal behavior of the plant using historical data and detect the anomalies during operation. A root-cause analysis can be carried-out to draw inferences that can be used to implement in actions.
• Augmented Reality (AR): Augmented Reality is comparatively a newer technology and industries are not yet prepared to adopt this rising trend. However, AR has a vast range of applications that can prove to be beneficial for the process industries. Whether it is managing their warehouse for object recognition or to check the system failures. Imagine, diagnosing a boiler with the help of AR glasses. This can be really helpful in minimizing the time consumed in inspecting the machines and equipment.