Industrial IoT Data Analysis & Data Visualization

The data collected from IIoT devices can provide useful insights into the performance of machines, processes, and overall operations. By analyzing this data, businesses can identify patterns, trends, and anomalies that can help them optimize their operations and make data-driven decisions.

For example, a manufacturing company can use IIoT data to monitor the performance of their machines and identify potential issues before they lead to costly breakdowns. This can save the company time and money by preventing downtime and improving overall efficiency.

Improving Predictive Maintenance

One of the most significant benefits of IIoT data analytics is its ability to improve predictive maintenance. By analyzing data from sensors and machines, AI can predict when a machine is likely to fail and schedule maintenance before it happens. This can cut maintenance costs and prevent unexpected downtime.

Enabling Real-Time Decision-Making

With the help of AI, IIoT data can be analyzed in real-time, allowing businesses to make quick and informed decisions. For example, a logistics company can use real-time data from their fleet of trucks to optimize routes (the Traveling Salesman Problem) and improve delivery times. This can give them a competitive edge in the market and improve customer satisfaction.

Machine Learning

Machine learning is a subset of AI that involves training algorithms to learn from data and make predictions or decisions without being explicitly programmed. In the context of IIoT data analytics, machine learning algorithms can analyze large datasets and identify patterns and anomalies that humans may not be able to detect.

For example, a machine learning algorithm can analyze data from sensors on a production line and identify patterns that indicate a potential issue with a machine. This helps prevent costly breakdowns.

Natural Language Processing

Natural language processing (NLP) is a branch of AI that deals with the interaction between computers and human language. In the context of IIoT data analytics, NLP can be used to analyze unstructured data, such as text from maintenance reports or customer feedback. You could also simply ask for whatever information you need at a given moment.

By using NLP, businesses can gain insights from this unstructured data and use it to improve their operations. For example, a manufacturing company can analyze customer feedback to identify common issues with their products and make improvements to their production processes.

Deep Learning

Deep learning is a subset of machine learning that involves training algorithms to learn from data and make decisions in a similar way to the human brain. In the context of IIoT data analytics, deep learning can be used to analyze complex and large datasets, such as images or videos.

For example, a deep learning algorithm can analyze images from a security camera in a manufacturing plant and identify potential safety hazards. This can help businesses prevent accidents and improve workplace safety.

Two of these are industrial IoT systems we built. The third is a direction for AI in data visualization.

Battery control in the energy industry

Skyfri runs an asset management platform for solar and battery portfolios. We built the energy management system (EMS) underneath it, which controls the batteries and inverters on each site. It decides when batteries charge and discharge, runs peak shaving and energy trading, responds to requests from the local grid operator and keeps exports under the grid connection limit. More in the Skyfri case study.

Digital twin for thermal energy storage

For Kyoto Group's thermal energy storage plant we built a SCADA-based system and a digital twin. It includes surveillance functions, weather data collection, energy price analysis and an interface for simulating plant performance. More in the KYOTO case study.

Industrial IoT Data Visualization

Imagine creating visualizations with natural language prompts. Instead of writing SQL or any other code, you ask how well a certain part is functioning, and a few seconds later you get the full picture in an easy-to-understand format.

The amount of data we gather rises exponentially, which will put pressure on data analysis teams. This is a challenge for businesses, because analyzing this data manually will not be possible. AI can take over this work and save time. It may also help with ETL (Extract, Transform, Load) or even clean data for you.

In the future, we can expect to see more advanced AI algorithms being used to analyze industrial IoT data. These algorithms will be able to analyze data in real time, identify patterns and anomalies, and make decisions without human intervention. This will enable businesses to optimize their operations and make data-driven decisions faster.

What is industrial IoT analytics?

Industrial IoT analytics is the analysis of data from sensors, machines and controllers in factories, power plants and other industrial sites. It finds patterns, trends and anomalies in how machines and processes perform, so a team can plan maintenance before a breakdown and make decisions on real-time data.

What data does an industrial IoT system collect?

In Monster, an airport lighting system we built with AP-TECH, the dashboard shows battery levels, power supplies, connectivity and UPS status. At KYOTO, a thermal energy storage plant for Kyoto Group, the system also collects weather data, analyzes energy prices and watches the site with infrared cameras. More in industrial IoT examples.

How does industrial IoT data analytics work?

Analysis happens in the fourth of the five layers described in what is industrial IoT:

1. Sensors and controllers measure the equipment.
2. Field protocols such as Modbus carry readings to a gateway.
3. An edge device makes decisions that cannot wait for the network.
4. A cloud or on-premises platform stores and analyzes the data.
5. Dashboards, alarms and reports show results to operators.

How does AI help analyze industrial IoT data?

Machine learning finds patterns in sensor data that point to a machine about to fail. Natural language processing reads unstructured text such as maintenance reports and lets people ask for data in plain language. Deep learning analyzes images and video, for example camera footage that shows safety hazards in a plant.

Which tools work for visualizing IoT data in a manufacturing plant?

For proofs of concept, Streamlit worked well for us on KYOTO. Real-time monitoring and control of that plant runs on a SCADA-based system. For daily operator work, the dashboard should follow the site's procedures, with roles, alerts and widgets operators can move and resize, as described in industrial IoT dashboard design.

How do you connect plant equipment to IT systems for real-time data?

Machines and controllers speak field protocols such as Modbus over RS-485, and factories often use OPC UA. A gateway on site reads them and sends the data on, often over MQTT, to a cloud or on-premises platform. On Zeronest's energy platform, a device reads the inverter over Modbus and sends data over MQTT on its own LTE link.

What makes industrial IoT data hard to analyze?

Equipment usually comes from several manufacturers, and each needs its own integration before a dashboard shows useful data. The readings also come in many formats and in volumes no analyst can review by hand. Sites lose their network too, which is why the battery control system we built for Skyfri runs fully offline.

Can industrial IoT data be exported to common analytics tools?

It depends on where the platform keeps the data. When readings land in a data store or pipeline the company controls, its analysts can query them with the tools they already use. On Skyfri's platform, for example, site data runs through a Databricks data pipeline hosted on Azure.

AI already helps analyze industrial IoT data and gives businesses useful insights. With machine learning, natural language processing, and deep learning, businesses can get much more out of their IIoT data and improve their operations.

As the use of IIoT devices continues to grow, we can expect to see more advanced AI algorithms being used to analyze data in real-time and make decisions without human intervention. This will enable businesses to stay competitive and achieve their goals through data optimization.