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IIoT Data: From Sensing to Predicting with Liferay and Timbergrove
5 min de leitura

IIoT Data: From Sensing to Predicting with Liferay and Timbergrove

In this article, we take a step-by-step look at how a successful Industrial Internet of Things (IIoT) project works—from the data origins (e.g., a physical sensor) to data capture, transmission, and analysis. We also discuss some of today’s challenges implementing a successful IIoT project, specifically on issues pertaining to data access. Finally, we look at a real-world IIoT project example and what you can learn from it.
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Abstract

In this article, we take a step-by-step look at how a successful Industrial Internet of Things (IIoT) project works—from the data origins (e.g., a physical sensor) to data capture, transmission, and analysis. We also discuss some of today’s challenges implementing a successful IIoT project, specifically on issues pertaining to data access. Finally, we look at a real-world IIoT project example and what you can learn from it.

 

Introduction

When it comes to the Industrial Internet of Things (IIoT), many of us are amazed by how far we’ve advanced in remote sensing and analytics. But for Ian Uriarte, CEO of Timbergrove, a Liferay Service Provider Partner and expert in IIoT and manufacturing solutions, the real wonder is how much farther we can develop IIoT projects to improve manufacturing processes and efficiency.

Where Data Comes From

In the world of the IIoT, data comes from many sources, including third-party systems, equipment sensors, and human-initiated data like uploading an image from a mobile app. We’re going to focus on the data that starts at a sensor (i.e., a physical device that can translate a real-life condition like pressure or vibration into an electrical signal).

While the IIoT is relatively new, professionals still incorporate many best practices of telemetry and telematics in their IIoT projects, such as optimal sensor and transmitter placement. Sensors are the link between the digital and physical worlds of IIoT, and it’s here where human expertise is critical. A sensor can’t determine if it has been correctly mounted and calibrated. Additionally, humans are required to ensure proper capture and analysis of the data. For example, machine learning can play a role in detecting that something is not working and, based on historical data and changes in patterns, provide an alert to a human who will check on what’s happened and take necessary action.

The Data Highway

After taking measurements and recording data, sensors must then transmit the data to a central location known as “the beach.” Depending on the circumstances, this might be via a wired internet network, a control network bus like Profibus or Fieldbus, wireless protocols such a LoRa WAN or xbee, WiFi, a cellular network, or even a satellite connection. With potentially hundreds of sensors taking thousands of measurements each second, data volume can quickly become an issue. Today it’s cheaper than ever to send data, but imagine sending gigabytes of data an hour, 24 hours a day—the data bill adds up. One way to make data transmission more efficient is through edge processing, that is, analyzing data at the sensor itself. The sensor then needs to only transmit the processed output data to the beach.

But data transmission is only part of the challenge. Very few people have the capacity to physically store so much data on their own servers. That’s why on-demand cloud computing platforms like AWS and Azure are so popular in the IIoT programs. The “on-demand” component means that clients can easily expand or shrink their storage allotment so that they only have to pay for the space they use. The elasticity of the cloud allows for flexibility in cost and availability of resources. Changes in production and manufacturing can be managed fluidly without penalty for “buying” larger or smaller virtual spaces. The ultimate goal is to have a single platform to access all sensor data at once on a consistent and reliable basis.

Data Analysis

The first step after receiving data is to make sure it’s complete. For example, a company may want to filter out data without a timestamp or identify unusual measurements for causation. As an example, camera footage can confirm whether a person was blocking a light sensor. The key is to understand ahead of time which variables you want to measure.

“It’s important to understand that the IIoT data is rarely analyzed by itself. Instead, data is pooled with other relevant datasets into an enormous “data lake.” For example, an engine manufacturer may have a data lake that includes sensor measurements from thousands of customer power plants and other engine-powered equipment over several years. Or, a manufacturer may want to create a data lake of sensor and other assembly line data from facilities around the world to compare, contrast and analyze operational efficiencies and need for equipment maintenance. By consolidating data, it’s possible to present, evaluate, and apply data trends more accurately,” says Uriarte.

Many manufacturers use the IIoT data today for equipment monitoring and prediction. The IIoT data analysis can help predict the optimal maintenance interval for industrial equipment based on external factors such as operating profile and even ambient temperature. If data analysis of the IIoT can make such detailed predictions, one may wonder why we don’t use the IIoT to directly control equipment for optimum service life.

“Unfortunately, even the fastest connections are just too slow to effectively control machinery in real time. But I predict that the introduction of 5G and 6G networks will completely change what’s possible with the IIoT as real-time remote control becomes a reality,” says Uriarte.

The Question of Data Access

One of the biggest debates in the IIoT revolves around data access and ownership. Today, the question of who has access to the IIoT data depends a lot on who’s collecting the raw data and the intended use of that data.

For the management of any sort of industrial facility, it’s easy to see how a single system involves components and products from different original equipment manufacturers (OEMs) around the world. Yet, all of these products work together physically to form a system that works for the manufacturer. Right now, this is not the case with an IIoT project. The equipment manufacturers tend to protect information about their equipment and send data directly to their own data hub for analysis.

“While OEMs are happy to send their clients the results of their data analysis, clients need to push for more access to the raw performance data if they want to make better real-time decisions at the system level. Clients hold the power to access more of their equipment data; they just need to work together to agree on a standardized data consortium,” adds Uriarte.

Even within the same company, data silos that isolate data sets often result in inefficiency, poor decision-making, and a lot of frustration.

Overcoming Today’s IIoT Challenges

Let’s consider an example in the oil and gas industry and how data integration made a positive difference. Drilling for oil is an expensive and delicate operation. A Timbergrove client was using an IIoT program to monitor and improve performance. One key drilling measurement is mud pressure. When analyzing the data, a sudden change in mud pressure might mean that the drill hole has collapsed. The problem is that it could also mean that another team of operators is (intentionally) raising the drill.

“When it came to cleaning up the pressure data, it was impossible to tell which team provided different pressure measurements because each team recorded data in separate systems. We created a solution that enabled the client to capture time-stamped events and match these events with recorded historical data to “annotate” the data so that the context of the sensor reading was clear for more accurate and useful daily reports,” says Uriarte

In short, technology used with the IIoT is a powerful tool that can completely change how business leaders and operating managers make informed decisions. The key is to avoid data silos and instead promote data integration wherever possible. This will help make your IIoT projects all the more valuable.

How Timbergrove and Liferay Can Help

Take the next steps in developing a successful IIoT project by establishing clear goals for your organization. More than 25% of IIoT projects stall at Proof of Concept due to the lack of a good strategy. 

To combat this, Timbergrove and Liferay are offering a free consultation on your next IIoT project. In this technical consultation, we will analyze your specific business needs, analyze your current road map, and assess where you are on implementing that road map. 


 

About Liferay

Liferay helps organizations solve digital challenges with omnichannel intranet, portal, commerce, and integration solutions. Our platform is open source, reliable, innovative, and secure. We try to leave a positive mark on the world through business and technology. Thousands of organizations in financial services, healthcare, government, insurance, retail, manufacturing, and multiple other industries use Liferay. Liferay. One Platform. Endless Solutions.

 

About Timbergrove

Timbergrove is a team of free-spirited, out-of-the-box thinkers who specialize in creating custom software and product development to solve operational pains for the manufacturing, oil and gas, and healthcare industries, among others. We’re in the business of solving problems; technology like IoT is simply the tools we use to achieve the end goal of making life better and easier for the organizations we work with. Check us out at www.timbergrove.com 

Publicado originalmente
20 de Agosto de 2021
Última atualização
17 de Dezembro de 2021
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