5 Technical Reasons

Why Edge Computing Improves your Predictive Maintenance Program

Predictive Maintenance based on Industrial Internet of Things and Edge computing will transform your current production facility into a “smart industry” and enter the era of Industry 4.0

Many analysts are referring to this period as the next wave of Industrial Revolution. This is, in fact, thanks to three technological advancements that are here to stay – The Industrial Internet of Things, Big Data analytics, and Edge computing. In fact, according to a McKinsey report, IIoT has the potential to increase your business productivity by 10 to 25 percent, yielding a global economic impact as high as $1.8 trillion by 2025.

In this blog post, we will discuss on why Edge computing is the right architecture for your Predictive Maintenance Program.

True Predictive Maintenance needs a holistic approach to pinpoint the source of the problem.

1. Unlock Predictive Maintenance with IIoT

Industrial Internet of Things collects a large amount of data from manufacturing equipment in production and transmits it to devices that can store and analyze it. Previously, the obstacle was the technology to analyze the data collected. Edge computing allows much of that analysis to be done right on-site or on the factory floor, which reduces the burden on networks while keeping the costs low.

Today’s accepted model for Internet of Things is data flowing to the Cloud. However, Industrial Internet of Things requires an architecture that is less Cloud-centric to unlock the full potential of a Predictive Maintenance Program. A study by Aberdeen Group showed that companies using a Predictive Maintenance IIoT solution could:

Increase return on assets (ROA) by up to 24%
Reduce unschedule downtime by 3.5%
Increase overall effectiveness by 89%
Reduce maintenance by 13%

2. Collect All the Data Without Increasing your Costs

Data Collection is a crucial step for you to prepare your Predictive Maintenance (PdM) Program. PdM requires an architecture that uses both historical data and analysis in real time to optimize and predict when a failure is about to occur. A typical Industrial IoT sensor might collect data about equipment every couple of seconds or even several times a second. It is not viable for your business to connect all the sensors and all data that has to be sent to the Cloud. However, An Edge server is built to handle a lot of sensors and analyze the data in real-time close to the source. At the same time saving your business money on your Cloud computing costs.

The Edge architecture gives you the ability to collect data from different data sources and combine the information to make calculations and take decisions. Unlike previous legacy architectures, Edge computing simplifies data collection because it is designed to connect to a lot of different machines and operating protocols.

3. Real-time Analytics and Ultra Low Latency - Is the New Standard

Many of the deviations and anomalies require sub-second processing time. Due to latency and type of connectivity, Cloud computing cannot process data quickly enough for mission-critical systems. Predictive Maintenance with Edge servers enable real-time analytics and deploying algorithms near the device or the data source.

4. Enterprise-level Security like Never Before

Enterprises act behind a firewall to secure their digital assets. Using Cloud services or external data centers implies acting on the open internet. This can represent a security risk depending on your network setup.

The Edge architecture enables enhanced security options for both inbound and outbound data transmission. Such solutions can transform your current outdated gateways into smart gateways that can enable certificates, authentication, encryption and on top of that multiple private end-points.

In this manner, you are making sure that all the devices needed to enable a Predictive Maintenance Program are secured and built from the ground up for Industrial IoT security in mind.

5. Don’t Forget about Horizontal Integration

One of the most important and overlooked aspects that you should keep in mind while implementing a Predictive Maintenance Program is Horizontal Integration. A Predictive Maintenance Program with Edge computing can simplify integration with your other management systems such as service systems or CRM’s. An Edge server can trigger workflows to automate service processes.

True Predictive Maintenance needs a holistic approach to pinpoint the source of the problem. Edge computing enables you to connect multiple data sources to the machine data sources to get a more true data overview.

Did you miss out on our last blog post touching on the business reasons to develop a Predictive Maintenance Program? Read it here >

Posted by Göran Appelquist (Ph.D) | CTO on 27 Sep 2017

About the author

Göran Appelquist (Ph.D) | CTO

Göran has 20 years experience in leading technology teams. He’s the lead architect of our end-to-end solution and is extremely focused in securing the lowest possible Total Cost of Ownership for our customers.

“Hidden Lifecycle (employee) cost can account for 5-10 times the purchase price of software. Our goal is to offer a solution that automates and removes most of the tasks that is costly over the lifecycle.

My career started in the academic world where I got a PhD in physics by researching large scale data acquisition systems for physics experiments, such as the LHC at CERN. After leaving academia I have been working in several tech startups in different management positions over the last 20 years.

In most of these positions I have stood with one foot in the R&D team and another in the product/business teams. My passion is learning new technologies, use it to develop innovative products and explain the solutions to end users, technical or non-technical."