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Predictive Maintenance at the Edge Keeps Devices Running

predictive maintenance

Gas compressors are a critical component in a multitude of industrial environments. Compressors pressurize and push gas into pipelines, portable tanks, and ships that transport liquefied natural gas (LNG) around the globe for energy production.

“Compressors are quite expensive devices. They cost millions and millions, and are usually at the heart of the production process,” says Alexander Bergner, Director of Product Management at TTTech Industrial, a company that specializes in real-time data collection in industrial workflows. “In LNG ships, when they do not compress, they actually have to burn the gas in order not to have too much pressure in the tanks. In a chemical industry, if they don’t have a compressor running, then systems get clogged and you have to take them all apart to clean and recommission them.”

So keeping gas compressors running at their best is just as critical as the essential functions they perform. That’s why predictive maintenance, which uses data collection and analytics to track the condition of compressor components, is increasingly common in production processes that rely on compression.

Not All Predictive Maintenance Solutions Are Built the Same

Predictive maintenance not only helps prevent wear and tear of compressers to extend equipment life, but more importantly, it allows operators to better plan when to replace parts—especially in situations where gas compressors are out at sea for long periods of time and parts can’t be easily replaced.

For instance, with LNG ships, the logistics of predictive maintenance can get complex. As ships sail from one port to another, it’s critical that components do not fail midway through. Predictive maintenance enables operators to dispatch technicians and parts to the next port where maintenance needs to occur, so everything is ready when the ship puts in. Conversely, predictive maintenance prevents replacing parts too early, which can drive up costs.

“Ship compressors are especially sensitive to well-planned maintenance,” Bergner says. “The spare parts need to be there at the right time and they need to be the right spare parts.”

To do this, the predictive maintenance needs to be built with performance and real-time monitoring in mind, which is easier said than done.

#PredictiveMaintenance not only helps prevent wear and tear of compressors to extend equipment life, but more importantly, it allows operators to better plan when to replace parts. TTTech Industrial via @insightdottech

HOERBIGER, a leading supplier of gas compressor components, learned that the hard way when it was looking for a better way to track the condition of its compression components. It wanted to provide a predictive maintenance solution to its customers in oil, gas, automotive, and process industries, where they rely on its cylinders, pistons, heads, and piston rings for compressors that mostly operate at edge sites.

The company built an in-house predictive maintenance solution with custom-designed hardware. However, they needed a next-generation system that could provide the computational power and flexibility to adapt to upcoming needs, Bergner explained.

That’s why HOERBIGER turned to TTTech Industrial, a subsidiary of TTTech Group, which went to work on a prototype to address the company’s specific needs. “They presented their technical challenges, and we sketched the solutions. We even went so far to as to sketch the workflows,” says Bergner.

HOERBIGER needed an IoT solution with edge capabilities since, in many settings, gas compressors operate 24/7 with or without cloud connectivity. TTTech Industrial based the solution on its Nerve edge computing platform, which enabled it to develop a proof of concept in about 100 hours with fewer than 150 lines of code.

HOERBIGER quickly approved the design and retained TTTech Industrial for installation and integration. “We at TTTech Industrial were responsible for providing the data ingestion framework and the storage and visualization framework specific to their needs. Their software engineers focused on developing the algorithms, which actually do the predictive maintenance,” Bergner says.

A Real-Time Edge Platform for Predictive Maintenance

Nerve is an open, secure, and modular edge platform that provides the foundation for myriad use cases, such as maintenance of cold forging tools, implementation of digital twins in manufacturing processes, and remote management of industrial production software.

For the HOERBIGER case, TTTech Industrial provided a Nerve Integration Services Package. The package delivered the architectural underpinnings and edge management software on top of which HOERBIGER built its predictive maintenance application.

The Nerve platform was installed on an industrial PC from MOXA with an Intel® Core i7 processor. The use of Intel processors and hardware were essential in the HOERBIGER because they included the necessary certifications to operate in hazardous environments.

The platform’s Soft PLC module also enabled high-speed data acquisition, which is required to calculate the wear of components such as piston rings and valves. This is possible by measuring cylinder pressure in relation to crank position values at sample rates of 50 KHz. As many as 600,000 samples per second must be processed.

Nerve’s Data Services module processes the data leveraging Nerve’s gateway application, which sends data to the Timescale Time-Series Database for post-processing to estimate compressor wear. Data visualization is then enabled by the Grafana system integrated in Nerve.

Another significant benefit of using Nerve, whether for HOERBIGER or other customers, is that the platform runs in cloud-connected systems as well as air-gapped edge environments. In some environments, air-gapping is necessary, accoding to Bergner.

“Imagine you run a fleet of machines. Part of that fleet is air-gapped because it is in critical infrastructure with no easy or legal possibility to bridge that air gap,” says Bergner. “You still want to have a homogeneous way of dealing with all the machines out there, so your solutions have to be capable of operating online, offline, or air-gapped.”

Nerve’s edge functionality makes it possible to securely collect and analyze data without a connection. But customers can access preprocessed edge data through a web portal linked to a central management system running on-premises or in the cloud.

Predictive Maintenance as a Service

Bergner estimates the HOERBIGER predictive maintenance solution will eventually reach thousands of locations, depending on how many customers sign up for it. Customers can buy the predictive maintenance as a service or they can use it internally with their own maintenance technicians, he explains.

Predictive maintenance is key to both HOERBIGER and its customers, enabling the company to deliver critical gas compressor parts at precisely the right time. “It allows companies to plan the logistics for replacement correctly,” Bergner says. “These are very critical parts, and you do not want the compressors to fail.”

Going forward, Bergner foresees more predictive maintenance use cases built on Nerve for different industries. Because of its edge capabilities, Nerve will enable companies to deliver cybersecurity updates and add functionality to their edge devices as needed. This will help future-proof operations so they can keep adapting as the technology evolves, Bergner explains.

This article was edited by Christina Cardoza, Associate Editorial Director for

About the Author

Pedro Pereira has covered technology for a quarter century. He has freelanced for some of the biggest names in IT publishing and an extensive list of marketing agencies and technology vendors. He was a pioneer in covering managed services and cloud computing, and currently writes about cybersecurity, IoT, cloud, and space. He holds a degree in Journalism from UMass/Amherst.

Profile Photo of Pedro Pereira