Succeeding by Predicting Failure: AI Startup Using Factory-Based Sensors to Avert Shutdowns
In a world reliant on the power of machines, breakdowns can be problematic, sometimes catastrophic.
A system failure at an auto manufacturer can cost up to $1.3 million an hour. An offshore oil platform going offline can waste around $3.5 million a day.
But technical failures don’t just drain money. They also risk the safety of employees, put customer relations on the line and can threaten the environment.
To counter this, many firms implement predictive maintenance programs to detect equipment flaws before damage occurs. Traditional techniques rely on installing a large number of purpose-built sensors and measuring the performance of specific machines.
But this narrow, isolated view means that larger, holistic problems are often missed or root causes aren’t addressed. And this can lead to additional, preventable breakages further down the line.
Reliability Solutions is taking a different approach. The Krakow, Poland-based startup uses deep learning to derive insights from the huge amount of data already being collected by the myriad of sensors previously installed by their clients, on premise.
A member of the NVIDIA Inception program, Reliability Solutions is one of the first companies to take this approach and is already working with some big names, including energy provider Tauron and automakers Opel and Volkswagen.
Predicting Failure Efficiently and Effectively
Predictive maintenance aims to predict when equipment failure might occur in sufficient time to take preventative measures.
Reliability Solution’s approach to predictive maintenance uses deep neural networks powered by an NVIDIA Tesla P100 GPU cluster in the data center.
“By using deep learning, we can avoid the common pain points associated with traditional predictive maintenance models — high hardware costs, high engineering costs and long lead times,” explains Mateusz Marzec, CEO of Reliability Solutions. “With the power of NVIDIA GPUs, we can train our models, using terabytes of data, in a few hours.”
One of the largest energy companies in Europe turned to Reliability Solutions to build a predictive model that could detect the failure of a fluidized bed combustion boiler. These systems burn solid fuels to create energy at lower temperatures and with reduced sulfur emissions than would otherwise be possible.
As the entire network of boilers supply approximately 50 TWh of electricity to over 5.5 million customers per year, any downtime has extensive consequences.
Reliability Solutions developed a predictive model based on 700GB of historical data collected from sensors already installed at the plant. It also utilized a full description of the events that had impacted the boiler over a three-year period, from 2013-2015. This data was used to train a series of deep neural networks on a cluster of NVIDIA GPUs.
When validated against operating data for 2016, the system predicted all of the failures with an accuracy level of 100 percent — and without any false positives. Every breakdown of the fluidized bed boiler was anticipated from between 2.5 and 17 hours before the actual breakdown took place. This would’ve given maintenance teams sufficient time to stop the malfunction, or at least minimize the damage caused.
With the predictive maintenance module now fully incorporated, the company is making yearly savings of 4 million euros.
From Predictive to Prescriptive
Reliability Solutions is now turning its attention to developing prescriptive maintenance. This enables them to not only identify what will go wrong, and when, but to suggest a recommended course of action.
This approach also applies to companies looking to optimize the performance of their machinery, rather than fix issues. In these cases, the prescriptive model can propose steps that will save companies money or reduce their CO2 emissions, for example.
Reliability Solutions is already working with one of the biggest chemical companies in central Europe to minimize resource consumption and maximize output by optimizing the configuration of their installation.
The startup built a deep neural network-based metamodel of the chemical installation and then validated the configuration in real life. They found that the metamodel had a 90 percent accuracy rate.
Using the prescriptive model, Reliability Solutions was able to reduce the company’s hydrogen consumption by more than 2 percent, which will save the company millions of euros each year.
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