Back in 2014 an Accenture report predicted that investment in the Industrial Internet of Things would reach $500 billion by 2020. A combination of cheap sensors, powerful data processing and machine learning has enabled companies to make their industrial processes significantly smarter and more efficient.
A good example of this in action comes in the rail industry, where a number of fascinating use cases have emerged in the past year.
For instance, Finnish company Sharper Shape have been using drones to map utility networks. They use machine learning to identify trees that are at risk of falling onto power lines.
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When I spoke to CEO Tero Heinonen recently, he told me that using drones to perform this task rather than humans can cut those costs by around 30%. Not only is it more cost effective though, it’s also considerably faster, with daily assessments possible as opposed to the weekly or even monthly assessments that are currently the case.
Or you have the European Train Control System, which is aiming to introduce a single, harmonized signalling system throughout Europe to replace the often incompatible systems deployed in individual nations.
Equally interesting is the University of Huddersfield led project to use sensor technology to automatically operate the level crossings that are dotted throughout the rail network. An interesting aspect of the project is that the sensors derive their energy from the vibrations of the track as trains travel along it.
These vibrations are being put to similarly useful ends by a German startup called KONUX, who deploy sensors on the track, with algorithms then monitoring the vibrations sent as trains zoom past to determine the health of the track.
The team behind the venture believe they can reduce rail maintenance costs by up to 25%, which when these costs typically run into billions of dollars per year is not to be sniffed at.
Smarter Transportation
Such improvements are also on the cards across the border at French rail company SNCF. They are undertaking a range of projects that are hoping to utilize machine learning to drive insights from the growing volumes of data they are able to capture about the functioning of their rail network.
What began with an effort to more rapidly perform corrective maintenance is rapidly moving towards a more proactive process whereby they are able to detect the early signs of potential failure and rectify matters before it impacts upon service delivery.
I spoke recently with Héloïse Nonne, Head of Data Science at SNCF, ahead of her presentation at the upcoming AI Europe event in London. She explained the growing importance of data to the operations of the company.
“SNCF Big Data Fab is a data expertise center where we create new service and products for our engineers and business units. Our primary goal is to increase performance and reliability of the system. SNCF has a long history of using data for operations. Nevertheless, until now, datasets were treated separately and for post event diagnostic. Today’s methods and data technologies allow us to create new value by combining data and accelerating access to data for our engineers. Any data project is a win if it saves time for our engineers so they can focus on their job, if it provides new insights by analyzing weak signals and correlations and uncovers upstream causes of failures, or if it anticipates malfunctions or incidents with real time monitoring and sends alerts to operations ahead of failure,” Nonne said.
Strategic Data
Central to the success of the project is to ensure that data moves out of departmental silos so that it can be combined and provide the kind of contextual insights that will make it really useful. Across projects for various departments, the team to automate much of this process to expedite the combination of data sets into one source.
Railways are among the most asset intensive industries in the world, so any improvements in the functioning of those assets can only yield significant returns.
It feels like we're on the cusp of breaking the age old routine of break and then fix, and moving towards a world in which problems are spotted before they happen.
Predictive maintenance analytics offers the promise of capturing crucial, and often hidden, data in real time, which when combined with existing data from visual inspections promises to revolutionize the industry, boosting asset availability and service levels.
Despite this promise however, it’s far from an open goal. Indeed, a recent survey from Bain found that just 10% of executives regarded digital technologies as a key priority for them. It is therefore, a relative minority who see how the Industrial Internet of Things has the potential to change nearly every aspect of industrial machinery.
“SNCF’s top direction made digital technologies a key component of the group’s transformation. In 2014, “Direction Digital” was founded, a department dedicated to digital technologies and the associated change management. Since then, 5 expertise centers, the “Fabs” have been created: Big Data, IoT, Design, Open Innovation and Agile Transformation. In all of their projects with SNCF departments, the “Fabs” receive support from SNCF top management. Many projects have been successful and the Fabs now act as accelerators for digital innovation,” Nonne revealed.
If you would like some help in becoming a data driven organization, you might enjoy this recent post on the topic where I outlined some pointers to get you started.
