AM-RAIL™ as a part of Smart City




What is a Smart City?



Summary of benefits to different stakeholders

Emphasize on data-acquisition on an early stage




In this article, we describe the use case of AM-X™ platform in Smart City transportation, the requirements for the ecosystem of the client, and implementation of the solution emphasizing the benefits in both short- and long-term. The goal of this article is to give an idea of how AI-solutions can be implemented to our clients' ecosystem and to emphasize the importance of gathering vast amounts of clear data at an early stage in the development.


What is a Smart City?





The ecosystem is commonly built on the following three essential elements:


On a common level, the process of these kinds of ecosystems are quite straightforward.

  1. Sensors measure the environment and collect data for analytic platforms, with time and location labels.

  2. Analytics platforms provide analyzed guidance information to the client’s ERP-system.

  3. On ERP-system, operators are inspecting and labeling the data, and the labels are provided back to the analytics platform.

  4. Self-learning algorithms on the analytics platform read measurement data to provide more accurate predictions per each new data-point.

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When implementing the AM-RAIL™ as a part of Smart City, a variety of options for the sensors, analytic platform models and ERP-systems are available for the ecosystem. Some of the options and their related considerations are listed below as suggestions that we recognize as valuable to the Smart City ecosystem.



All sensor data should be labeled with time and location labels when using the sensor.


Analytic platform models:




We have categorized the labels into the following categories:

  • Fault labels (a label that indicates a fault in the ecosystem)
  • Effect labels (a label that indicates causal effect in the ecosystem)
  • Component labels (data that is labeled relation to a component)
  • Simulated data labels (simulated data label from engineering models)


We often hear from our networks that they do not wish to be first ones to implement AI-solutions to their own operations, but want to wait for markets to mature and consider applying AI-solutions at a later stage. We believe that the major fault in this reasoning is that after AI-solutions have developed enough, the AI would be easily applicable to various operation models. Unfortunately, this mix-up of the intelligence explosion and use-case based machine learning application is a common mistake. As intelligence explosion would make a machine that only requires calculation power to develop, machine learning requires adaptation to the specific user cases. If the assumption of the intelligence explosion is faulty, it does not matter, because the machine would still be solving most of our problems, one way or another.

The second line of reasoning we often hear from our clients is that the system would only bring benefits after several years of training of the AI, which would mean that ROI for the project investment does not make sense. Our belief is that when the implementation process is built smartly, we can add value to the clients´ ecosystem early on in different phases that support each other. This way the investment will start paying back quickly, and the benefits of the system will increase when more data is gathered.

On implementing AM-RAIL™ to railway and city maintenance operations, we estimated the following timetable and benefits from the system.


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Phase 1, Sensors and improvement of the current planned predictive maintenance by providing an increasing amount of data from the field. This data can be used as an alarm-rate maintenance operation right after the sensor data is gathered. The data is stored in the database. The estimated time for implementation is 1,5 – 2 years. Components can be recognized and the information can be used for ownership management.

Phase 2, An analytics platform is chosen and implemented. The analytical platform can recognize decay rates from the data gathered from the sensor. The decay information can be used for maintenance operations and the operations can be moved towards a preventive maintenance model.

Phase 3, The analytics platform starts to receive labels from the ERP-systems for self-learning algorithms. The self-learning algorithms will receive fault, effect, component and simulated data labels from ERP-systems which leads to the platform performing better with each new data point. Maintenance can be moved towards a predictive maintenance model.

Phase 4, AR-solutions can be implemented to operations to provide guidance for the field operators. While providing guidance the guiding person can label components from the pictures and train machine vision solutions for recognition of these components. After a sufficient amount of component labels, machine vision can be used for guiding robotics and maintenance the operations can be further automated in steps. The ultimate goal is set to automate maintenance operations fully.

As we see, AI-solutions will bring benefits to operations also in short-term.


A summary of the benefits to different stakeholders


Construction and maintenance operators

Recognizing the need for maintenance before additional cost occurs - moving to a Predictive maintenance model. This increases route capacity and decreases costs of maintenance operations by decreasing down-time of the track. In addition, component recognition can be used to maintain uptodate information for ownership management.

By adding measurement solutions to provide data for a BIM engineering software and providing real-time uptodate information, the right baseline information can be provided for designing operations. These measurement solutions will also remove the need for manual measurement of structures and gathering the measurement data into database can be automated.

In addition, an AR-platform can be used to increase efficiency and quality in the installation operations by providing guidance remote guidance during installation operations. At the same time, operators can label videodata and labels can be used for the training of machine vision solutions.

For traffic and transportation operators

Moving into a Predictive maintenance model decreases requirement for maintenance operations on the route and improves route capacity.  Facial recognition system provides smooth passenger transportation and provides baseline for TaaS-solutions.

Smart city, citizens of the city, Institutes and companies operating in the city

Camera systems attached to transportation machinery can measure and recognize components from surrounding structures in its route meaning anything from the surveillance of street-lights to the condition of buildings. Changes in these structures can be surveilled and data can be used for the allocation of maintenance resources. The same data can then be used for building a uptodate digital model of the city. All this data can be used as a platform for new inventions resulting in a better quality of life for the citizens.



Emphasis on data-acquisition on an early stage

Implementing AI-solutions to most of the ecosystem requires a lot of data to work with. The data needs to be raw, clear and labeled to provide sufficient information for self-learning algorithms that aim to provide information for operations and finally make automation possible for the application.

When a system requires huge amount of clean raw-data from several sensors, the makes cost of a single measurement to work as major cost-driver. This means heavy and expensive sensors wont do the trick, instead by using multiple cheap sensors in the ecosystem most of the benefits can be achieved.

In addition, the know-how of the field operators needs to be digitalised and stored as data. Machine learning means that the machine needs to learn what to do, and so far humans are acting as teachers. This means personnel working in the ecosystem needs to use digital tools on their operations in order to provide sufficient information for machines to eventually operate automatically.

We recommend all operators to start collecting data by implementing cheap sensors to their ecosystems and storing the raw-data to databases for the future’s needs. Ungathered information can never be recovered.


Recon AI
Henri Memonen


Don’t wait FOR your data to arrive -

let’s get it for you.


Should a robot do it?

This is one of those blogs where we get all excited about machine learning.

Fortunately, getting all excited about machine learning (ML) is pretty easy. All it takes is to google for pictures with the tags “machine learning” or “AI” and then adore high-resolution images showing robots, stylized human brains, randomly placed circuit boards and/or cog wheels. Usually in shades of blue. These things have little to do with ML and have more in common with technology magic, but honestly, does anyone really care?

In a short and precise blogpost the tech-entrepreneur Marco Varone calls bullsh*t on years’ worth of marketing hype around ML that lead to overblown expectations on its business value. Though potentially powerful, making self-learning algorithms into a product (one that people actually want to buy) comes with a whole host of challenges that need to be addressed. The worst offender on this list, hands down, is getting sufficiently good training data. A lot of it. A real lot. Yet, somehow, in our experience, people who want to do ML beyond the standard MNIST character recognition get repeatedly surprised by the demands placed on training data. Many articles like this of Marco have been published, yet things haven’t really changed and ML is hyped as ever. Never mind Microsoft’s Hitler-bot. Or rather peculiar ways to solve the data-issue.

If there really is no relationship between ML knowledge and having a successful career in “data engineering”, then we feel we are in an excellent position to start this blog. After all, we seek not only to celebrate all the nice things ML bestows upon humanity, but try to make a decision whether self-learning algorithms are a good investment in a given business field or not. To be clear: we are intrigued about the many varieties of self-learning algorithms and their potential use-cases. Yet, we feel it is important give a fair amount of attention to problems that are known to be major headaches in various industries and then see if and why some sprinkling-in of ML might help it.

Frankly, it is quite hard to say whether any of the ML techniques is actually worth applying to a given problem or not. The factors that influence the decision to say “yes” or “no” are often not ML-related. Imagine, for instance, an elevator that is operated with voice command rather than buttons, complete with recurrent neural networks and microprocessor for each elevator. A “innovation” like this is certainly worth of the technological progress of the 21st century, yet I would argue that putting old-fashioned buttons there might still be the better option... economically speaking. Buttons are cheap, they do a good job and they are not a problem for anybody. And this is fundamentally what we care about in this blog. Is it economical to apply ML? Does it work well enough to change entire processes? Is there a way to use ML in a role that does not cause too much disruption, so as to ease implementation in a given industry?

In a series of upcoming articles we discuss the economic value of ML and try to identify key-fields that might profit from self-learning algorithms that do not look too obvious, yet. Perhaps, in the future, an intelligent agent will be much better at identifying and predicting use-cases for ML. Until then, though, we feel that is worth doing it ourselves.


T. Kerst, Recon AI