Víctor Vallejo Carballo

We are immersed in an historic technological revolution in which data analysis has taken centre stage and sooner rather than later will lead all business organisations, or at least those that want to remain competitive and profitable, to become fully Data Driven organisations. This technological revolution has given rise within the industrial sector to the term Industry 4.0, or fourth industrial revolution, a new scenario that leverages both the automation of processes and the interconnection of data based on IIoT technology (Internet of Things applied to Industry). This is a set of tools, devices and, of course, sensors, which are responsible for both data collection and analysis for subsequent decision-making at the operational and management level within the organisation itself. Sensors, an essential component Sensors, therefore, have become a basic component, since, through the detection, measurement and analysis of factors, they enable greater automation of industrial processes. Their measurements are subsequently translated into commands, which are then executed by the actuating/executing components within a well-defined action/response plan. However, the functionality offered by sensors is not limited exclusively to increasing process automation; their use becomes essential for industrial maintenance, as these assets can enable significant savings in maintenance or repair costs caused by unplanned production stoppages, improvements in profitability thanks to constant monitoring throughout the manufacturing process, thus generating higher performance rates on production lines, as well as improvements in the safety of industrial workers themselves. So, what exactly do the sensors measure? Photo: Arshad Pooloo / Unsplash As can be expected, the answer to this question is a wide range of variables, which will depend on the specific characteristics of what is manufactured, but we can group them into environmental variables (temperature, humidity, light, vibrations...), mechanical variables, derived from the machinery itself (position, proximity, speed,...), electrical variables from energy consumption (voltage, current, resistance, power,...) and process variables about physical or chemical conditions generated during manufacture (fluid level, temperature increase in machines and cooling times, waste level, densities,...). Sensors have become one of the most sensitive parts in the process of capturing early information to provide an adequate response in time and form during manufacturing. Given such heterogeneity of available variables, it follows that sensors have become one of the most sensitive parts in the process of capturing early information to provide an adequate response in time and form during manufacturing. This is why both the identification of the type of sensor to be installed, its location within the chain and the maintenance of these sensors are crucial to ensure that the measurements are reliable and significant, as incorrect measurements due to a defect or fault in the sensor can end up leading to imbalances in the composition of the manufactured goods or can even mean a total shutdown due to a critical error, either because of having used too many or too few components or ingredients that are necessary in the right proportion to maintain the quality expected and approved in the standards, protocols and certifications. Cyber Security Vulnerabilities, threats and cyber-attacks on industrial systems May 24, 2022 So, what type of maintenance should be carried out? Photo: Mech Mind / Unsplash There are different approaches to address this question, which can be summarised in 4 different types of maintenance, depending on the implementation strategy. Corrective, where the sensor can work until it fails, at which point it is repaired or replaced. Preventive, which is carried out systematically through inspections, whether or not the asset has failed, and which, together with corrective maintenance, are the most widespread strategies to date. Predictive maintenance, which makes use of predictive algorithms to estimate in advance the moment of sensor failure, so that maintenance will only be carried out when necessary, anticipating the incident. Prescriptive strategy, which is based on predictive maintenance and incorporates elements of maintenance management, costs, etc. A proactive strategy focused on anticipating and correcting and will determine with greater precision the useful life of equipment, risks of failure and potential impact on the system. As sensors become cheaper, their implementation continues to be promoted throughout the production chain, and this interconnection of data generated during manufacturing, in combination with Artificial Intelligence techniques within the Big Data technological environment, is causing a shift from prevention to forecasting in maintenance processes. It will be less and less necessary to stop processes to analyse errors and/or solve problems when deploying constant predictive maintenance, since predictive models executed in real time using historical, inventory and process data will be used to model the failure pattern by learning patterns that precede failures in a machine, sensor, asset, etc. and, consequently, predicting when maintenance or replacement of the sensor or part will be necessary before the functional failure occurs. In other words, we will be implementing a proactive strategy focused on anticipating and correcting and will determine with greater precision the useful life of equipment, risks of failure and potential impact on the system. Photo: Vaclav / Unsplash This proactive strategy based on the 'sensorisation' of the plant and the adoption of automatic learning techniques contributes to the fact that, in the long term, predictive maintenance offers lower recurrent costs than other maintenance strategies, since the higher initial investment is returned in increased ROI, as the number of incidents detected in advance increases, thus reducing the rate of critical failures in the chain. A clear example of this change in maintenance strategy can be seen in those industries that have an intensive use of electric batteries, both in controlled static environments (industrial facilities, telephone systems, etc.) and in dynamic mobility environments (railway environment, electrified transport, etc.), where it is vital to estimate acceptance-rejection values for batteries with a projected useful life of several years to ensure that they will not be operating in the near future within critical ranges that compromise their integrity. Photo: Lenny Kuhne / Unsplash In the automotive sector, more and more car manufacturers are relying on predictive maintenance to continuously monitor the performance of electric vehicle batteries. Sensors installed in the car constantly feed data to a virtual model of the battery, known as a digital twin, which enables large-scale modelling of the service performance and estimation of optimal battery life under different usage conditions in a laboratory environment. This approach to creating digital batteries leads to significant time savings, as physical testing of different conditions is a handicap due to the long lifetime of batteries, while allowing multiple simulations in parallel without the need to deploy complex and costly physical test environments. Knowing how long it will take to reach critical values that compromise performance will allow specific actions to be deployed to extend battery life by replacing parts and improving the design of new cells and batteries. Moreover, this optimisation of performance has the additional positive effect of reducing the environmental impact, as less and less waste will be discarded and reused more frequently, extending vehicle lifetime and allowing batteries to be a real key lever for change in the decarbonisation of transport and part of the industrial processes. 🔵 More content on IoT and Artificial Intelligence can be found in other articles in our series – the first article of which can be found here, AI OF THINGS AI of Things(I): Multiplying the value of connected things February 28, 2022

Digitalisation or, to put it another way, the movement to transform analogue processes and physical objects into digital ones, is advancing at a constant pace, encompassing practically all aspects of our daily lives. One of the sectors where it is having the greatest impact, despite the existence of some currents that predicted its decline due to the rise of the Internet and digital marketing, is none other than outdoor advertising --OOH, an acronym for Out Of Home-- which in this way becomes one more mechanism in the process of urban conversion from the traditional city to the Smart cities of the future. Mupis or Urban Furniture as an Information Point are undoubtedly the star device supporting this digitalisation process in order to be successful in digital marketing/advertising campaigns at street level. Traditionally, it has been one of the tools used for signage and outdoor advertising in cities, located in locations with high visibility, both outdoors and indoors, to maximise the number of impacts of campaigns. Advantages of digital outdoor advertising The digitalisation of these assets brings with it a series of advantages, such as the elimination of printing and travel costs to change the signage, but above all the adaptation of the commercial message in different locations and time slots or daily. This improves the capture of the attention of the target audience in each area, thanks to specific animated communications and creating interactive campaigns that measure the effectiveness of the campaign in real time through the sentiment generated and conversion rates. And all of this at a contained cost by being able to plan exposure periods instead of having static signage for weeks or months as has been the case up to now. Photo: Finn / Unsplash At the same time, the decrease in costs in the production of LCD devices has only pushed towards the complete transformation of these assets, which together with the integration of more technology in the terminals (WiFi, bluetooth, augmented reality, communication with RRSS, etc.) allow offering a more enriching experience, favouring the alignment between different channels of communication with the customer and, therefore, improving conversion rates or reaching your target audience more effectively. Smart displays open up new possibilities and locations This reduction in costs is also opening up the possibility of finding new locations for adapted digital Mupis, such as in petrol stations while refuelling, in areas of electric chargers,... New locations that can mean an improvement in revenue for businesses derived from the placement of these Mupis, but above all allow them to better connect with users at times when their attention span will be greater as they have almost no competition from stimuli (do not look at the mobile while refuelling etc.). Thus, one of the main keys to successful communication and subsequent conversion is to correctly identify the target audience to be addressed and to know what their general mobility patterns are with the greatest possible granularity, namely: mobility on a daily, weekly, time slot, most frequented locations and most likely routes to reach them, recurrence of visits, average dwell time and a long etcetera will allow us to modulate and articulate the projected message in an unprecedented way. Photo: Yuksel Goz / Unsplash By identifying areas where clusters of individuals from your target audience are concentrated or recurrently pass through, you will be able to communicate with them more efficiently; moreover, the adaptability of the message will allow, for example, the message on a rainy Monday to be very different from that of a sunny Friday afternoon, or to have different messages depending on the location of the Mupi where they are projected. Big Data to adapt content on smart screens Why not? We are all aware that, to a greater or lesser extent, the weather, the day of the week, the time of day and other exogenous factors modify our perception of reality and, therefore, our sensitivity and capacity to react to stimuli. As we said before, we are not as motivated, happy or sensitive on a sunny day as we are on a grey and rainy one, or at 8 a.m. on a Tuesday morning waiting for the bus as we are on our way to our favourite activity on a Sunday at noon. You really only need to have different communications prepared so that when the right conditions are met, those that bring together clusters of target audiences with sites or locations, weather, calendar and the stimuli to which they react best, the content is displayed per screen or set of screens spread over a given geographical area. The digitalisation of Mupis turns them into programmed platforms, where the purchase of the space and the delivery of the message occurs in real time What seems like science fiction is already a reality thanks to Big Data and some of the mobility solutions, such as Smart Steps, since working with multiple sources and large volumes of anonymised and aggregated data allows us to profile and discover insights that previously went unnoticed, or to detect improvements for situations or actions that we took for granted and unchangeable. This ability to profile the audience and its subsequent communication through smart Mupis is also available to public administrations, which can carry out public awareness campaigns in different areas such as ecology, sustainability, civic awareness, etc., so that when the optimal conditions are met, these campaigns can be executed, such as this one in Australia to promote healthy eating habits to tackle the obesity epidemic among its citizens. Photo: John Cameron / Unsplash If you want to reach more people, or rather if you want to reach the people you really need to reach, profiling consumers not only by their consumption habits but also by their general mobility criteria is key to attracting, converting and/or retaining customers at a contained cost in a world that interconnects the physical and digital planes in more and more facets of daily life. If you want to know more applications of the fusion of the Internet of Things and Artificial Intelligence, known to us as AIoThings, you can read other articles in the series: AI OF THINGS AI of Things(I): Multiplying the value of connected things February 28, 2022 IA & Data AI of Things (II): Water, a sea of data March 16, 2022 AI of Things IoT anomalies: how a few wrong pieces of information can cost us dearly September 18, 2023 AI of Things AI of Things (IV): You can already maximise the impact of your campaigns in the physical space April 26, 2022