The full potential of predictive maintenance has not yet been utilised. Current solutions focus on individual steps of the predictive maintenance cycle and only work for very specific settings. The overarching challenge of predictive maintenance is to leverage these individual building blocks to obtain a framework that supports optimal maintenance and asset management. The PrimaVera project has identified four obstacles to tackle in order to utilise predictive maintenance at its full potential: lack of orchestration and automation of the predictive maintenance workflow, inaccurate or incomplete data and the role of human and organisational factors in data-driven decision support tools. Furthermore, an intuitive generic applicable predictive maintenance process model is presented in this paper to provide a structured way of deploying predictive maintenance solutions https://doi.org/10.3390/app10238348 LinkedIn: https://www.linkedin.com/in/john-bolte-0856134/
The full potential of predictive maintenance has not yet been utilised. Current solutions focus on individual steps of the predictive maintenance cycle and only work for very specific settings. The overarching challenge of predictive maintenance is to leverage these individual building blocks to obtain a framework that supports optimal maintenance and asset management. The PrimaVera project has identified four obstacles to tackle in order to utilise predictive maintenance at its full potential: lack of orchestration and automation of the predictive maintenance workflow, inaccurate or incomplete data and the role of human and organisational factors in data-driven decision support tools. Furthermore, an intuitive generic applicable predictive maintenance process model is presented in this paper to provide a structured way of deploying predictive maintenance solutions.
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In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.
Het is een tijds- en kostenintensief proces om de conditie van assets in de publieke ruimte te monitoren. Nieuwe technologie in de vorm van 3D LiDAR scanning biedt nieuwe mogelijkheden voor conditiemonitoring. Het doel van deze KIEM-aanvraag is (i) om de hardware geschikt te maken voor frequente en goedkope opnames in de stedelijke omgeving, (ii) de analysetechnieken van de geproduceerde datasets verder te ontwikkelen en (iii) een geannoteerde dataset gefocust op asset management te produceren. Dit zorgt ervoor dat publieke en MKB-partijen slimmere, snellere en volledigere onderhoudsbeslissingen kunnen nemen. Het consortium van Fietskoerier.nl, Sonarski, Gemeente Amsterdam en de Hogeschool van Amsterdam heeft elkaar gevonden in de vraag: “Hoe kan (publieke) LiDAR data bijdragen aan SMART Asset Management?” Dit project bevat een unieke combinatie van twee technologieën die op dit moment in ontwikkeling zijn (i) sensor data gedreven conditiemonitoring en (ii) point cloud algoritmes op LiDAR data. Fietskoerier.nl heeft de resources om op een duurzame manier de stad in kaart te brengen. Sonarski heeft een oplossing voor het uitvoeren van de 3D scans en Gemeente Amsterdam is een belangrijke kennispartner en heeft groot scala aan assets in de publieke ruimte. De deelnemers van dit project zien deze aanvraag als een eerste stap en hebben de intentie om te groeien tot een groter consortium welke de gehele keten van onderhoud omvat.
Korte projectomschrijving: Aanleg en operation & maintenance van windparken op zee, hebben een grote behoefte aan ‘first time right’ in de gehele keten. Onderhoud en de engineering zijn complex en de kennis en innovatieve initiatieven zijn versnipperd bij zowel MKB als grootbedrijven en kennisinstellingen. Verbeteringen zijn nodig binnen de thema’s Cable Maintenance, Bolting, Remote Operations and predictive maintenance, Rotor Blades én Energieopwekking, -opslag en –balancering. Offshore wind zorgt voor een groeiende werkgelegenheid voor (N)NL. HG : accent op realisatie testfaciliteiten en ontwikkelen van opleidingen en onderzoek,
