Amiral Technologies

✈️ Ravis de sponsoriser cet événement dédié à l’intelligence artificielle dans l’aéronautique ! 🗓️ Retrouvez François Behr le 5 décembre sur Future Intelligence et échangez autour de notre logiciel DiagFit

💡 Find out why Time Series can be a real asset to industry. - Reduce unwanted downtimes - Improve product quality - and much more.. Discover the role of time series and how they can improve the efficiency of your operations. 👉 https://lnkd.in/eaDupY6v #TimeSeries #AnomaliesDetection #SeriesTemporelles #IIoT #Industry4

🗓️ Rencontrez nous sur l’espace #startup (hall 7.1) du salon #TechShow pour en savoir plus sur notre logiciel #DiagFit. Logiciel dédié au traitement des séries temporelles industrielles et la détection d’anomalies. #IIOT #MaintenancePredictive #seriestemporelles #anomalydetection

🚄 Train doors are essential to the smooth running of a train journey. However, this equipment is regularly put to the test by passengers and the weather. Find out how, using DiagFit, it has been possible to generate a model for analyzing data from train doors to support maintenance. Discover the full use case 👉 https://lnkd.in/es3nbqgQ DiagFit, the software that transform your industrial time series into actionable insights. #TimeSeries #DiagFit #Industry #IIOT #MachineLearning #railway

📢 Amiral Technologies will be present at Tech Show Paris! We look forward to hearing from you about your industrial data projects: - Automated analysis of lab test results - Analysis of non-destructive testing results - Root cause analysis for maintenance - Predictive maintenance and failure anticipation ✨ Come to meet Katia Hilal & Laurent Lafarge and discover how DiagFit can help you to speed up the processing of your industrial time series 📅 When? November 27 and 28, 2024 📍Where? Paris Expo - Porte de Versailles, Hall 7.1 Start-up

💡 How to detect failures on critical equipment remotely ? Our customer, a specialist in drilling tools for oil and gas wells, faces a major challenge : detecting and anticipating failures on critical electronic boards located hundreds of meters under the sea. Discover how we build an anomaly detection model with DiagFit to analyse weak signals on the board voltage data. 🔍 Learn how 👉 https://lnkd.in/dxtJ_Ew8 #MachineLearning #Industrie4.0 #Innovation

💥 New release for DiagFit, discover what’s new! 💥 A new release 3.1 is available for DiagFit, our solution to build operations' workflows from industrial time series.🚀 Developed for domain experts, our software allies easy, efficient and powerful features with a fluent UI. 🎯 Test DiagFit 3.1 now! 👉 https://lnkd.in/eyNhsibr #Innovation #Prediction #PredictiveMaintenance

🔊 Amiral Technologies participe au Tech Show Paris ! Nous sommes ravis de vous annoncer notre présence au Tech Show Paris dans le hall dédié à la Data et l'IA. Venez découvrir les nouveautés de notre logiciel DiagFit, dédié à l'exploitation des séries temporelles industrielles pour les transformer en sources de décisions opérationnelles. 📅  Rejoignez-nous dans l'espace Startup Hall 7 - Porte de Versailles à Paris François Behr Laurent Lafarge Katia Hilal #TechShowParis #Innovation #DiagFit #SalonTech

❄️ The iceberg of predictive models reveals much more than we imagine ! AI doesn't solve everything with a wave of a magic wand. Setting up robust predictive models requires meticulous work upstream. 💡 Thanks to #DiagFit, master these challenges and simplify this data analysis and preparation work carried out by Data-Scientists. #Innovation #Tech #DataScience #IA #MachineLearning #IA #Industry

Get actionnable insights from your IIoT data, use #DiagFit! 🚀 Thanks to its automated, machine learning-based functionalities, DiagFit enables domain experts to explore their time series to extract information useful to their business without having to write a single line of code. Discover some of the features: ✨ Data exploration: Visualization of time series thanks to a fluid interface Create virtual sensors using simple mathematical operators Frequency analysis using spectra and spectrograms ✨ Creating sensor subgroups: Analysis of sensor correlations Spatial clustering Model selection based on data typology ✨ Improving model explicability: Single-sensor sub-models Specialized multi-sensor sub-models Analysis of sensor contribution More info 👉 https://lnkd.in/dXpBamyc #DiagFit #MachineLearning #NoCode #IndustrialTimeSeries #AI