Chalk & Talk: Front End
Chalk & Talk: A 3-phase deployment – Phase 3
Analyze, clean and transform industrial time series: from discovery to virtual sensors creation to maximize data utilization
The industry is moving towards more advanced utilization of data from industrial equipment. This data, often in the form of complex time series, requires careful preparation and/or thorough exploration by domain experts. When industrial users embark on analyzing their data to solve a specific problem (such as detecting specific failures, anticipating machine downtime, etc.), they […]
Chalk & Talk: A 3-phase deployment – Phase 2
Chalk & Talk: A 3-phase deployment – Phase 1
Blind failure prediction by Simon Gazikian, CEO of Amiral Technologies during TechInnov 2023
Scientific publication – Sensor fault detection using machine learning applied on acoustic test bench
Article written by Thibaut Le Magueresse, data-scientist at Amiral Technologies, Jérémie Derré and Florent Mercat, Airbus Operations – Acoustics Testing Team Introduction This paper presents a study of sensor fault detection from an acoustic test bench, performed by machine learning. The concerned rig is based on the modal generation and detection principle, aiming at characterizing […]
The importance of time series in the industrial sector
Industry 4.0 sets a major revolution in the modern industrial landscape. This new era is characterized by the integration of connected digital technologies within manufacturing processes, commonly referred to as the Industrial Internet of Things (IIoT). IIoT brings a new dimension of efficiency, flexibility and innovation to industry, paving the way for more efficient production […]
What are the 3 types of industrial maintenance?
Industrial maintenance is a critical pillar of operational performance. In Industry 4.0 environments, where production systems are increasingly automated and interconnected, ensuring equipment reliability is more strategic than ever. There are three main types of industrial maintenance used across production sites: corrective, preventive and predictive maintenance. Each approach reflects a different level of maturity in […]
Predictive maintenance: Labeling anomalies
What you need to know about anomaly labeling Labeling anomalies is a central step in predictive maintenance. Its significance is demonstrated through the complementarity between humans, domain experts, and machines. In this article, explore what anomaly labeling involves and how this crucial step strengthens predictive maintenance. What is anomaly labeling in the context of predictive […]
