By harnessing the power of artificial intelligence in our products and services, we drive business growth and help companies achieve their goals faster.
Lublin Industrial Upland Foundation
NOMATEN Centre of Excellence — a scientific department of the National Centre for Nuclear Research in Świerk
Microelectronics, Electronics and Photonics Cluster
T-Mobile Technology Incubator
AI systems for early fault detection, degradation forecasting, and technical condition assessment of machines.
Dedicated diagnostic systems based on neural networks, fully integrable with existing technical and IT infrastructure (PLC/HMI/SCADA).
Systems for automatic detection of defects and deviations in the production process.
Adaptive models for dynamic process parameter tuning and waste reduction.
AI model deployment over wireless technologies (Wi-Fi, 5G, MQTT), edge computing, and clear visualization on industrial dashboards (Grafana).
Precise vibration and noise measurement across multiple measurement points, with full time synchronization and high sampling resolution.
Identification of vibration sources, imbalance, and misalignment using FFT and order analysis.
Detection of rolling-element and gear-tooth defects through envelope analysis, cepstrum analysis, and signal filtering in characteristic frequency bands.
Support in research and development activities involving structural analysis, 3D modeling, durability testing, and numerical simulations.
Integrated vibroacoustic analysis that enables linking sound sources with structural vibrations within a single measurement environment.
Development of automation solutions with consideration for cost, efficiency, and scalability.
Creation of strategic and operational action plans that account for technological and organizational growth.
Assessment of operational and technical risks, implementation of work standards, current-state analysis, and audits of processes and infrastructure.
Support in research and development activities involving structural analysis, 3D modeling, durability testing, and numerical simulations.
Development and deployment of roadmaps that are a key element in the effective execution of projects related to R&D, digitalization, and automation.
The intelligent machine diagnostics system is based on vibration signal analysis.
Data collected in real time by triaxial accelerometers is processed by a convolutional neural network, which identifies anomalies indicative of potential faults or degradation of mechanical components.
The system provides operators with key information about the machine’s technical condition — including a health score, predictions of the remaining useful life (RUL), and a complete history of detected irregularities.
The solution supports predictive maintenance, enabling early intervention and reducing the risk of unplanned downtime.
An innovative system powered by neural networks enables automatic classification of the technical condition of components during the quality control process. Built on a proprietary application, it analyzes measurement data in real time and detects both defective and non-defective cases, eliminating the need for manual inspection and increasing the reliability of assessments.
The system supports automated testing, provides real-time data monitoring, and allows measurement results to be saved in any database format. Thanks to easy integration with the production line, the solution accelerates decision-making, increases inspection repeatability, and helps maintain high production quality.
AI models were applied to analyze the data, focusing on their representation in feature space. An autoencoder-type neural network was trained to map high-dimensional data into a three-dimensional latent space while preserving the key structural information.
Clustering algorithms were then used to identify typical groupings and patterns. This approach enabled effective exploration of the dataset, detection of dominant classes, and a deeper understanding of the distribution and diversity of the observed phenomena.
In the FEA study, the propagation of elastic waves in a composite laminate was simulated, taking into account its anisotropic mechanical properties. The simulation made it possible to determine the direction of elastic wave propagation within the material and to calculate the actual propagation velocity for the given structural configuration.
The obtained results were used to precisely calibrate an acoustic emission system designed for defect detection in real composite structures. This enabled more accurate damage localization and better alignment of signal-analysis algorithms.
At WaveFlow System, we develop scalable and flexible solutions from the FlowInspect series that support industrial companies in automating quality control processes and implementing predictive maintenance strategies.
Our systems combine artificial intelligence algorithms, IoT technology, and sensor networks into comprehensive analytics platforms that operate both locally and in the cloud.
By leveraging Edge Computing and Edge AI technologies, real-time data analysis is performed directly on devices—ensuring faster response times, greater reliability, and complete control over industrial processes.
We tailor our solutions to the specific operational and strategic needs of each organization, delivering tools that genuinely improve efficiency, reduce costs, and drive business growth in the era of Industry 4.0.
At Waveflow System, we develop intelligent solutions for industrial companies. We integrate artificial intelligence, automation, process management, and product development engineering into a cohesive ecosystem of tools tailored to the specific operational and strategic needs of our clients.
We design and implement autonomous systems for maintenance support and quality control — flexible, scalable, and precisely adapted to the characteristics of a given industrial environment.
We assist our clients in data analysis, development and implementation of industrial AI models, execution of R&D projects, and the optimization and management of production processes.
We have a team of experts with over ten years of experience
in both production environments and research & development
A specialist in machine diagnostics and the development of industrial AI models. He designs intelligent measurement systems that support maintenance and quality-control processes, integrating artificial neural-network algorithms with IoT and edge-computing technologies.
He earned his doctorate through an international PhD programme run in collaboration with the University of Pisa and now serves as an assistant professor at Lublin University of Technology. The author of numerous scientific publications and a recipient of the Polish Minister of Science Award, he successfully blends academic insight with engineering practice, advancing intelligent measurement systems for Industry 4.0.
A specialist in industrial process automation and the deployment of advanced IT systems within manufacturing environments. Focused on operational optimisation, process digitisation and the delivery of both green-field and brown-field investment projects, he supports technological transformation across a range of industries.
With more than eight years of experience executing complex programmes for global brands including Mercedes-Benz, Audi and Porsche — he is an expert in strategic planning, risk analysis and elevating production efficiency through cutting-edge technologies. By blending strong technical skills with proven project-management expertise, he delivers solutions that boost competitiveness and accelerate the digital maturity of industrial enterprises.
A specialist in materials-technology development and industrial R&D applications. His work centres on fracture mechanics and the non-destructive diagnostics of engineering structures. He pioneers innovative damage-identification methods that merge classical mechanics with advanced materials diagnostics.
He is a Professor at Lublin University of Technology and Head of the Department of Fundamentals of Production Engineering. The author of more than 60 scientific publications and co-inventor on 19 patent applications, he is a recipient of the Commission of National Education Medal and the Polish Minister of Science Award. A member of numerous scientific societies and an expert reviewer for the National Science Centre (NCN), he actively collaborates with industry and research institutions across Europe, spearheading projects aligned with Industry 4.0.
Specialist in the field of automation and robotics of production processes. Focused on the design, implementation, and optimization of robotic workstations using industrial robots, PLC systems, and Industry 4.0 technologies. Integrates vision systems, sensors, and safety solutions in compliance with industry standards.
A hands-on expert with extensive experience in the automotive sector. Delivers projects for leading manufacturers across Europe, focusing on process efficiency, reliability, and flexibility. Combines technical expertise with a project-oriented approach aimed at measurable business outcomes.
5 Steps of Collaboration with WaveFlow System
We discuss your needs, goals, and challenges.
Based on the gathered information, we prepare a report outlining possible directions and actions.
We develop a clear plan along with a preliminary cost estimate.
We design, build, and test in short iterations.
Each stage is summarized with a progress report.
We implement the solution and migrate the data.
At this stage, we perform full testing and train your team.
We apply final adjustments and prepare the system for autonomous operation.
We hand over the initiative and remain available if needed.
Your team operates independently, while we provide second-line support.
Project no. FEPW.01.01-IP.01-0007/23 under “Eastern Business Accelerator 2”
Project no. FENG.02.28-IP.02-0013/23 under “Unicorn Hub Startup Booster”
Project no. FENG.02.22-IP.02-0008/23-00 under “hub4industry”
