{"id":4799,"date":"2026-02-17T10:00:13","date_gmt":"2026-02-17T09:00:13","guid":{"rendered":"https:\/\/cz.ai-matters.eu\/service-highlight-how-data-and-ai-based-systems-create-reliable-solutions-for-predictive-maintenance\/"},"modified":"2026-05-21T18:31:15","modified_gmt":"2026-05-21T16:31:15","slug":"service-highlight-how-data-and-ai-based-systems-create-reliable-solutions-for-predictive-maintenance","status":"publish","type":"post","link":"https:\/\/cz.ai-matters.eu\/en\/service-highlight-how-data-and-ai-based-systems-create-reliable-solutions-for-predictive-maintenance\/","title":{"rendered":"Service Highlight: How data and AI-based systems create reliable solutions for predictive maintenance"},"content":{"rendered":"\n<p>CEITEC Brno University of Technology is engaged in predictive maintenance of machines and equipment as part of its activities in the field of artificial intelligence and high-performance computing. Thanks to the model industrial factory (testbed) and the NVIDIA DGX A100 \/ DGX H100 HPC cluster, which is part of the TEF infrastructure, we can not only analyze but also measure and collect data. One of our customers using machine time is the Czech company Neuron Soundware, one of the leading specialists in predictive maintenance. Prof. Pavel V\u00e1clavek, head of the research group, adds, &#8220;We have extensive experience in the diagnosis of electric drives, from vibrodiagnostics to the development and deployment of artificial intelligence algorithms in industrial microcontrollers. We work with clients in the manufacturing industry, either directly on specific projects or by giving them access to HPC technology to optimise their own operations.&#8221;    <\/p>\n\n<p>Predictive maintenance approaches for drives are becoming an essential part of modern operational strategies that are expanding across industries such as robotics, automotive, CNC and other industrial applications. These solutions facilitate early fault detection, condition monitoring and follow-up in drive systems. <\/p>\n\n<p>Permanent magnet synchronous motors (PMSM) are one of the most important types of electric drives on the market, especially in the automotive industry. One of the main risks associated with these motors is the possibility of a short circuit between the windings, which can lead to damage or failure. During operation, the motor windings are subjected to high stresses caused by a combination of mechanical and electrical factors such as increased vibration, elevated temperature and high voltage. These influences lead to degradation of the insulation of the conductors, resulting in short-circuit faults. Diagnostics can not only help detect faults, but also predict faults before they occur, allowing time for planned repairs. Changing control strategies at an early stage and shutting down the machine immediately if necessary can protect equipment from further damage and loss.     <\/p>\n\n<p>Depending on the specific needs, we use a range of diagnostic methods that include both conventional algorithms and combinations of these with artificial intelligence applications. Several parameters need to be taken into account when planning a specific methodology: budget, time available for implementation, need for universal solutions, response after fault detection, level of automation, etc. It is recommended to start the implementation of these diagnostic systems already in the prototyping phase. This approach reduces costs and increases the value and sophistication of the final product. Our service typically includes the design of an appropriate algorithm and additional hardware such as vibration sensors, if applicable.    <\/p>\n\n<p>One of the highly innovative methods we use is the application of a conditional convolutional autoencoder. Our team specializes in the design and development of autoencoders with integrated fault detection algorithms, which we then integrate into the target microcontroller for use in controlling or monitoring electrical devices. The microcontroller is capable of reliably detecting short circuits between windings in real time.  <\/p>\n\n<p>Alternatively, a methodology based on monitoring drive vibration data and analyzing it using artificial intelligence can be used for fault detection. The advantages of this approach over measuring electrical quantities are its direct correlation with the mechanical condition of the actuator, its speed of deployment and its ability to detect faults at an early stage before they affect electrical parameters. Detection based on the measurement of electrical quantities can be difficult in the case of low motor speeds and small fault ranges. However, the vibrodiagnostics method requires the installation of additional sensors.   <\/p>\n\n<h2 class=\"wp-block-heading\">AI application method using convolutional autoencoder<\/h2>\n\n<p>A teacherless artificial intelligence method based on a conditional convolutional autoencoder is used to diagnose mechanical, magnetic and electrical faults in permanent magnet synchronous motors (PMSMs). This approach responds to the current trend of increasing the computational power of microcontrollers, allowing advanced condition monitoring and diagnosis directly on edge devices (edge computing) without the need for external computing hardware. <\/p>\n\n<p>An autoencoder is an encoder-decoder neural network that is trained solely on data representing the healthy state of the engine. The encoder extracts key features of the input signals (e.g., currents, voltages, vibration or magnetic quantities) and maps them into a low-dimensional latent space. This latent space is a compressed representation of the normal system behaviour adapted to the constraints of the microcontroller. The decoder then attempts to reconstruct the original input signal from this representation. In real applications, the principle of fault detection is based on the evaluation of anomalies without the need for explicit knowledge of specific fault types. The models are trained in the TensorFlow environment and then optimized directly on the target microcontroller.     <\/p>\n\n<figure class=\"wp-block-image aligncenter size-full\"><img fetchpriority=\"high\" decoding=\"async\" width=\"841\" height=\"460\" src=\"https:\/\/cz.ai-matters.eu\/wp-content\/uploads\/2026\/02\/image.png\" alt=\"\" class=\"wp-image-3686\" srcset=\"https:\/\/cz.ai-matters.eu\/wp-content\/uploads\/2026\/02\/image.png 841w, https:\/\/cz.ai-matters.eu\/wp-content\/uploads\/2026\/02\/image-300x164.png 300w, https:\/\/cz.ai-matters.eu\/wp-content\/uploads\/2026\/02\/image-768x420.png 768w, https:\/\/cz.ai-matters.eu\/wp-content\/uploads\/2026\/02\/image-600x328.png 600w\" sizes=\"(max-width: 841px) 100vw, 841px\" \/><figcaption class=\"wp-element-caption\">Structure of the basic autoencoder<\/figcaption><\/figure>\n\n<p>Measured derivation times are short enough for real-time deployment and early detection of faults before irreversible thermal damage to the engine occurs. Combined with a multiphase motor configuration and a suitable fault management strategy, continuous operation with reduced maximum torque can be ensured. The ability of the device to remain in operation even after a fault is detected enhances user safety, for example, in the operation of electric vehicles.  <\/p>\n\n<figure class=\"wp-block-image aligncenter size-full\"><img decoding=\"async\" width=\"645\" height=\"714\" src=\"https:\/\/cz.ai-matters.eu\/wp-content\/uploads\/2026\/02\/image-1.png\" alt=\"\" class=\"wp-image-3688\" srcset=\"https:\/\/cz.ai-matters.eu\/wp-content\/uploads\/2026\/02\/image-1.png 645w, https:\/\/cz.ai-matters.eu\/wp-content\/uploads\/2026\/02\/image-1-271x300.png 271w, https:\/\/cz.ai-matters.eu\/wp-content\/uploads\/2026\/02\/image-1-600x664.png 600w\" sizes=\"(max-width: 645px) 100vw, 645px\" \/><figcaption class=\"wp-element-caption\">Autoencoder performance results<\/figcaption><\/figure>\n\n<h2 class=\"wp-block-heading\">Predictive maintenance of machinery and equipment using vibration diagnostics<\/h2>\n\n<p>The detection of short circuits between the windings of a PMS motor using vibration data processed by AI can be very reliable, even if only mechanical signals (vibrations generated by a faulty motor) are measured. The collected data sets are transformed into simple 2D images. 2D CNN provides very reliable results for PMSM fault detection (with more than 99% efficiency) from vibration signals without any prior knowledge of the system or intensive preprocessing of the input data. The raw vibration data from the microphone and accelerometers are processed directly by a simple convolutional neural network. The amount of measured data depends on the specific device, but usually large data sets are not required and it is not necessary to spend too much time testing, even if values for different motor operating conditions such as speed, torque, fault type and severity are used. One hundred test runs may be enough to train the neural network and verify the neural network structure. This collected data is converted into 2D images that can be easily analyzed in Keras.      <\/p>\n\n<figure class=\"wp-block-image aligncenter size-full\"><img decoding=\"async\" width=\"823\" height=\"709\" src=\"https:\/\/cz.ai-matters.eu\/wp-content\/uploads\/2026\/02\/image-2.png\" alt=\"\" class=\"wp-image-3690\" srcset=\"https:\/\/cz.ai-matters.eu\/wp-content\/uploads\/2026\/02\/image-2.png 823w, https:\/\/cz.ai-matters.eu\/wp-content\/uploads\/2026\/02\/image-2-300x258.png 300w, https:\/\/cz.ai-matters.eu\/wp-content\/uploads\/2026\/02\/image-2-768x662.png 768w, https:\/\/cz.ai-matters.eu\/wp-content\/uploads\/2026\/02\/image-2-600x517.png 600w\" sizes=\"(max-width: 823px) 100vw, 823px\" \/><figcaption class=\"wp-element-caption\">Upper part: analogue data from microphone and accelerometers, lower part: velocity profile (red line) and presence of error (blue line) of the PMSM.<\/figcaption><\/figure>\n\n<h2 class=\"wp-block-heading\">The process of cooperation<\/h2>\n\n<p>To discuss the possibility of cooperation, please contact us directly. The first step is an inquiry form. Request for quotation. This form is used to specify your expectations and requirements, including a brief description of the project, the number of GPU hours required, any support needed, and the required timeline. The collaboration is very similar to a standard commercial collaboration in that it does not involve any unnecessary delays or additional paperwork after payment.    <\/p>\n\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"331\" height=\"309\" src=\"https:\/\/cz.ai-matters.eu\/wp-content\/uploads\/2026\/02\/image-3.png\" alt=\"\" class=\"wp-image-3692\" srcset=\"https:\/\/cz.ai-matters.eu\/wp-content\/uploads\/2026\/02\/image-3.png 331w, https:\/\/cz.ai-matters.eu\/wp-content\/uploads\/2026\/02\/image-3-300x280.png 300w\" sizes=\"(max-width: 331px) 100vw, 331px\" \/><figcaption class=\"wp-element-caption\">Next steps &#8211; the collaboration process<\/figcaption><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>CEITEC Brno University of Technology is engaged in predictive maintenance of machines and equipment as part of its activities in the field of artificial intelligence and high-performance computing. Thanks to the model industrial factory (testbed) and the NVIDIA DGX A100 \/ DGX H100 HPC cluster, which is part of the TEF infrastructure, we can not [&hellip;]<\/p>\n","protected":false},"author":9,"featured_media":3712,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[178],"tags":[173],"class_list":["post-4799","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","tag-service_highlight"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Service Highlight: How data and AI-based systems create reliable solutions for predictive maintenance - AI Matters<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/cz.ai-matters.eu\/en\/service-highlight-how-data-and-ai-based-systems-create-reliable-solutions-for-predictive-maintenance\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Service Highlight: How data and AI-based systems create reliable solutions for predictive maintenance - AI Matters\" \/>\n<meta property=\"og:description\" content=\"CEITEC Brno University of Technology is engaged in predictive maintenance of machines and equipment as part of its activities in the field of artificial intelligence and high-performance computing. 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