Denis Mikhaylov


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Mikhaylov

First Name

Denis

ORCID

0000-0003-2427-8958

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2022 - 20245
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Publications 1 - 5 of 5
  • Towards the Future Generation of Railway Localization and Signaling Exploiting sub-meter RTK GNSS
    Item type: Conference Paper
    Amatetti, Carla; Polonelli, Tommaso; Masina, Enea; et al. (2022)
    2022 IEEE Sensors Applications Symposium (SAS)
    Internet of Things devices and smart sensors have become increasingly more pervasive in railway transportation applications, where they have the potential to significantly improve reliability, capacity, safety, and to reduce costs. In the ‘smart rail’ concept a key enabler is the ability to accurately localize trains with centimeter precision. This can be achieved using a combination of a high-precision GNSS-based module capable of achieving sub-meter accuracy and emerging radio and sensor technologies. This paper proposes a train tracking sensor node for in-field assessments fusing the absolute localization data from the GNSS and from local reference systems, such as Real Time Kinematics (RTK) with Inertial Measurement Unit (IMU) and Dead Reckoning (DRK). A complete wireless sensor node has been designed and evaluated in the field for functionality and power consumption. Within the sensor node, two different GNSS modules have been tested, with and without RTK and DRK, under different GNSS coverage conditions in various static and dynamic scenarios. We demonstrate that centimeter accuracy is achievable, with an accuracy of 2 ± 1 cm under static conditions and perfect satellite visibility, 4 ± 18 cm and 17 ± 40 cm under dynamic conditions in perfect and poor coverage conditions, respectively.
  • On-Sensor tinyML Event-Based Fault Detection Strategies on Wind Turbine Blades
    Item type: Conference Paper
    Mikhaylov, Denis; Polonelli, Tommaso; Magno, Michele (2024)
    2024 IEEE Sensors Applications Symposium (SAS)
    With the advent of wireless low-power Internet of Things (IoT) devices, Structural Health Monitoring (SHM), in which structures or machines are monitored using sensors, has become increasingly important to reduce costs and increase structure longevity. On-sensor machine learning represents a significant leap forward in addressing the challenge of managing the copious amounts of data these SHM systems generate. Novel sensors such as the ISM330IS Inertial Measurement Unit (IMU) from STMicroelectronics feature an onboard Intelligent Sensor Processing Unit (ISPU) enabling some of the required data processing to be computed onboard the sensor, reducing the need to run the main processor and ensuring only interesting data is collected, e.g., when a fault occurs. Using an existing state-of-the-art monitoring system as a baseline, this work investigates the power consumption, bandwidth requirements, and latency of potential data acquisition strategies using an event-based tiny Machine Learning (tinyML) model for fault detection in the context of wind turbine blades. The results demonstrate that replacing the current time-based data acquisition strategy of the existing system with an event-based strategy using the ISPU could reduce power consumption by up to 75% while decreasing latency between fault occurrence and alerting of the remote monitoring system to near-zero, with an always-on power of just 1.29 mW.
  • Advancing Freight Train Integrity Monitoring for Next-Generation Railway Signaling Using UWB Ranging
    Item type: Conference Paper
    Mikhaylov, Denis; Zheng, Fu; Mayer, Philipp; et al. (2023)
    2023 IEEE World Forum on Internet of Things: The Blue Planet: A Marriage of Sea and Space, WF-IoT 2023
    Reliable train integrity monitoring is crucial to enabling the implementation of state-of-the-art railway signaling and safety systems such as ETCS Level 3. However, train integrity monitoring presents a significant challenge for European freight trains, which currently consist of unpowered freight wagons and a locomotive, with only mechanical and pneumatic connections between them. This paper presents a study and experimental evaluation under static and dynamic conditions of the feasibility of using Ultra-WideBand (UWB) ranging to monitor train integrity with a network of low-power and cost-optimized Internet of Things (IoT) devices. Static tests are performed on freight wagons to investigate the optimal UWB sensor positioning and the impact of Non-Line-of-Sight (NLoS) conditions on ranging accuracy. These tests show that positioning has a significant impact on the proportion of ranging attempts that are successful, with an error rate of less than 4% in the best case and over 90% in the worst case. The standard deviation of the UWB ranging is 0.03m in the best case in LoS conditions and 1.26m in the worst case. The dynamic tests show that coupling and uncoupling can be successfully detected using a combination of UWB and an Inertial Measurement Unit (IMU), and that a network of self-contained UWB sensor nodes is well-suited to monitoring train integrity compared to other approaches such as Global Navigation Satellite Systems (GNSS) that rely on external infrastructure.
  • Tiny On-Device Structural Health Monitoring for Wind Turbines Using MEMS Pressure Sensors
    Item type: Conference Paper
    von Däniken, Elias; Mikhaylov, Denis; Moallemi, Amirhossein; et al. (2024)
    2024 IEEE Sensors Applications Symposium (SAS)
    Wind turbines are extensively used as a renewable source of energy. These turbines are exposed to turbulence and highly variable weather conditions such as wind, rain, and ice, which can cause significant damage and degraded power generation performance if not detected in time. The emergence of the Industry 4.0 paradigm enables a reduction in maintenance costs and increased efficiency. Recent research has demonstrated the possibility of monitoring the aerodynamic performance of the wind turbine directly from the blades with today’s state-of-the-art wireless monitoring systems. However, these need external support to process and analyze the collected data. This paper proposes an effective tiny machine learning approach to monitor the blade structural integrity directly onboard the sensor node, requiring only 3 kB of RAM and 35 kB of Flash running on a low-power ARM Cortex-M4F at 48MHz. We experimentally evaluate the proposed model with a dataset acquired in a wind tunnel with 40 MEMS barometers placed around the airfoil in 6 different damage conditions. The XGBoost classification model is purely based and trained on the blade aerodynamics, featuring a classification accuracy above 83% over 6 labeled classes of increasing damage. With a total execution time of 579 ms while it only consumes 9 mJ, it enables real-time analysis for a sustainable battery-based monitoring system.
  • Toward the Future Generation of Railway Localization Exploiting RTK and GNSS
    Item type: Journal Article
    Mikhaylov, Denis; Amatetti, Carla; Polonelli, Tommaso; et al. (2023)
    IEEE Transactions on Instrumentation and Measurement
    Smart sensors have become pervasive in railway transportation applications, particularly in Europe where digital technologies are increasingly being applied to the railway signaling field. In the future, safety-critical track-side train detection equipment, such as track circuits and axle counters, will be eliminated in favor of an accurate position estimate supplied by the train. However, the best approach to calculate an accurate position estimate remains an open research question, especially due to the high availability and reliability required. This article describes two static experiments performed with a global navigation satellite system (GNSS) module, which demonstrate that the real-world accuracy achievable with GNSS and real time kinematic (RTK) alone is not sufficient for safety critical applications, meaning further complementary sensors are required. Furthermore, a custom sensor node containing a GNSS module with RTK and an inertial measurement unit (IMU) has been used to acquire several data sets from an operating passenger train during dynamic tests on a railway line between Formigine and Modena, in Emilia-Romagna, Italy. These labeled GNSS and IMU data have been made freely available to the scientific community. The positioning accuracy of the GNSS and RTK measurements is evaluated, providing an in-depth study of the localization error and satellite coverage on the entire route. We demonstrate, with experimental evaluation, that centimeter accuracy (1.9 +/- 0.8) cm) is achievable under favorable static conditions, while accuracy can deteriorate to 8 m with RTK in urban scenarios with many reflections and poor sky view, worse than with GNSS alone. Under controlled conditions we show that shielding the GNSS receiver without RTK with a grounded metal plate causes a reduction in accuracy from 0.8 +/- 0.04 to 3.7 +/- 0.55 m in the least and most shielded case respectively. Our dynamic tests on a train show that although at least meter-level accuracy (1.08 +/- 1.3 m) is achievable with GNSS and RTK alone under dynamic conditions, a sensor fusion approach is necessary to accurately localize trains when GNSS conditions are poor or GNSS is unavailable.
Publications 1 - 5 of 5