Bengt Oelmann

The integration of ortho-planar springs in electromagnetic vibration energy harvesters offers significant advantages and serves asthe main focus of the current study. For this type of integration, the spring structure plays a significant role during the design processof the electromagnetic energy harvester due to its effect on the harvester’s frequency response and thus the harvester’s performance.In this paper, three different ortho-planar spring topologies are under investigation. These include rectangular, radial, and angledtopologies. We utilize a fully automated optimization procedure with the help of an optimization algorithm implemented in Pythonto find the most promising configurations for each topology. Subsequently, these design candidates have been fabricated andassembled into electromagnetic vibration harvesters for experimental evaluation. The results demonstrate that the spring topologysignificantly affects the nonlinear stiffness of the harvesters. Rectangular topologies exhibit the most nonlinear behavior, whileangled topologies show the most linear behavior. Differences in linear and nonlinear stiffness within each topology group alsoinfluence the harvester’s overall frequency response. The peak average output power at 0.8g up-sweep input acceleration is foundto be similar across all topologies, with values of 4.76 ± 0.68mW, 4.89 ± 0.51mW, and 5.1 ± 0.56mW for rectangular, radial, andangled configurations, respectively. This equivalence indicates that while the choice of optimized configuration has a limited effecton the peak power of the harvesters, it remains critical to ensuring system reliability and performance predictability. Specifically, thehigher data dispersion observed in the rectangular layouts reveals a heightened sensitivity to geometric deviations compared to theradial and angled configurations. In addition, the choice of spring topology heavily impacts the frequency response, performance,and stability of electromagnetic energy harvesters. Furthermore, the fabrication-induced tolerances also play a crucial role in infinal device characterization.

Conveyor belts are crucial in mining operations by enabling the continuous and efficient movement of bulk materials over long distances, which directly impacts productivity. While detecting anomalies in specific conveyor belt components has been widely studied, identifying the root causes of these failures, such as changing production conditions and operator errors, remains critical. Continuous monitoring of mining conveyor belt work cycles is still at an early stage and requires robust solutions. Recently, an anomaly detection method for duty cycle operations of a mining conveyor belt has been proposed. Based on its limited performance and unevaluated long-term proper operation, this study proposes two novel methods for classifying normal and abnormal duty cycles. The proposed approaches are pattern recognition systems that make use of threshold-based duty-cycle detection mechanisms, manually extracted features, pattern-matching, and supervised tiny machine learning models. The explored low-computational models include decision tree, random forest, extra trees, extreme gradient boosting, Gaussian naive Bayes, and multi-layer perceptron. A comprehensive evaluation of the former and proposed approaches is carried out on two datasets. Both proposed methods outperform the former method in anomaly detection, with the best-performing approach being dataset-dependent. The heuristic rule-based approach achieves the highest F1-score in the same dataset used for algorithm training, with 97.3% for normal cycles and 80.2% for abnormal cycles. The ML-based approach performs better on a dataset including the effects of machine aging, with an F1-score scoring 91.3% for normal cycles and 67.9% for abnormal cycles. Implemented on two low-power microcontrollers, the methods demonstrate efficient, real-time operation with energy consumption of 13.3 and 20.6 J during inference. These results offer valuable insights for detecting mechanical failure sources, supporting targeted preventive maintenance, and optimizing production cycles.

Acoustic Emission (AE)-based fault diagnosis in Structural Health Monitoring (SHM) systems faces challenges of data scarcity and model overfitting due to the complexity of AE data acquisition and the high cost of labeling. To address these issues, this study systematically explores various data augmentation techniques for AE signal processing and evaluates their impact on model robustness and accuracy. Furthermore, given the complexity of traditional machine learning (ML) models and their deployment challenges on resource-constrained embedded devices, we investigate lightweight ML algorithms and propose a Tiny Machine Learning (TinyML)-based fault diagnosis approach. Experimental validation on a carbon fiber panel fault diagnosis case demonstrates that the proposed method significantly improves classification performance under data scarce conditions while enabling real-time fault diagnosis on embedded systems. These findings underscore the potential of integrating data augmentation, lightweight ML algorithms, and TinyML to enhance both diagnostic accuracy and real-time performance in SHM applications.

Precision livestock farming leverages cutting-edge technologies and data-driven solutions to enhance the efficiency of livestock production, its associated management and its welfare. Continuous monitoring of the masticatory sound of cattle allows the estimation of dry-matter intake, classification of jaw movements, and recognition of grazing and rumination bouts. Over the past two decades, algorithms for analyzing feeding sounds have seen improvements in performance and computational requirements. Nevertheless, in some cases, these algorithms have been implemented on resource-constrained electronic devices, limiting their functionality to one specific task: either classifying jaw movements or recognizing feeding activities (such as grazing and rumination). In this work, we present an acoustic monitoring system that comprehensively analyzes grazing cattle’s feeding behavior at multiple scales. This embedded system classifies different types of jaw movements, identifies feeding activities, and provides predictor variables for estimating dry-matter intake. Results are transmitted remotely to a base station using long-range communication. Two variants of the system have been deployed on a Raspberry Pi Pico board, based on a low-power ARM Cortex-M0+ microcontroller. Both firmware versions make use of direct access memory, sleep mode, and clock-gating techniques to minimize energy consumption. In laboratory experiments, the first deployment consumes 20.1 mW and achieves an F1-score of 87.3% for the classification of jaw movements and 87.0% for feeding activities. The second deployment consumes 19.1 mW and reaches an F1-score of 84.1% for jaw movements and 83.5% for feeding activities. The modular design of the proposed embedded monitoring system facilitates integration with energy-harvesting power sources for autonomous operation in field conditions.

The feasibility of energy harvesting as a viable alternative for powering low-energy electronics has been demonstrated through advancements in transduction mechanisms. Energy harvesters incorporating counterweights have gained attention in rotational energy harvesting to develop single-anchored devices with flexible placement and easy installation. In this work, a three-phase variable reluctance energy harvester (VREH) with low torque ripple is combined with a counterweight to facilitate a single-anchored design, specifically targeting low rotational speed applications. The energy harvester is integrated with a low-power sensor system to enable energy-neutral operation. We present the design, implementation, and evaluation of an on-rotor RPM sensor system powered by the single-anchored three-phase VREH. Experimental evaluations on a laboratory test bench demonstrate the system performance under varying conditions, with the ability to supply the sensor system at low speeds achieving, for example, a 3.5 Hz sample rate at a low speed of 3 rpm. Evaluations of the system illustrate that pendulum effects induced by the interaction of the cogging torque and the gravitational torque improve the output power of the harvester under low-speed conditions. This promises for the proposed design to be suitable to power wireless sensors for industrial condition monitoring, providing a flexible solution for energy-neutral sensor systems with reduced installation complexity.

For the field of structural health monitoring (SHM), acoustic emission (AE) technology is important as a damage identification technique that does not cause secondary damage to concrete. Nowadays, applications of non-destructive concrete damage identification are mostly limited to commercial software or identification algorithms running on desktop computers. It has so far not been deployed in low-power embedded devices. In this study, a lightweight convolutional neural network (CNN) model for online non-destructive damage type recognition of concrete materials is presented and deployed on a resource-constrained microcontroller unit as a tiny machine learning (TinyML) application. The CNN model uses raw acoustic emission signals as input and damage recognition types as output. 15,000 acoustic emission signals are used as data sets divided into training, validation, and test sets in the ratio of 8:1:1. The experimental results show that an accuracy of 99.6% is achieved on the nRF52840 microcontroller (ARM Cortex M4) with only 166.822 ms and 0.555mJ for a single inference using only 20K parameters and 30.5KB model size. This work demonstrates the effectiveness and feasibility of the proposed model, which achieves a trade-off between high classification accuracy and deployability on resource-constrained MCUs. Consequently, it provides strong support for online continuous non-destructive structural health monitoring.

Investigating the coil–magnet structure plays a significant role in the design process of the electromagnetic energy harvester due to the effect on the harvester’s performance. In this paper, the performance of four different electromagnetic vibration energy harvesters with cylindrical shapes constrained in the same volume were under investigation. The utilized structures are (i) two opposite polarized magnets spaced by a mild steel; (ii) a Halbach array with three magnets and one coil; (iii) a Halbach array with five magnets and one coil; and (iv) a Halbach array with five magnets and three coils. We utilized a completely automatic optimization procedure with the help of an optimization algorithm implemented in Python, supported by simulations in ANSYS Maxwell and MATLAB Simulink to obtain the maximum output power for each configuration. The simulation results show that the Halbach array with three magnets and one coil is the best for configurations with the Halbach array. Additionally, among all configurations, the harvester with two opposing magnets provides the highest output power and volume power density, while the Halbach array with three magnets and one coil provides the highest mass power density. The paper also demonstrates limitations of using the electromagnetic coupling coefficient as a metric for harvester optimization, if the ultimate goal is maximization of output power.

Low-power energy harvesting has been demonstrated as a feasible alternative for the power supply of next-generation smart sensors and IoT end devices. In many cases, the output of kinetic energy harvesters is an alternating current (AC) requiring rectification in order to supply the electronic load. The rectifier design and selection can have a considerable influence on the energy harvesting system performance in terms of extracted output power and conversion losses. This paper presents a quantitative comparison of three passive rectifiers in a low-power, low-voltage electromagnetic energy harvesting sub-system, namely the full-wave bridge rectifier (FWR), the voltage doubler (VD), and the negative voltage converter rectifier (NVC). Based on a variable reluctance energy harvesting system, we investigate each of the rectifiers with respect to their performance and their effect on the overall energy extraction. We conduct experiments under the conditions of a low-speed rotational energy harvesting application with rotational speeds of 5 rpm to 20 rpm, and verify the experiments in an end-to-end energy harvesting evaluation. Two performance metrics—power conversion efficiency (PCE) and power extraction efficiency (PEE)—are obtained from the measurements to evaluate the performance of the system implementation adopting each of the rectifiers. The results show that the FWR with PEEs of 20% at 5 rpm to 40% at 20 rpm has a low performance in comparison to the VD (40–60%) and NVC (20–70%) rectifiers. The VD-based interface circuit demonstrates the best performance under low rotational speeds, whereas the NVC outperforms the VD at higher speeds (>18 rpm). Finally, the end-to-end system evaluation is conducted with a self-powered rpm sensing system, which demonstrates an improved performance with the VD rectifier implementation reaching the system’s maximum sampling rate (40 Hz) at a rotational speed of approximately 15.5 rpm.

Condition monitoring devices in hydraulic systems that use batteries or require wired infrastructure have drawbacks that affect their installation, maintenance costs, and deployment flexibility. Energy harvesting technologies can serve as an alternative power supply for system loads, eliminating batteries and wiring requirements. Despite the interest in pressure fluctuation energy harvesters, few studies consider end-to-end implementations, especially for cases with low-amplitude pressure fluctuations. This generates a research gap regarding the practical amount of energy available to the load under these conditions, as well as interface circuit requirements and techniques for efficient energy conversion. In this paper, we present a self-powered sensor that integrates an energy harvester and a wireless sensing system. The energy harvester converts pressure fluctuations in hydraulic systems into electrical energy using an acoustic resonator, a piezoelectric stack, and an interface circuit. The prototype wireless sensor consists of an industrial pressure sensor and a low-power Bluetooth System-on-chip that samples and wirelessly transmits pressure data. We present a subsystem analysis and a full system implementation that considers hydraulic systems with pressure fluctuation amplitudes of less than 1 bar and frequencies of less than 300 Hz. The study examines the frequency response of the energy harvester, the performance of the interface circuit, and the advantages of using an active power improvement unit adapted for piezoelectric stacks. We show that the interface circuit used improves the performance of the energy harvester compared to previous similar studies, showing more power generation compared to the standard interface. Experimental measurements show that the self-powered sensor system can start up by harvesting energy from pressure fluctuations with amplitudes starting at 0.2 bar at 200 Hz. It can also sample and transmit sensor data at a rate of 100 Hz at 0.7 bar at 200 Hz. The system is implemented with off-the-shelf circuits.

Models of photovoltaic devices are an important tool for the estimation of their I-V characteristics. These characteristics, in turn, can be used to optimize production, compare devices, or predict the output power under different illumination conditions. Equivalent circuit models are the most common model types utilized. Although these models and the estimation of their parameters are thoroughly investigated, little is known about their performance under indoor illumination conditions. This, however, is essential for applications where photovoltaic devices are used indoors, such as for PV-powered sensors, wearables or Internet of Things devices. In this paper, a comprehensive and quantitative study of parameter estimation methods for the two-diode model is conducted, focusing particularly on the performance at indoor illumination levels. We reviewed and implemented a set of six common parameter estimation methods, and evaluate the performance of the estimated parameters on a typical photovoltaic module utilized in indoor scenarios. The results of this investigation demonstrate that there is a large performance variation between different parameter estimation methods, and that many methods have difficulties to estimate accurate parameters at low illumination conditions. Moreover, the majority of methods result in physically infeasible parameters, at least under some of the evaluated conditions. When applying physically motivated parameter scaling methods to these parameters, large estimation errors are observed, which limits the model’s applicability for power estimation purposes.