Electromechanical Systems Research Articles

Regulating the electrical performance of contact-separation mode triboelectric nanogenerators based on double-sided groove textures

The contact-separation mode triboelectric nanogenerators (CS-TENGs) is a kind of micro/nano electromechanical power system based on the contact electrification and electrostatic induction. With the expansion of application field of the CS-TENG, regulating the electrical performance becomes essential. In this paper, simple and effective methods for the CS-TENG output regulation based on the double-sided groove textures are proposed. To do this, groove textures on both Cu contact electrode and PDMS dielectric layer are fabricated, and influences and mechanisms of the alignment angle (the angle between the direction of groove textures on the Cu electrode and that on the PDMS dielectric layer), matching condition (textures sidewall contact and non-contact) and applied pressure on the CS-TENG output are explored through output tests, contact and electrostatic simulations. It is shown that altering the alignment angle, matching condition and applied pressure of groove texture on the Cu contact electrode and PDMS dielectric layer, could simply regulate the output of CS-TENG, where changing the alignment angle is the most effective. Under low and high applied pressures, the variations of the output voltage with alignment angle from 0˚–90˚ reach to 343.8% and 297.2%, respectively, and CS-TENG at 0˚ angle outputs the maximum voltage under both pressures, while the minimum voltages under the two pressures are exhibited from different angles. The change of contact area induced by the longitudinal deformation and transverse expansion of the groove textures is the main reason for the output regulations.

Non-linear dynamics of vibration transport machine as an electromechanical system

Non-linear dynamics of vibration transport machine as an electromechanical system

A real-time field bus architecture for multi-smart-motor servo system

The multi-motor servo system (MMSS) is an electro-mechanical system widely used in various fields, including electric vehicles, robotics, and industrial machinery. Depending on the application, the number of motors in the system can range from several dozens to tens of thousands, which imposes additional communication demands. Thus, ensuring synchronization and control precision of the system requires addressing the challenge of guaranteeing the performance and reliability of communication among motors in the MMSS. In this paper, we design a smart servo motor (SSM) to upgrade the system to the multi-smart-motor servo system (MSMSS) based on a distributed real-time field bus architecture, namely, Multi-Motor Bus (MMB) architecture. The proposed MMB architecture is lightweight and stable, providing real-time support for Control Area Network connections to a central user computer and inter-integrated circuit connections to SSM units. This MMB architecture facilitates the synchronization of command transmission across SSMs and ensures the consistency of motors in the MSMSS. Additionally, a serial experiments to examine 3 key system performance and reliability characteristics are conducted, including command transmission time, transmission jitters, and rotation consistency. The analysis of these characteristics demonstrates the system’s potential and feasibility to be applicable in industry.

Stiffness Modeling of Tooth Interior Fatigue and Tooth Flank Fracture and Their Fault Characteristics in Electromechanical Coupling Systems

Tooth flank fracture (TFF) and tooth interior fatigue fracture (TIFF) usually occur on case-harden gears in electromechanical coupling systems, both starting from the core caused by metal fatigue. The cracks propagate rapidly compared with the total life of a gear, so that it is necessary to detect and identify the faults in time when they take place in order to avoid danger. In this paper, the time-varying mesh stiffness model of two types of faults is established by the potential energy method and validating by the finite element method. Based on the stiffness model, the influence of faults on the electromechanical system and their fault characteristics are analyzed, which provides a theoretical basis for the health detection of electromechanical coupling systems. The results indicate that in the early stage of the three faults, i.e., tooth root fracture, tooth interior fatigue fracture, and tooth flank fracture, it is hard to discriminate the faults. But after the second stage, the faults can be distinguished by the vibration amplitude, frequency band components, and phase diagrams. The TFF can be discerned into different stages by frequency domains and phase diagrams. When the fracture occurs completely, three faults can be easily distinguished by the time domain and phase diagram. In the frequency domain, the TRF can also be distinguished from the other two faults by referring to the sideband component near the meshing frequency.

Microfabrication of piezoelectric MEMS based on thick LiNbO3 single-crystal films

Microfabrication procedure of piezoelectric micro electro-mechanical systems based on 5 μm thick LiNbO3 films on SiO2/Si substrate at wafer scale including deep dry etching of thick LiNbO3 films by implementing pulsed mode of Ar/SF6 gas was developed. In particular, two (YXlt)/128°/90°LiNbO3-Si cantilevers with tip mass were fabricated and characterized in terms of resonance frequency (511 and 817 Hz), actuation and acceleration sensing capabilities. The quality factor of 89.5 and the electromechanical coupling of 4.8% were estimated from measured frequency dependency of electrical impedance, fitted by using Butterworth–Van Dyke model. The fabricated piezoelectric micro-electro-mechanical systems have demonstrated highly linear displacement with good sensitivity (5.28 ± 0.02 μm V−1) as a function of applied voltage and high sensitivity to vibrations of 667 mV g−1 indicating a suitability of the structure for actuation purposes and for acceleration or frequency sensing with high precision, respectively.

MEMS strapdown inertial attitude measurement system using rotational modulation technology.

Attitude determination involves the integration of methodologies and systems for estimating the time varying attitude of moving objects. Strapdown Inertial Attitude Measurement System (SIAMS) is among the most widely used navigation systems. The development of cost effective Micro Electro Mechanic System (MEMS) based inertial sensors has made attitude measurement system more affordable. However, MEMS sensors suffer from various errors that have to be calibrated and compensated to get acceptable attitude results. Given the auto-compensation of inertial sensor bias in rotation error modulation, the objective of this paper is to develop a MEMS-based rotary SIAMS, in which the significant sensor bias is automatically compensated by rotating the IMU, to offer comparable performance with respect to a tactical-grade Inertial Measurement Unit (IMU). With the analysis of the relationship between the MEMS error and misalignment, a MEMS calibration model is derived, and a combined calibration method of multi position rotation is applied to estimate the deterministic sensor errors such as bias, scale factor, and misalignment. Simulation and experiment results indicate that the proposed method can further modulate and compensate the MEMS errors, thereby improving the MEMS attitude accuracy.

Heat-sink-free solar-driven thermoelectric device for micro energy scavenging

Harvesting energy from nature, such as mechanical energy, thermal energy, and biomass energy, has become an effective way to supply power for self-driven sensing and Internet of Things (IoT) applications. Among that, solar-driven thermoelectric (STE) device takes advantage of all-solid-state energy conversion, such as no noise, easy access, and free of wear, exhibiting great potential. Here, we propose a solar-driven thermoelectric device for harvesting solar energy, based on Micro Electromechanical System (MEMS) technology. Specifically, the optimization of solar absorptances of structural layers significantly improved the photo-thermo-electric conversion performance. Furthermore, the electrical output strongly depends on temperature difference dominated by the absorptance difference. Therefore, the reported device can be free of traditional heat sink, which greatly simplifies its structure and expands the working conditions, especially in microdevices. Particularly, a continuous output voltage of 0.34 V/cm2 is achieved with concentrated solar radiation, demonstrating its potential under no heat sink conditions. Besides, a fast response (4.2 ms under 660 nm laser) is achieved benefitting from the micron-size solar absorber, showing its significance in sensing applications. Finally, the proposed STE device is utilized to harvest outdoor solar energy, displaying promising potential in solar energy harvesting devices and providing an insightful perspective for thermoelectric applications.

Development of a Simultaneous Quantification Method for Multiple Modes of Nitrogen in Leaf Models Using Near-Infrared Spectroscopic Measurement.

By focusing our attention on nitrogen components in plants, which are important for cultivation management in data-driven agriculture, we developed a simple, rapid, non-chemical and simultaneous quantification method for proteinic and nitrate nitrogen in a leaf model based on near-infrared (NIR) spectroscopic information obtained using a compact Fourier Transform NIR (FT-NIR) spectrometer. The NIR spectra of wet leaf models impregnated with a protein-nitric acid mixed solution and a dry leaf model obtained by drying filter paper were acquired. For spectral acquisition, a compact MEMS (Micro Electro Mechanical Systems) FT-NIR spectrometer equipped with a diffuse reflectance probe accessory was used. Partial least square regression analysis was performed using the spectral information of the extracted absorption bands based on the determination coefficients between the spectral absorption intensities and the contents of the two-dimensional spectral analysis between NIR and mid-infrared spectral information. Proteinic nitrogen content in the dry leaf model was well predicted using the MEMS FT-NIR spectroscopic method. Additionally, nitrate nitrogen in the dry leaf model was also determined by the provided method, but the necessity of adding the data for a wider range of nitric acid concentrations was experimentally indicated for the prediction of nitrate nitrogen content in the wet leaf model. Consequently, these results experimentally suggest the possibility of the application of the compact MEMS FT-NIR for obtaining the bioinformation of crops at agricultural on-sites.

An Innovative Sensor Integrated with GNSS and Accelerometer for Bridge Health Monitoring

This paper presents an innovative integrated sensor that combines GNSS and a low-cost accelerometer for bridge health monitoring. GNSS and accelerometers are both significant and effective sensors for structural monitoring, but they each have limitations. The sampling rate of GNSS data is relatively low, making it challenging to capture high-frequency vibrations, while accelerometers struggle with low-frequency signals and are susceptible to environmental changes. Additionally, GNSS receivers and accelerometers are often installed separately, leading to challenges in data fusion processing due to differing temporal and geospatial references. The proposed integrated sensor addresses these issues by synchronizing GNSS and an accelerometer’s time and geospatial coordinate reference. This allows for a more accurate and reliable deformation and vibration measurement for bridge monitoring. The performance of the new sensor was assessed using a high-quality/cost Leica GM30 GNSS receiver and a Sherborne A545 accelerometer. Experiments conducted on the Wilford suspension bridge demonstrate the effectiveness of this innovative integrated sensor in measuring deformation and vibration for bridge health monitoring. The limitation of the low-cost MEMS (Micro Electromechanical System) accelerometer for the weak motion frequency detection is also pointed out.

Adaptive control of BLDC driven robot manipulators in task space

AbstractIn this study, task space tracking control of robot manipulators driven by brushless DC (BLDC) motors is considered. Dynamics of actuators are taken into account and the entire electromechanical system (i.e. kinematic, dynamic, and electrical models) is assumed to include parametric/structured uncertainties. A novel adaptive controller is designed and the stability of the closed loop system is ensured via novel Lyapunov type tools. To demonstrate performance and applicability of the proposed method, a simulation study is conducted using the model of a two degree of freedom, planar robotic manipulator driven by BLDC motors.

Simulation of screw size effect on performance of electro-mechanical braking system

Electro-mechanical brakes (EMB) are promising alternatives to conventional braking systems, as they are eco-friendly and have lower maintenance requirements. Currently, researchers and automotive manufacturers are interested in developing the performance of EMB systems for implementation in commercial vehicles. Current studies are either conducted experimentally and/ or modeled using mathematical based simulation models using different vehicles kinematic and dynamic models. These models are time-consuming, and expensive in case of experimental implementation. This paper presents a simple and easy-to-construct simulation model using MATLAB Simulink Graphical user interface (GUI) without using conventional mathematical modelling techniques. The presented simulation model studies the effect of the lead screw size on performance of EMB in terms of the generated clamping force and system response time. The accuracy and reliability of the proposed model were statistically verified using ANOVA. In addition, its performance was verified by comparing its results with the same input data used in previously validated experimental and mathematical-based simulation models from the literature review. The proposed model can be used to improve the performance of EMB systems and facilitate comparisons among different EMB designs.

Towards advanced piezoelectric metamaterial design via combined topology and shape optimization

Metamaterials open up a spectrum of artificially engineered properties otherwise unreachable in conventional bulk materials. For electromechanical energy conversion systems, lightweight materials with high hydrostatic piezoelectric coupling coefficients and negative Poisson’s ratio can be obtained. Thus, in this contribution, we explore the possibilities of piezoelectric metamaterials design by employing structural optimization. More specifically, we apply a sequential framework of topology and shape optimization to design piezoelectric metamaterials with negative Poisson’s ratio for electromechanical energy conversion under uniform pressure. Topology optimization is employed to generate the initial layout, whereas shape optimization fine tunes the design and improves durability and manufacturability of the structures with the help of a curvature constraint. An embedding domain discretization (EDD) method with adaptive domain and shape refinement is utilized for an efficient and accurate computation of the state problem in the shape optimization stage. Multiple case studies are conducted to determine the importance of desired stiffness characteristics, symmetry conditions and objective formulations on the design of piezoelectric metamaterials. Results show that the obtained designs are highly dependent on the desired stiffness characteristics. Moreover, the addition of the EDD-based shape optimization step introduces significant changes to the designs, confirming the usability of the sequential framework.

Safety protocols in electro-mechanical installations: Understanding the key methodologies for ensuring human and system safety

This study systematically examines the safety protocols in electro-mechanical systems, focusing on methodologies, trends, and innovations that ensure human and system safety. The primary objective is to bridge the gap between current practices and the evolving demands of these systems, considering technological advancements and regulatory compliance. A systematic literature review methodology was employed, utilizing databases like IEEE Xplore, ScienceDirect, and Google Scholar. The search strategy involved keywords related to safety protocols and electro-mechanical systems, adhering to strict inclusion and exclusion criteria to ensure a focused analysis. Key findings reveal the growing importance of integrating advanced sensing technologies, artificial intelligence, and human factors into safety management. The study highlights the shift from reactive to proactive safety measures, emphasizing the need for continuous monitoring and predictive maintenance. Collaborative approaches involving various stakeholders, including industry and academia, are identified as crucial for enhancing safety. The future landscape of safety in electro-mechanical systems is expected to be significantly influenced by emerging technologies, leading to more sophisticated safety protocols. The study recommends continuous updating of safety protocols in line with technological advancements and changing regulatory landscapes. It also suggests interdisciplinary research and industry-academia collaboration to address complex safety challenges. Finally, the evolution of safety protocols in electro-mechanical systems is marked by technological advancements and a deeper understanding of human-system interaction. The study underscores the necessity for continuous improvement and adaptation of safety protocols, ensuring their effectiveness in safeguarding human lives and system sustainability.

Adaptive containment control of heterogeneous high‐order fully actuated multi‐agent systems

AbstractIn this article, the distributed containment control schemes are presented for a class of nonlinear heterogeneous high‐order fully actuated multi‐agent systems. First, for each follower, the reference trajectory generator is constructed by using the output information of the neighbor agents. Then, the distributed containment controllers are designed for the system with known nonlinear system function matrices and control gain matrices. Further, since the existence of modeling errors and other uncertain factors, the obtained system model is often not accurate enough, that is, the system function matrix and control gain matrix are actually unknown nonlinear matrices. To tackle these obstacles, we employ the radial basis function neural network method to approximate the unknown nonlinear system function matrices and skillfully construct the controllers combined with the adaptive technology to eliminate the effects of the unknown control gain matrices. The developed control strategies can guarantee that the containment control can be realized and the containment errors can be adjusted to arbitrary precision. In the end, the simulation results on the electromechanical system and robotic system demonstrate the availability and effectiveness of the provided schemes.

A novel electromechanical system for guide vane adjustment in a small turbine test rig

A novel electromechanical system for guide vane adjustment in a small turbine test rig

Retracted: Design and Application of Electromechanical Control System Based on Computer Fault Tolerance Technology

Retracted: Design and Application of Electromechanical Control System Based on Computer Fault Tolerance Technology

Model order reduction and stochastic averaging for the analysis and design of micro-electro-mechanical systems

Electro-mechanical systems are key elements in engineering. They are designed to convert electrical signals and power into mechanical motion and vice-versa. As the number of networked systems grows, the corresponding mathematical models become more and more complex, and novel sophisticated techniques for their analysis and design are required. We present a novel methodology for the analysis and design of electro-mechanical systems subject to random external inputs. The method is based on the joint application of a model order reduction technique, by which the original electro-mechanical variables are projected onto a lower dimensional space, and of a stochastic averaging technique, which allows the determination of the stationary probability distribution of the system mechanical energy. The probability distribution can be exploited to assess the system performance and for system optimization and design. As examples of application, we apply the method to power factor correction for the optimization of a vibration energy harvester, and to analyse a system composed by two coupled electro-mechanical resonators for sensing applications.

Machine Fault Diagnosis through Vibration Analysis: Continuous Wavelet Transform with Complex Morlet Wavelet and Time–Frequency RGB Image Recognition via Convolutional Neural Network

In pursuit of advancing fault diagnosis in electromechanical systems, this research focusses on vibration analysis through innovative techniques. The study unfolds in a structured manner, beginning with an introduction that situates the research question in a broader context, emphasising the critical role of fault diagnosis. Subsequently, the methods section offers a concise summary of the primary techniques employed, highlighting the utilisation of short-time Fourier transform (STFT) and continuous wavelet transform (CWT) for extracting time–frequency components from the signal. The results section succinctly summarises the main findings of the article, showcasing the results of features extraction by CWT and subsequently utilising a convolutional neural network (CNN) for fault diagnosis. The proposed method, named CWTx6-CNN, was compared with the STFTx6-CNN method of the previous stage of the investigation. Visual insights into the time–frequency characteristics of the inertial measurement unit (IMU) data are presented for various operational classes, offering a clear representation of fault-related features. Finally, the conclusion section underscores the advantages of the suggested method, particularly the concentration of single-frequency components for enhanced fault representation. The research demonstrates commendable classification performance, highlighting the efficiency of the suggested approach in real-time scenarios of fault analysis in less than 50 ms. Calculation by CWT with a complex Morlet wavelet of six time–frequency images and combining them into a single colour image took less than 35 ms. In this study, interpretability techniques have been employed to address the imperative need for transparency in intricate neural network models, particularly in the context of the case presented. Notably, techniques such as Grad-CAM (gradient-weighted class activation mapping), occlusion, and LIME (locally interpretable model-agnostic explanation) have proven instrumental in elucidating the inner workings of the model. Through a comparative analysis of the proposed CWTx6-CNN method and the reference STFTx6-CNN method, the application of interpretability techniques, including Grad-CAM, occlusion, and LIME, has played a pivotal role in revealing the distinctive spectral representations of these methodologies.

Measuring power consumption efficiency of an electromechanical system within a long-term period by fuzzy DEA and TOPSIS for sustainability

Measuring power consumption efficiency of an electromechanical system within a long-term period by fuzzy DEA and TOPSIS for sustainability

A generative adversarial networks based methodology for imbalanced multidimensional time-series augmentation of complex electromechanical systems

Multidimensional monitoring time-series of complex electromechanical systems (CESs) plays a foundational role in data-based state management, maintenance, and performance adjustment. However, it is still a challenging work to extract valuable and complete information due to the imbalanced data. To address this issue, a methodology called GAN4MTS (Generative Adversarial Networks for Multidimensional Time-Series) that generates synthetic data closely mimicking the characteristics of real data was proposed, thus directly tackling the problem of data imbalance. First, the uniqueness of multidimensional time series of CESs was analyzed to identify the requirements for data augmentation and to define the problem formulations. Second, the architecture and loss functions of GAN4MTS model were designed based on generative adversarial networks and three specific constraints. Finally, the effectiveness of the proposed work was validated through comparative analysis. Furthermore, the intrinsic mechanisms of data augmentation in enhancing the model capabilities were discussed. The proposed methodology serves as a comprehensive technical solution for data augmentation, enabling the generation of high-quality synthetic data that adheres to the constraints of multidimensional time series in CESs. Additionally, as an open architecture model, this work provides novel methods for time-series data augmentation, addressing the issues of low accuracy and limited model generalization in state classification, identification, and prediction tasks for CESs caused by the presence of highly imbalanced data.