Machines, Volume 11, Issue 10 (October 2023) – 61 articles

Nowadays, railway track monitoring strategies are based on the use of railway inspection vehicles and wayside dynamic monitoring systems. The latter sometimes requires traffic disruption, as well as higher time and cost-consumption activities, and the use of dedicated inspection vehicles is less economical and efficient as the use of in-service vehicles. Furthermore, the use of non-automated algorithms faces challenges when it comes to early damage detection in railway infrastructure, considering operational, environmental, and big data aspects, and may lead to false alarms. To overcome these challenges, the application of artificial intelligence (AI) algorithms for early detection of track defects using accelerations, measured by dynamic monitoring systems in in-service railway vehicles is attracting the attention of railway managers. In this paper, an AI-based methodology based on axle box acceleration signals is applied for the early detection of distributed damage to track in terms of the longitudinal level and lateral alignment. The methodology relies on feature extraction using an autoregressive model, data normalization using principal component analysis, data fusion and feature discrimination using Mahalanobis distance and outlier analysis, considering eight onboard accelerometers. For the numerical simulations, 75 undamaged and 45 damaged track scenarios are considered. The alert limit state defined in the European Standard for assessing track geometry quality is also assumed as a threshold. It was found that the detection accuracy of the AI-based methodology for different sensor layouts and types of damage is greater than 94%, which is acceptable. Full article

Mobile robot path planning involves designing optimal routes from starting points to destinations within specific environmental conditions. Even though there are well-established autonomous navigation solutions, it is worth noting that comprehensive, systematically differentiated examinations of the critical technologies underpinning both single-robot and multi-robot path planning are notably scarce. These technologies encompass aspects such as environmental modeling, criteria for evaluating path quality, the techniques employed in path planning and so on. This paper presents a thorough exploration of techniques within the realm of mobile robot path planning. Initially, we provide an overview of eight diverse methods for mapping, each mirroring the varying levels of abstraction that robots employ to interpret their surroundings. Furthermore, we furnish open-source map datasets suited for both Single-Agent Path Planning (SAPF) and Multi-Agent Path Planning (MAPF) scenarios, accompanied by an analysis of prevalent evaluation metrics for path planning. Subsequently, focusing on the distinctive features of SAPF algorithms, we categorize them into three classes: classical algorithms, intelligent optimization algorithms, and artificial intelligence algorithms. Within the classical algorithms category, we introduce graph search algorithms, random sampling algorithms, and potential field algorithms. In the intelligent optimization algorithms domain, we introduce ant colony optimization, particle swarm optimization, and genetic algorithms. Within the domain of artificial intelligence algorithms, we discuss neural network algorithms and fuzzy logic algorithms. Following this, we delve into the different approaches to MAPF planning, examining centralized planning which emphasizes decoupling conflicts, and distributed planning which prioritizes task execution. Based on these categorizations, we comprehensively compare the characteristics and applicability of both SAPF and MAPF algorithms, while highlighting the challenges that this field is currently grappling with. Full article

The mobile platform of the parallel robot designed for picking and placing operations is usually equipped with one or two extra degree(s) of freedom to enable flexible grasping orientations. However, additional motors indicate extra loads for the moving platform, and the total payload performance shrinks. This paper proposes a spatial picking-and-placing manipulator, in which one actuator that is supposed to be installed on the mobile platform is placed far away from the mobile platform. The platform has a large workspace along one direction. The comprehensive analytical inverse and forward kinematic solutions of this robot are derived. The reachable workspace of the parallel manipulator module is then explored. The novel performance level index is designed to normalize the performance index and demonstrate the performance rank for any pose. A mathematical proof is provided for this novel index. The manipulability index is taken as an example to examine the level indicator. A multi-objective optimization is implemented to pursue optimal performance; then, the initial design and optimized results are compared in detail. A sample trajectory is provided to verify the correctness of the kinematic mathematical model of the parallel mechanism. Full article

This paper presents the development of a 6-DOF (Degrees of Freedom) parallel robot for single-incision laparoscopic surgery (SILS). The concept of the robotic system is developed with respect to a medical protocol designed by the medical experts in the team targeting a SILS procedure in urology. The kinematic model of the robotic system was defined to determine the singularities that may occur during functioning. FEM analyses were performed to determine the components of the robotic structure that may compromise the rigidity of the robotic system, and these components were redesigned and integrated into the final design of the robot. To verify the kinematic model a series of numerical and graphical simulations were performed, while to test the functionality of the robotic system, a low-cost experimental model was developed. The accuracy of the experimental model was measured using an optical motion tracking system. Full article

With the increasing utilization of portable electronic devices and wearable technologies, the field of human motion energy harvesting has gained significant attention. These devices have the potential to efficiently convert the mechanical energy generated by human motion into electrical energy, enabling a continuous power supply for low-power devices. This paper provides an overview of the fundamental principles underlying various energy harvesting modes, including friction-based, electromagnetic, and piezoelectric mechanisms, and categorizes existing energy harvesting devices accordingly. Furthermore, this study conducts a comprehensive analysis of key techniques in energy harvesting, such as mode selection, efficiency enhancement, miniaturized design of devices, and evaluation of energy harvesting experiments. It also compares the distinct characteristics of different energy harvesting modes. Finally, the paper summarizes the challenges faced by these devices in terms of integrating human biomechanics, achieving higher energy harvesting efficiencies, facilitating micro-miniaturization, enabling composite designs, and exploring broader applications. Moreover, it offers insights into the future development of human motion energy harvesting technology, laying a theoretical framework and providing a reference for future research endeavors in this field. Full article

Rotating machinery is the fundamental component of almost all industrial frameworks. Therefore, prognostics and health management (PHM) have emerged as crucial requirements for effectively managing and sustaining various systems in a timely manner. The unbalanced fault has been recognized as a significant contributing factor in the development of faults in rotor-bearing systems, eventually causing failure. Thus, it is essential to monitor the unbalance and maintain it within acceptable bounds in order to guarantee the system’s proper operation. Most approaches to the rotor’s unbalance monitoring are model-based instead of data-driven due to the shortage of faulted data. In a derived model-based approach, proper identification of the model’s parameters, e.g., bearing parameters, always plays a very crucial role. Nonetheless, the identified model’s parameters in their initial state would inevitably degenerate during a long-term operation because of aging or environmental changes, such that they are no longer well representative of the real system. In this context, this paper offers an adaptive model-based approach for the assessment of unbalance faults developing over days in a rotor-bearing model. The model is adaptive in the sense that it automatically adjusts its parameters so that they are more closely aligned with the real system. A particle swarm optimization (PSO) scheme is utilized in the parameter identification process. The residual serves as the index for initiating the adaptive process when it is greater than a preset percentage. Individual feature errors work as a gauge to determine which bearing parameters need to be reevaluated. A set of 16-month operational data from a local petrochemical company is used to validate the approach. The unbalanced deterioration trend is evaluated, and results from the adaptive methodology are assessed to show its superiority over the original one. It is also observed that the model’s capacity to anticipate unbalance is greatly enhanced by the adaptive strategy. Finally, future unbalances are explored to show its capacity for continuous monitoring-based maintenance solutions. Full article

Data-driven controller synthesis methods use input/output information to find the coefficients of a proposed control architecture. Virtual Reference Feedback Tuning (VRFT) is one of the most popular frameworks due to its simplicity and one-shoot synthesis style based on open-loop system response for classic regulators such as PI or PID. This paper presents a recursive VRFT framework to extend VRFT into high-order controllers with more complex structures. The framework first defines a reference model and controller structure, then uses the open-loop data to compute the virtual reference and error signals, and, finally, uses these to find the controller parameters via an optimization algorithm. Likewise, the recursive VRFT controller performance is improved by adding a model-based feedforward loop to improve reference signal tracking. The recursive method is tested to design a temperature uniformity control system. The obtained results show that the recursive VRFT with a feedforward improves the system response while allowing more complex controller synthesis. Full article

Robots gradually have the ability to plan grasping actions in unknown scenes by learning the manipulation of typical scenes. The grasping pose estimation method, as a kind of end-to-end method, has rapidly developed in recent years because of its good generalization. In this paper, we present a grasping pose estimation method for robots based on convolutional neural networks. In this method, a convolutional neural network model was employed, which can output the grasping success rate, approach angle, and gripper opening width for the input voxel. The grasping dataset was produced, and the model was trained in the physical simulator. A position optimization of the robotic grasping was proposed according to the distribution of the object centroid to improve the grasping success rate. An experimental platform for robot grasping was established, and 11 common everyday objects were selected for the experiments. Grasping experiments involving the eleven objects individually, multiple objects, as well as a dark environment without illumination, were performed. The results show that the method has the adaptability to grasp different geometric objects, including irregular shapes, and it is not influenced by lighting conditions. The total grasping success rate was 88.2% for the individual objects and 81.1% for the cluttered scene. Full article

The Industrial Revolution drives the digitization of society and industry, entailing Cyber-Physical Systems (CPSs) that form ecosystems where system owners and third parties share responsibilities within and across industry domains. Such ecosystems demand smart CPSs that continuously align their architecture and governance to the concerns of various stakeholders, including developers, operators, and users. In order to satisfy short- and long-term stakeholder concerns in a continuously evolving operational context, this work proposes self-adaptive software models that promote DevOps for smart CPS. Our architectural approach extends to the embedded system layer and utilizes embedded and interconnected Digital Twins to manage change effectively. Experiments conducted on industrial embedded control units demonstrate the approach’s effectiveness in achieving sub-millisecond real-time closed-loop control of CPS assets and the simultaneous high-fidelity twinning (i.e., monitoring) of asset states. In addition, the experiments show practical support for the adaptation and evolution of CPS through the dynamic reconfiguring and updating of real-time control services and communication links without downtime. The evaluation results conclude that, in particular, the embedded Digital Twins can enhance CPS smartness by providing service-oriented access to CPS data, monitoring, adaptation, and control capabilities. Furthermore, the embedded Digital Twins can facilitate the seamless integration of these capabilities into current and future industrial service ecosystems. At the same time, these capabilities contribute to implementing emerging industrial services such as remote asset monitoring, commissioning, and maintenance. Full article

The increasing complexity of mathematical models developed as part of the recent advancements in autonomous mobility platforms has led to an escalation in uncertainty. Despite the intricate nature of such models, the detection, decision, and control methods for autonomous mobility path tracking remain critical. This study aims to achieve path tracking based on pixel-based control errors without parameters in the mathematical model. The proposed approach entails deriving control errors from a multi-particle filter based on a camera, estimating the error dynamics coefficients through a recursive least squares (RLS) approach, and using the sliding mode approach and weighted injection to formulate a cost function that leverages the estimated coefficients and control errors. The resultant adaptive steering control expedites the convergence of control errors towards zero by determining the magnitude of the injection variable based on the control errors and the finite-time convergence condition. The efficacy of the proposed approach is evaluated through an S-curved and elliptical path using autonomous mobility equipped with a single steering and driving module. The results demonstrate the capability of the approach to reasonably track target paths through driving and steering control facilitated by a multi-particle filter and a lidar-based obstacle detection system. Full article

In order to study the internal flow characteristics of the electric submersible pump (ESP) when the gas–liquid two-phase flow is conveyed by the variable frequency variable speed operation and the change of the imported gas content, the impeller of the Q10# ESP is taken as the research object, based on the Eulerian-Eulerian non-homogeneous phase. The flow model, the unsteady Reynolds time-averaged N-S equation, and the standard k-ε turbulence model are used for transient simulation calculations of the gas–liquid two-phase flow in the impeller of the ESP. Calculations show that with the rotation of the impeller, the gas phase is unevenly distributed in the flow channel. The gas phase is mainly concentrated on the inlet side of the flow channel near the front cover, and the gas phase exhibits periodic aggregation and diffusion in the flow channel. When the impeller speed increases, the period of periodic accumulation and diffusion of gas in the flow channel is shortened and the gas concentration in the impeller decreases, the overall flow velocity in the flow channel increases, and the pressure difference between the inlet and outlet increases. The pressure difference between the two sides of the blade is proportional to the speed of the impeller, and the fluctuation frequency of the blade surface also increases. As the gas content increases, the maximum concentration of gas phase in the flow channel increases. The area occupied by the high concentration of gas phase in the flow channel expands toward the blade’s working surface, and periodically accumulates, diffuses, and grows. The gas-liquid splitting area shrinks toward the front cover side and the pump. The internal pressure increases slightly, the main flow velocity increases, and the vortex action range increases. Full article

Rotor unbalance is the most important factor affecting the dynamic performance of aircraft engines. The existing unbalance prediction and control methods are insufficient for multi-stage rotors. The post-assembly unbalance of rotors in aircraft engines is a critical factor affecting their dynamic performance. In order to predict and reduce the unbalance of multi-stage rotors after assembly, this paper establishes a measurement model for the center-of-mass offset of aircraft engine rotors through decoupled calculations of the unbalance. Furthermore, it constructs an unbalance prediction model using the spatial transfer mechanism of combined rotor offset centers under the influence of manufacturing errors. Additionally, a method for measuring rotor unbalance during the assembly phase is proposed. The experimental results of the unbalance in multi-stage combined rotor assembly indicate that the degree of agreement between the predicted results and the experimental results is 91.3%, resulting in a reduction in the mean error of 15.3% compared to before the correction. The study also investigates the impact of manufacturing errors on unbalance. This research provides robust support for controlling the unbalance in multi-stage combined rotor assembly. Full article

This article, besides offering data of great value for any designer of high-power short-circuits of special three-winding design, illustrates the correlation with the corresponding FRA measurements, validating this type of measurement. The frequency response measurements can provide data about the transformer’s status after it is put into service, its vulnerability in incipient states, and, particularly for this type of transformer, its insulation, which is the subject of high dielectric stress due to its invertor working regime. This article presents the behavior of a three-phase 8400 kVA medium-voltage step-up transformer (corrugated hermetic tank) specially designed for photovoltaic applications during short-circuit tests. This transformer, fed by two inverters, has two secondaries with elliptical windings (non-circular aluminum foil for LV windings and an aluminum conductor for HV windings). Various experiments were performed, including measurements of winding resistance, measurements of voltage ratio, measurements of short-circuit impedance and load loss on three tappings, measurements of no-load loss and current, a frequency response analysis, and short-circuiting. These experiments were performed to study the behavior of the transformer, which, in real life, is powered by photovoltaic inverters on the LV side that feed into the MV grid on the HV side, making it the interface between the photovoltaic inverter and the MV grid. An auxiliary supply transformer may be connected to the LV side. Given these elements, concerning both the importance and the particularities of the problem studied, we can say that this article represents a niche study on the guarantee of good functioning and safety in operation given the passing of the test to withstand the dynamic effects of short-circuiting. Full article

Direct current (DC) networks play a pivotal role in the growing integration of renewable energy sources. However, the occurrence of DC arc faults can introduce disruptions and pose fire hazards within these networks. In order to ensure both safety and optimal functionality, it becomes imperative to comprehend the characteristics of DC arc faults and implement a dependable detection system. This paper introduces an innovative arc fault detection algorithm that leverages current filtering based on the empirical rule in conjunction with intelligent machine learning techniques. The core of this approach involves the sampling and subsequent filtration of current using the empirical rule. This filtering process effectively amplifies the distinctions between normal and arcing states, thereby enhancing the overall performance of the intelligent learning techniques integrated into the system. Furthermore, this proposed diagnosis scheme requires only the signal from the current sensor, which reduces the complexity of the diagnosis scheme. The results obtained from the detection process serve to affirm the effectiveness and reliability of the proposed DC arc fault diagnosis scheme. Full article

Disabled pedestrians are among the most vulnerable groups in road traffic. Using technology to assist this vulnerable group could be instrumental in reducing the mobility challenges they face daily. On the one hand, the automotive industry is focusing its efforts on car automation. On the other hand, in recent years, assistive technology has been promoted as a tool for consolidating the functional independence of people with disabilities. However, the success of these technologies depends on how well they help self-driving cars interact with disabled pedestrians. This paper proposes an architecture to facilitate interaction between disabled pedestrians and self-driving cars based on deep learning and 802.11p wireless technology. Through the application of assistive technology, we can locate the pedestrian with a disability within the road traffic ecosystem, and we define a set of functionalities for the identification of hand gestures of people with disabilities. These functions enable pedestrians with disabilities to express their intentions, improving their confidence and safety level in tasks within the road ecosystem, such as crossing the street. Full article

The temperature prediction of hoist motor is one of the effective ways to ensure the safe production of mine hoist. Digital twin technology is a technology that combines the physical system of the real world with the digital model of the virtual world. Through digital twin technology, the physical system in the real world can be monitored and simulated in a virtual environment, and the state information of these systems can be monitored in real time. Recurrent neural network is a kind of neural network suitable for processing sequence data, which can automatically extract and learn the feature information in sequential data. To achieve online monitoring and over-advance perception of the temperature of the mine hoist motor, a temperature prediction and advance sensing method based on digital twins and recurrent neural network is proposed. To begin with, a high-fidelity digital twin monitoring system for mine hoists is constructed, enabling the acquisition of real-time temperature data. These temperature data are then fed into a neural network for feature extraction and precise prediction of the motor’s state. Subsequently, based on the temperature prediction module in the digital twin hoist monitoring system, a user interface (UI) is developed, and a fully functional digital twin temperature monitoring system is built and experimentally validated. The experimental results demonstrate that the digital twin system effectively monitors the real-time temperature state of the motor during the operation of the mine hoist. Furthermore, the integration of digital twin and recurrent neural network enables the accurate prediction and proactive detection of temperature variations in the motor of the mine hoist. This innovative approach introduces a novel perspective for implementing predictive maintenance in the mining industry, enhancing the safety and reliability of mine hoists. Additionally, it offers valuable technical support in improving maintenance efficiency and reducing associated costs. Full article

The existence of vortex ropes inside the draft tube significantly impacts hydraulic efficiency and operational stability, and few studies on the formation mechanism of vortex ropes and hydraulic loss problems have been explored. Hence, in this paper, we build an inherent correlation between the local entropy production rate (LEPR) in the draft tube and the dynamics of vortex motion, by incorporating the vortex identification method ${\tilde{\Omega }}_R$ with entropy production theory, using the OpenFOAM-v2212 software. From the analysis of the entropy production theory, the entropy production rate caused by turbulence dissipation (EPTD) is responsible for the majority of energy loss in the form of entropy production rate, accounting for about 87% of the total entropy production rate (TEPR) in different load operations. Comparatively, the entropy production rate caused by wall shear stress (EPWS) can account for up to 12%, while the entropy production rate due to direct dissipation (EPDD) plays a minor role in TEPR. The rotating vortex rope movement of the unit at part load conditions leads to more intense LEPR. Therefore, to determine the hydraulic loss caused by the vortex rope, the TEPR at the cross-section can be used to assess the hydraulic characteristics of the draft tube. Full article

There are various studies on the structural and functional constructions of hand prostheses inspired by human biomechanics, and there are different kinds of prostheses available on the market. This paper aims to present the relevant stages of designing a hand prosthesis prototype that is innovative due to its mechanical structure and, therefore, the prosthesis fingers’ DOF and mobility. The prosthesis is designed to have independent finger motion with the rotations of each of the three phalanges and, most importantly, rotation for each of the fingers relative to the palm. All these motions are generated and controlled by micromotors, a microcontroller, and sensors. A reverse engineering technique was applied for obtaining the exterior surface dimensions of the prosthesis and this consequently ensures that this prosthesis looks as realistic as possible. Small, light mechanical parts were designed as components of the mechanical system for the motions of finger phalanges and most of them (gears, levers, shells) were made using 3D-printing technologies (digital light processing (DLP) and/or selective laser sintering (SLS)). Aspects of some technical problems which arose during the prototype assembly are also recorded in the paper. Further research development will focus on the tests conducted on the prosthesis and the consequent adjustments of the prototype. Full article

The reliable operation of power transmission networks depends on the timely detection and localization of faults. Fault classification and localization in electricity transmission networks can be challenging because of the complicated and dynamic nature of the system. In recent years, a variety of machine learning (ML) and deep learning algorithms (DL) have found applications in the enhancement of fault identification and classification within power transmission networks. Yet, the efficacy of these ML architectures is profoundly dependent upon the abundance and quality of the training data. This intellectual explanation introduces an innovative strategy for the classification and pinpointing of faults within power transmission networks. This is achieved through the utilization of variational autoencoders (VAEs) to generate synthetic data, which in turn is harnessed in conjunction with ML algorithms. This approach encompasses the augmentation of the available dataset by infusing it with synthetically generated instances, contributing to a more robust and proficient fault recognition and categorization system. Specifically, we train the VAE on a set of real-world power transmission data and generate synthetic fault data that capture the statistical properties of real-world data. To overcome the difficulty of fault diagnosis methodology in three-phase high voltage transmission networks, a categorical boosting (Cat-Boost) algorithm is proposed in this work. The other standard machine learning algorithms recommended for this study, including Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF), and K-Nearest Neighbors (KNN), utilizing the customized version of forward feature selection (FFS), were trained using synthetic data generated by a VAE. The results indicate exceptional performance, surpassing current state-of-the-art techniques, in the tasks of fault classification and localization. Notably, our approach achieves a remarkable 99% accuracy in fault classification and an extremely low mean absolute error (MAE) of 0.2 in fault localization. These outcomes represent a notable advancement compared to the most effective existing baseline methods. Full article

In cooperative tasks, the ability to keep a kinematic relationship between the robots involved is essential. The main goal in this work is to design a synchronization control law for mobile manipulator robots (MMRs) considering a (2,0) differential mobile platform, which possesses a non-holonomic motion constraint. To fulfill this purpose, a generalized trajectory tracking control law based on the computed torque technique, for an MMR with n degrees of freedom, is presented. Using Lyapunov stability theory, it is shown that the closed loop system is semiglobal and uniformly ultimately boundedness (UUB) stable. To add position-level static coupling terms to achieve synchronization on a group of MMRs, the control law designed for the trajectory tracking problem is extended. Both experimental and numerical simulation results are presented to show the designed controllers performance. A successful experimental validation for the trajectory tracking problem using an 8 degrees of freedom (DoF) robot model (KUKA youBot) is depicted. Finally, numerical simulations in the CoppeliaSim environment are shown, which are used to test the synchronization control law made on the hypothetical scenario, where a two robot system has to manipulate an object over a parametric trajectory. Full article

Potassium dihydrogen phosphate (KDP) has garnered considerable attention due to its diverse applications across various scientific and engineering domains. Although promising machining performance enhancements have been achieved in ultra-precision diamond cutting, the brittle–ductile transition (BDT) depth for KDP crystals is essentially at the nanometer range and limits the further improvement of machining efficiency. In this paper, a novel ultra-precision diamond cutting process based on tool trapezoidal modulation is proposed for the first time to investigate the BDT characteristics of KDP crystals. By intentionally designing the tool modulation locus, the uncut chip thickness and cutting direction in the cutting duty cycle are kept constant, which provides a new strategy for probing the BDT mechanism and enhancing the machining performance. The BDT depth is significantly increased compared to the conventional ultra-precision diamond cutting owing to its unique modulation machining advantages. The significance of this paper lies not only in the improvement of the machining efficiency of KDP crystals through the proposed modulation cutting process, but also in the possibility of extending the relevant research methods and conclusions to the machining performance enhancement of other brittle optical crystals. Full article

The fault feature extraction and diagnosis of autonomous underwater vehicles (AUVs) in complex environments pose significant challenges due to the intricate nature of the signals that reflect the AUVs’ states in the deep ocean. In this paper, an analytical model-free fault diagnosis algorithm based on a multi-channel full convolutional neural network (MC-FCNN) is introduced to establish patterns between AUV states and potential fault types using multi-sensor signals. Firstly, the AUV raw dataset undergoes random forest multiple imputation by chained equations (RF-MICE) to serve as the input of the convolution neural network. Next, signal features are extracted through the full convolution channel, which can be fused as multilayer perceptron (MLP) input and Softmax classifier for fault identification. Finally, to validate the effectiveness of the proposed MC-FCNN model, fault diagnosis experiments are conducted using the dataset sourced from the Zhejiang University Laboratory with missing data. The experimental results demonstrate that, even with $60\%$ of the data missing, the proposed RF-MICE with MC-FCNN model can still achieve an ideal fault identification. Full article

The main contribution of this article is creating synergy between subjects; this means that students use the same graphical tool in several subjects. So far, the bond graph has not been used in control theory, but it is the “native language” of mechatronics engineers, so we would like to introduce it into the teaching of control theory. The bond graph method is proposed as a novel teaching method to teach mechatronics subjects in the paper. The bond graph is a graphical alternative to ordinary differential equations from a mathematical standpoint. Traditionally, control theory employs ordinary differential equations, as they are familiar to control theorists. However, mathematically, both approaches are equivalent but require a slightly different approach in their application. This article highlights the mathematical similarities between the two approaches while emphasizing the distinctions in graphical representation. Another contribution is that the PID and sliding mode controller are represented using the bond graph method. In the meantime, through the use of practical examples, we effectively illustrate how the same problem can be solved using either approach. In the training materials, the PID controller and an adaptive robust sliding mode controller (ARSMC) with the bond graph are utilized as examples to demonstrate synergy in mechatronics. Finally, we present proof that mechatronic engineers achieve superior outcomes when utilizing the bond graph approach, based on test results from undergraduate students. Full article

Due to the importance of induction motors in a wide variety of industrial processes, it is crucial to properly identify abnormal conditions in order to avoid unexpected stops. The inter-turn short circuit (ITSC) is a very common failure produced with electrical stresses and affects induction motors (IMs), leading to catastrophic damage. Therefore, this work proposes the use of the empirical wavelet transform to characterize the time frequency behavior of the IM combined with a self-organizing map (SOM) structure to perform an automatic detection and classification of different severities of ITSC. Since the amount of information obtained from the empirical wavelet transform is big, a genetic algorithm is implemented to select the modes that allow a reduction in the quantization error in the SOM. The proposed methodology is applied to a real IM during the start-up transient considering four different fundamental frequencies. The results prove that this technique is able to detect and classify three different fault severities regardless of the operation frequency. Full article

Because of its non-contact measurement characteristics, trackside acoustic technology is now utilized for train bearing fault diagnosis. However, the collected acoustic signal produces Doppler distortions that can impact the accuracy of bearing fault diagnosis. Additionally, when a fault occurs in the train bearing, it is analyzed using cyclostationary methods. In this study, we combine bearing fault characteristics with Doppler distortion correction and cyclostationary analysis methods. The trackside acoustic test platform is employed to collect and test the fault signals from bearings. These signals are processed and analyzed using Doppler distortion correction algorithms and cyclostationary techniques. A comparison between time domain maps and power spectrum maps before and after correction reveals an increase in SNR (signal to noise ratio) and a more concentrated energy distribution within the fault signals—at least a 50% improvement is observed. To further validate our method’s effectiveness, we select existing TADS equipment from a depot to collect bearing signals for analysis and processing using our proposed bearing fault diagnosis method. Comparison of time domain maps and power spectrum maps before and after correction shows clearer overall images and amplitude increase of nearly 125%. Therefore, we have successfully developed a stepwise method for bearing fault diagnosis based on cyclostationary Doppler distortion correction. Full article

The difficulties of testing the dynamic transmission errors of gears in complex environments, such as high-speed operations or situations with oil contamination, and the limited variety of available testing methods have been recognized as issues which this study endeavors to tackle. In this study, a testing method for evaluating high-speed dynamic transmission errors of spur gears through the use of strain sensors is proposed. To reduce the interference of environmental noise on testing signals, physical measures, such as the use of copper foil to shield signal wires and the grounding of data acquisition equipment, were implemented during the testing process. Utilizing wavelet decomposition to distinguish between the high- and low-frequency components of the testing signals, the transmission error of gears during high-speed operation was calculated. After confirming the feasibility of the stress–strain stiffness approach in gear transmission error testing using the magnetic grid method, tests on modified and non-modified gears at various speeds and loads were carried out. The two types of test data were processed and evaluated to determine the effect of speed and load on gear dynamic transmission error. It was possible to conduct research on the testing technique of gear dynamic transmission errors utilizing strain sensors, which provides a new, fast, simple, and practical testing strategy for gear transmission error testing. Full article

Collaborative robots (cobots) become more and more important in industrial manufacturing as flexible companions, working side by side with humans without safety fences. A key challenge of such workplaces is to guarantee the safety of the human co-workers. The safeguarding Power and Force Limiting, as specified by ISO 10218-2 and ISO/TS 15066, has the objective to protect humans against robot collisions by preventing the robot from exceeding biomechanical limits. Unintended contact such as collisions can occur under unconstrained spatial conditions (a human body part can move freely) or constrained spatial conditions (a human body part is pinched). In particular, collisions under constrained conditions involve a high risk of injury and thus require the robot to stop immediately after detecting the collision. The robot’s speed has a significant influence on its stopping behavior, though, and thus on the maximum collision forces that the robot can exert on the human body. Consequently, a safe velocity is required that avoids the robot from exerting forces and pressures beyond the biomechanical limits. Today, such velocities can only be ascertained in costly robot experiments. In this article, we describe a model that enables us to determine the contact forces of a cobot as they occur in constrained collisions. Through simulations, it becomes possible to iteratively determine the maximum safe velocity for a specific contact hazard that occurs under constrained spatial conditions. Experimental tests with different cobots confirm the results of our model, albeit not for all robots. Despite the mixed test results, we strongly believe that our model can significantly improve the reliability of assumptions made today during the planning of cobots. Full article

Disc pumps have obvious advantages in dealing with difficult-to-pump media. Energy efficiency and sustainable energy management are important topics with regard to reducing costs and promoting carbon neutrality. Though the concept of the disc pump was proposed in the 1850s, development was slow and limited by its initial model. However, with the development of industries such as petrochemicals and food, the efficient pumping of difficult-to-pump media is much needed, but facing challenges. Therefore, research on energy-efficient disc pumps is particularly important moving forward. In this paper, the available information from the open literature about the research and development of the disc pump will be thoroughly reviewed. It focuses on the historical development, energy efficiency and physical model application of the disc pump. The review ends with a proposal for the direction of future development, and in this aspect, it is proposed that the energy efficiency prediction model based on velocity slip theory, the energy management system based on multi-scenarios and the design method based on energy conversion theory are important. The latest achievements in energy conversion are given. This review also provides a new perspective for the development of energy-efficient disc pumps. Full article

The processing of aerial images taken by drones is a challenging task due to their high resolution and the presence of small objects. The scale of the objects varies diversely depending on the position of the drone, which can result in loss of information or increased difficulty in detecting small objects. To address this issue, images are either randomly cropped or divided into small patches before training and inference. This paper proposes a defect detection framework that harnesses the advantages of slice-aided inference for small and medium-size damage on the surface of wind turbine blades. This framework enables the comparison of different slicing strategies, including a conventional patch division strategy and a more recent slice-aided hyper-inference, on several state-of-the-art deep neural network baselines for the detection of surface defects in wind turbine blade images. Our experiments provide extensive empirical results, highlighting the benefits of using the slice-aided strategy and the significant improvements made by these networks on an ultra high-resolution drone image dataset. Full article

Kinematic modelling of parallel manipulators poses significant challenges due to the absence of analytical solutions for the Forward Kinematics (FK) problem. This study centres on a specific parallel planar robot, specifically a 3RRR configuration, and addresses the FK problem through two distinct methodologies: Genetic Algorithms (GA) and Neural Networks (NN). Utilising the Inverse Kinematic (IK) model, which is readily obtainable, both GA and NN techniques are implemented without the need for closed-loop formulations or non-systematic mathematical tools, allowing for easy extension to other robot types. A comparative analysis against an existing numerical method demonstrates that the proposed methodologies yield comparable or superior performance in terms of accuracy and time, all while reducing development costs. Despite GA’s time consumption limitations, it excels in path planning, whereas NN delivers precise results unaffected by stochastic elements. These results underscore the feasibility of using neural networks and genetic algorithms as viable alternatives for real-time kinematic modelling of robots when closed-form solutions are unavailable. Full article