Machines, Volume 12, Issue 1 (January 2024) – 84 articles

Advanced driving algorithms, control strategies, and their optimization in self-driving vehicles in various scenarios are hotspots in current research; 4WS-4WD (four-wheel steering and four-wheel drive) is another hotspot in the study of new concept models; and the nonlinear dynamic characteristics of self-driving vehicles (AVs) are prominent in the fast cornering mode, which leads to a significant reduction in the accuracy and stability of trajectory tracking. Based on these research backgrounds, this paper proposes a control strategy optimization idea based on the 4WS4WD vehicle and its optimization model. The main content includes the establishment of a 3D vehicle model that takes into account vehicle load transfer and position change, and the establishment of a hierarchical control strategy based on the optimized NMPC and 4WS4WD models. The controller consists of two parts: an upper tracking controller based on the new vehicle model and NMPC, and a lower decoupled controller. The tracking control effect of the algorithmic control strategy based on the model and controller is validated in the high-speed serpentine motion mode and double-shift linear motion mode on the joint simulation platform of Car Sim and Simulink. Full article

The drum rotor of an aero-engine is connected by one or multiple mounting edges through bolts, and their dynamics are significantly influenced by the preload state of the bolts. Long working hours in challenging environments can result in the deterioration of bolt pre-tightening during assembly or service, which impacts the rotor’s dynamic stability and overall performance. Currently, there are no available methods for detecting the dynamic characteristics of the drum connection components. This paper analyzes the impact of the natural characteristics of the drum composite structure of a high-pressure aero-engine turbine based on the refined finite element method when the preloading state changes. Two conditions of deviation and uneven stiffness distribution were applied to the connected components of the drum. The analysis focused on the impact of the pre-tightening state on its natural frequency. After analyzing the feasibility of identifying the pre-tightening state, two methods are proposed. These methods focus on changes in natural frequency and mode shape, specifically the sensitive natural frequency change method and the mode step change method. The methods proposed in this paper can serve as a reference for evaluating the quality of assembling complex disc–drum structures with multiple bolt connections. Full article

The feature extraction problem of coupled vibration signals with multiple fault modes of planetary gears has not been solved effectively. At present, kernel principal component analysis (KPCA) is usually used to solve nonlinear feature extraction problems, but the kernel function selection and its blind parameter setting greatly affect the performance of the algorithm. For the optimization of the kernel parameters, it is very urgent to study the theoretical modeling to improve the performance of kernel principal component analysis. Aiming at the deficiency of kernel principal component analysis using the single-kernel function for the nonlinear mapping of feature extraction, a dual-kernel function based on the flexible linear combination of a radial basis kernel function and polynomial kernel function is proposed. In order to increase the scientificity of setting the kernel parameters and the flexible weight coefficient, a mathematical model for dual-kernel parameter optimization was constructed based on a Fisher criterion discriminant analysis. In addition, this paper puts forward a swarm intelligent fusion algorithm to increase this method’s advantages for optimization problems, involving the shuffled frog leaping algorithm combined with particle swarm optimization (SFLA-PSO). The new fusion algorithm was applied to optimize the kernel parameters to improve the performance of KPCA nonlinear mapping. The optimized dual-kernel function KPCA (DKKPCA) was applied to the feature extraction of planetary gear wear damage, and had a good identification effect on the fuzzy damage boundary of the planetary gearbox. The conclusion is that the DKKPCA optimized by the SFLA-PSO swarm intelligent fusion algorithm not only effectively improves the performance of feature extraction, but also enables the adaptive selection of parameters for the dual-kernel function and the adjustment of weights for the basic kernel function through a certain degree of optimization; so, this method has great potential for practical use. Full article

The design of components and elements comprising industrial machinery, as well as those that are part of an industrial system, has been carried out in recent years using various design methodologies. Currently, the products demanded by customers, as well as the needs of different companies, governments, and individuals, require considerations beyond traditional design, including multidisciplinary aspects such as sustainability, environmental friendliness, and circular economy. The design methodologies considered for this study are the quality function deployment (QFD) methodology, the theory of inventive problem-solving methodology, Ashby’s Materials Selection methodology, and the systematic approach methodology, which are currently the main design methodologies. These methodologies present some disadvantages, such as multidisciplinary requirements not being considered directly, the selection of materials based on standards is not well established, and obtaining technical requirements is ambiguous for the technical purposes of the design or manufacturing, and the designer’s experience in these examples is important to the design process and the development of the product. For these reasons, the traditional design methodologies are presented, next, a new design methodology is proposed and described, then a case study is performed in order to compare the proposed methodology with traditional design methodologies. Finally, the results show advantages over the traditional design methodologies. Full article

The main objective of this paper is to discuss the experimental validation of a tele-operation system for remote center-of-motion tasks, such as laparoscopic surgery. This validation is based on the use of an extra sensor placed on the master manipulator. The tele-operation system is composed of a new hybrid haptic device (nHH) intended to be used as a master manipulator controlling a collaborative robot, used as a slave surgical robot. The resolution of the forward kinematic model (FKM) of the master device is performed experimentally thanks to the use of an extra sensor. The IMU, as the extra sensor, is installed on the serial part of the nHH device to measure the orientation and is enabled to solve the FKM of the parallel part of the nHH device. The use of an extra sensor reduces the calculation time, improves the accuracy of the KFM, and makes it suitable for real-time applications. The preliminary validation of the force feedback in the nHH workspace is validated. Experiments were conducted on the master–slave platform to validate the proposed approach. The results are promising, which proves that the nHH device presents a suitable performance for the desired task. Full article

Due to the complex structure of the joint module and harsh working conditions of unmanned platforms, the fault information is often overwhelmed by noise. Moreover, traditional mechanical health state recognition methods usually require a large amount of labeled data in advance, which is difficult to obtain for specific fault data in engineering applications. This limited amount of fault data restricts the diagnostic performance. Additionally, the characteristics of convolutional neural networks (CNNs) limit their ability to capture the relative positional information of fault features. In order to obtain more comprehensive fault information, this paper proposes an intelligent health state recognition method for unmanned platform joint modules based on feature modal decomposition (FMD) and the enhanced capsule network. Firstly, the collected vibration signals are decomposed into a series of feature modal components using FMD. Then, time–frequency maps containing significant fault features are generated based on the continuous wavelet transform (CWT). Finally, a multi-scale feature enhancement (MLFE) module and an efficient channel attention (ECA) module are proposed to enhance the feature extraction capability of the capsule network, extracting more comprehensive global and local feature information from the time–frequency maps to achieve the intelligent state recognition of joint modules. This approach enhances fault features while reducing the impact of redundant features, significantly improving the feature extraction capability without increasing the model’s computational complexity. The effectiveness and superiority of the proposed method are validated through experiments on an unmanned platform joint-module testbed. An ablation experiment demonstrates the effectiveness of the MLFE and ECA modules, and a comparison with other advanced network models proves the superiority of the proposed method for health status recognition. Full article

This paper presents a geometric approach to solve the inverse kinematics for an anthropomorphic robotic arm with seven degrees of freedom (DoF). The proposal is based on conformal geometric algebra (CGA), by which many geometric primitives can be operated naturally and directly. CGA allows for the intersection of geometric entities such as two or more spheres or a plane’s projection over a sphere. Rigid transformations of such geometric entities are performed using only one operation through another geometric entity called a motor. CGA imposes geometric restrictions on the inverse kinematics solution, which avoids computation of the forward kinematics or other numerical solutions, unlike traditional approaches. Comparisons with state-of-the-art algorithms are included to prove our algorithm’s superior performance: such as decreased execution time and errors of the end-effector for a series of desired poses. Full article

This paper introduces a two-stage intelligent fault diagnosis model for rolling element bearings (REBs) aimed at overcoming the challenge of limited real-world vibration training data. In this study, bearing characteristic frequencies (BCFs) extracted from a novel hybrid method combining cepstrum pre-whitening (CPW) and high-pass filtering developed by the authors’ group are used as input features, and a two-stage approach is taken to develop an intelligent REB fault detect and diagnosis model. In the first stage, various machine learning (ML) methods, including support vector machine (SVM), multinomial logistic regressions (MLR), and artificial neural networks (ANN), are evaluated to identify faulty bearings from healthy ones. The best-performing ML model is selected for this stage. In the second stage, a similar evaluation is conducted to find the most suitable ML technique for bearing fault classification. The model is trained and validated using vibration data from an EU Clean Sky2 I2BS project (An EU Clean Sky 2 project ‘Integrated Intelligent Bearing Systems’ collaborated between Schaeffler Technologies and the University of Southampton. Safran Aero Engines was the topic manager for this project) and tested on datasets from Case Western Reserve University (CWRU) and the US Society for Machinery Failure Prevention Technology (MFPT). The results show that the two-stage model, using an SVM with a polynomial kernel function in Stage-1 and an ANN with one hidden layer and 0.05 dropout rate in Stage-2, can successfully detect bearing conditions in both test datasets and perform better than the results in literature without the requirement of further training. Compared with a single-stage model, the two-stage model also shows improved performance. Full article

Understanding the creep behaviour of materials is crucial in structural design, since assessing their durability and long-term performance is essential for ensuring the safety of the structures. Experimental testing allows to gather data on the creep behaviour of materials, as well as observe the damage mechanisms and dependence on environmental effects, such as stress and temperature. In this paper, the development of a cyclic creep testing station is presented. An innovative compact device is designed for testing single-lap joints using pressure-sensitive adhesives (PSAs) at different stress and temperature levels. The design is based on a mechanism that periodically supports a hanging weight resulting in an alternating load applied to the bonded joint. The assembled testing setup is validated by comparing the results of the developed machine with cyclic creep experimental data obtained with a servo-hydraulic testing machine adapted for cyclic creep. After validation, preliminary tests with one PSA at 55 °C are presented to evaluate its performance at higher temperatures. The results indicate that the developed cyclic creep machine can be used to characterise the creep behaviour of PSAs under cyclic loading. Full article

The drill pipe of a rotary rig is subject to the dynamic influence of non-stationary loads, including rotation torque and applied force. In order to address the challenge of simultaneously extrapolating multiple non-stationary loads, a novel extrapolation framework is proposed. This framework utilizes rainflow counting to obtain mean and amplitude sequences of the loads. The extreme values of the amplitude sequence are fitted using the Generalized Pareto Distribution (GPD), while the median values are fitted using the Double Kernel Density Estimation (DKDE). By extrapolating the Inverse Cumulative Distribution Function (ICDF) based on the fitted distribution, a new amplitude sequence can be derived. The combination of this extrapolated amplitude sequence with the original mean sequence forms a new load spectrum. The results of applying the proposed extrapolation method to the drill pipe of a rotary rig demonstrate the ability of the method to yield conservative extrapolation results and accurately capture the variations in damage under the original working conditions. Full article

In this study, we propose an optimization method for occupant protection systems using a machine learning technique. First, a crash simulation model was developed for a Euro NCAP MPDB frontal crash test condition. Second, a series of parametric simulations were performed using a THOR dummy model with varying occupant safety system design parameters, such as belt attachment locations, belt load limits, crash pulse, and so on. Third, metamodels were developed using neural networks to predict injury criteria for a given occupant safety system design. Fourth, the occupant safety system was optimized using metamodels, and the optimal design was verified using a subsequent crash simulation. Lastly, the effects of design variables on injury criteria were investigated using the Shapely method. The Euro NCAP score of the THOR dummy model was improved from 14.3 to 16 points. The main improvement resulted from a reduced risk of injury to the chest and leg regions. Higher D-ring and rearward anchor placements benefited the chest and leg regions, respectively, while a rear-loaded crash pulse was beneficial for both areas. The sensitivity analysis through the Shapley method quantitatively estimated the contribution of each design variable regarding improvements in injury metric values for the THOR dummy. Full article

For robots to become effective apprentices and collaborators, they must exhibit some level of autonomy, for example, recognizing failures and identifying ways to address them with the aid of their human teammates. In this systems paper, we present an integrated cognitive robotic architecture for a “robot apprentice” that is capable of assessing its own performance, identifying task execution failures, communicating them to humans, and resolving them, if possible. We demonstrate the capabilities of our proposed architecture with a series of demonstrations and confirm with an online user study that people prefer our robot apprentice compared to robots without those capabilities. Full article

The mass production of metallic components requires high agility in the working process conditioned by the necessity of building details of different shapes and sizes. Changing the size of the components theoretically influences the thermal dissipation capability of the same, which could lead to a change in their structure and mechanical properties. This is particularly important when aluminum alloys are concerned. For this reason, two Al5356 single-track specimens were built using the same technological conditions of layer deposition by varying only their geometrical size. In all cases, the specimens were wire and arc additively manufactured (WAAM) using a process based on gas metal arc welding (GMAW) in the cold arc pulse mode (CAP). The structure of both specimens was studied and defects along their surfaces were detected in the form of micro-pores and micro-cracks. A high concentration of undissolved Mg particles was also detected, along with some standalone Si particles. Uniformity in the build-up process was achieved, which led to the formation of nearly identical structures in the specimens. Subsequently, the resultant mechanical properties were also highly comparable. This indicates that the geometry-related variation in thermal conditions has an insignificant influence on the component’s structure and properties. Full article

Silicon carbide particle-reinforced aluminum matrix composites (SiCp/Al) find diverse applications in engineering. Nevertheless, SiCp/Al exhibit limited machinability due to their special structure. A pulsed laser ultrasonic vibration assisted cutting (PLUVAC) method was proposed to enhance the machining characteristics of SiCp/Al and decrease surface defects. The finite element model was constructed, considering both the thermal effect of the pulsed laser and the location distribution of SiC particles. The model has been developed to analyze the damage forms of SiC particles and the formation mechanisms for the surface morphology. The influence of pulsed laser power on average cutting forces has also been analyzed. Research results indicate that PLUVAC accelerates the transition from the brittleness to the plastic of SiC particles, which helps to reduce surface scratching caused by fragmented SiC particles. Furthermore, the enhancement of surface quality is attributed to the decrease in surface cracks and the beneficial coating effect of the Al matrix. The accuracy of the simulation is verified by experiments, and the feasibility of PLUVAC method to enhance the surface quality of SiCp/Al is confirmed. Full article

A fault diagnosis method based on deep learning integration is proposed focusing on fault text data to effectively improve the efficiency of fault repair and the accuracy of fault localization in the braking control system of an electric multiple unit (EMU). First, the Borderline-SMOTE algorithm is employed to synthesize minority class samples at the boundary, addressing the data imbalance and optimizing the distribution of data within the fault text. Then, a multi-dimensional word representation is generated using the multi-layer bidirectional transformer architecture from the pre-training model, BERT. Next, BiLSTM captures bidirectional context semantics and, in combination with the attention mechanism, highlights key fault information. Finally, the LightGBM classifier is employed to reduce model complexity, enhance analysis efficiency, and increase the practicality of the method in engineering applications. An experimental analysis of fault data from the braking control system of the EMU indicates that the deep learning integration method can further improve diagnostic performance. Full article

Roller mills are commonly used in the production of mining derivatives, since one of their purposes is to reduce raw materials to very small sizes and to combine them. This research evaluates the mechanical condition of a mill containing four rollers, focusing on the largest cylindrical roller bearings as the main component that causes equipment failure. The objective of this work is to make a prognosis of when the overall vibrations would reach the maximum level allowed (2.5 IPS pk), thus enabling planned replacements, and achieving the maximum possible useful life in operation, without incurring unscheduled corrective maintenance and unexpected plant shutdown. Wireless sensors were used to capture vibration data and the ARIMA (Auto-Regressive Integrated Moving Average) and Holt–Winters methods were applied to forecast vibration behavior in the short term. Finally, the results demonstrate that the Holt–Winters model outperforms the ARIMA model in precision, allowing a 3-month prognosis without exceeding the established vibration limit. Full article

The turning of small-diameter deep holes is usually critical when the process of machining is unstable and the use of a special boring bar is often necessary. This paper is focused on the influence of cutting speed with a combination of cutting conditions such as feed and tool overhang on chatter marks, surface roughness and roundness of machined holes. In the experiment, two types of tool material for indexable boring bars were used, namely cemented carbide and steel. These are a group of boring bars used for the internal turning of holes of small diameters with indexable cutting inserts. Monolithic carbide boring bars are already used for internal turning of holes of even smaller diameters. Uncoated turning inserts made of cermet were used. The cutting tests were performed on the DMG CTX alpha 500 turning center. In the case of the steel boring bar, decreasing the cutting speed really led to an increase in the quality of the surface roughness and reduced the formation of chatter marks and large chatter marks. The cemented carbide boring bar also followed a similar trend, but it should be noted that the overall effect was not so great. This means that increasing the cutting speed makes the cutting process less stable and, vice versa, lower values of cutting speed reduce the formation of chatter marks and the related deterioration of the surface quality. The occurrence of chatter is directly related to the increase in the surface roughness parameters Ra and Rz of the machined surface. It can be stated that the dependence of roundness deviations on cutting speed values has a similar character to the results of the measured surface roughness values. Therefore, if the cutting speed is increased, it will make the cutting process less stable; this is also indirectly reflected in larger roundness deviations. However, it is necessary to state that this phenomenon can be observed in turning holes with small diameters using the steel boring bar, where the unstable cutting conditions materialized in the form of chatter marks. Full article

Motivated by the significant efforts developed by researchers and engineers to improve the economic and technical performance of microgrids (MGs), this paper proposes an Active Disturbance Rejection Control (ADRC) for Distributed Energy Resources (DER) in microgrids. This approach is a nonlinear control that is based on a real-time compensation of different estimated disturbances. The DER operates along with the electrical grid to provide the load requirements. This load has a nonlinear and uncertain character, which presents a source of unmodeled dynamics and harmonic perturbations of the MG. The main objective of this paper is to ensure the stability and the continuity of service of the distributed generation resources by controlling the DC-AC converter. The ADRC as a robust control technique is characterized by its ability to compensate for the estimated total disturbances caused by the load variation and the external unmodeled perturbations to guarantee the high tracking performance of sinusoidal reference signals in the DER system. The ADRC technique is characterized by its nonlinear function, which provides a high robustness to the controlled system. However, in order to simplify the control structure by keeping its high reliability, this paper proposes to replace the nonlinear function with a simple error (termed linear ADRC), compares the impact of this modification on the system performances, and evaluates its operation in the presence of linear and nonlinear load variations. Simulation results are presented to demonstrate the efficiency of the proposed control approach for a three-phase DER. Full article

Three-phase AC/DC pulse-width-modulated (PWM) converters have been widely employed in various renewable energy systems and industrial applications, which require a high-efficiency power converter operation. This article proposes a technique to reduce switching loss in AC/DC converters by integrating a voltage vector preselection strategy to model predictive virtual flux control. The voltage vector preselection strategy preselects available voltage vectors corresponding to switching states that lead to minimum switching loss in the phase leg, which conducts the highest current. By using preselected voltage vectors, clamping intervals are generated at every fundamental period to maintain the present switching states of the power switches, resulting in the reduction in switching loss. Additionally, by using virtual flux control, the proposed approach can effectively be used under both ideal and distorted source voltage conditions. The proposed method is compared with the conventional model predictive current control and the conventional model predictive virtual flux control. Both a simulation and experiment are performed to validate the correctness and effectiveness of the proposed method, which has been found to decrease the switching loss of an AC/DC converter by up to 15% compared to conventional control schemes at a negligible increase in the input current total harmonic distortion and DC output voltage ripple. Full article

Because the average number of missing people in our country is more than 20,000 per year, determining how to efficiently locate missing people is important. The traditional method of finding missing people involves deploying fixed cameras in some hotspots to capture images and using humans to identify targets from these images. However, in this approach, high costs are incurred in deploying sufficient cameras in order to avoid blind spots, and a great deal of time and human effort is wasted in identifying possible targets. Further, most AI-based search systems focus on how to improve the human body recognition model, without considering how to speed up the search in order to shorten the search time and improve search efficiency, which is the aim of this study. Hence, by exploiting the high-mobility characteristics of unmanned aerial vehicles (UAVs), this study proposes an integrated YOLOv5 and hierarchical human-weight-first (HWF) path planning framework to serve as an efficient UAV searching system, which works by dividing the whole searching process into two levels. At level one, a searching UAV is dispatched to a higher altitude to capture images, covering the whole search area. Then, the well-known artificial intelligence model YOLOv5 is used to identify all persons in the captured images and compute corresponding weighted scores for each block in the search area, according to the values of the identified human bodies, clothing types, and clothing colors. At level two, the UAV lowers its altitude to sequentially capture images for each block, in descending order according to its weighted score at level one, and it uses the YOLOv5 recognition model repeatedly until the search target is found. Two improved search algorithms, HWFR-S and HWFR-D, which incorporate the concept of the convenient visit threshold and weight difference, respectively, are further proposed to resolve the issue of the lengthy and redundant flight paths of HWF. The simulation results suggest that the HWF, HWFR-S, and HWFR-D search algorithms proposed in this study not only effectively reduce the length of a UAV’s search path and the number of search blocks but also decrease the search time required for a UAV to locate the search target, with a much higher search accuracy than the two traditional search algorithms. Moreover, this integrated YOLOv5 and HWF framework is implemented and tested in a real scenario to demonstrate its capability in enhancing the efficiency of a search and rescue operation. Full article

During the assembly of mechanical systems, the dual-arm robot is always used for cabin docking. In order to ensure the accuracy and reliability of cabin docking, a multi-objective trajectory planning method for the dual-arm robot was proposed. A kinematic model of the dual-arm robot was constructed based on the Denavit–Hartenberg (D-H) method firstly. Then, in the Cartesian space, the end trajectory of the dual-arm robot was confirmed by the fifth-order B-spline curve. On the basis of a traditional multi-objective cuckoo search algorithm, a modified cuckoo algorithm was built using the improved initial population generation method and the step size. The total consumption time and joint impact were selected as the objective functions, the overall optimal solution for the modified cuckoo algorithm was obtained using the normalized evaluation method. The optimal trajectory planning was achieved. Finally, the feasibility and effectiveness of the trajectory planning method were verified with the experiments. Full article

The use of lasers in various precise material removal processes has emerged as a viable and efficient alternative to traditional mechanical methods. However, the laser ablation of materials is a complex, multi-parameter process where scanning paths need to be repeated multiple times. This repetition causes changes in the absorption and temperature distribution along the scanning path, thereby affecting the accuracy of the ablation. Therefore, it is crucial to thoroughly study these phenomena. This article presents an experimental and numerical study on the laser ablation of bronze (DIN: 1705) in a multi-track ablation process. Specifically, six consecutive passes using a ns laser at three different energy densities were conducted. After each pass, measurements of the ablation depth and pile-up height were taken at three distinct points along the track (start, middle, and end) to evaluate the efficiency and quality of the process. To gain a deeper understanding of the underlying physical mechanisms, a numerical simulation model based on the Finite Element Method (FEM) was developed. The effective absorptivity was defined through reverse engineering, and the material’s cooling rates were also estimated. This study’s findings provide significant insights into the influence of machining parameters on the ablation process and its progression with varying numbers of consecutive repetitions. A primarily linear correlation was deduced between the ablation depth, energy density, and number of repetitions, while the relationship with the pile-up height appeared to be more ambiguous and nonlinear. The estimated cooling rates ranged from 10⁶ to 10¹⁰ [K/s]. Additionally, a heat accumulation phenomenon and a gradual temperature increase resulting from consecutive laser scans were also observed. A good agreement between the simulation results and experiments for the ablation depths was observed. Full article

In recent years, the rapid advancement of technology has caused an increase in the development of wearable products. These are portable devices that can be worn by people. The main goal of these products is to improve the quality of life as they focus on the safety, assistance and entertainment of their users. The introduction of many new technologies has allowed these products to evolve into many different fields with multiple uses. The way in which the design of wearable products/devices is approached requires the study and recording of multiple factors so that the final device is functional and efficient for its user. The current research presents an in-depth overview of research studies dealing with the development, design and manufacturing of wearable products/devices and applications/systems in general. More specifically, in this review, a comprehensive classification of wearable products/devices in various sectors and applications was carried out, resulting in the creation of eight different categories. A total of 161 studies from the last 13 years were analyzed and commented on. The findings of this review show that the use of new technologies such as 3D scanning and 3D printing are essential tools for the development of wearable products. In addition, many studies observed the use of various sensors through which multiple signals and data could be recorded. Finally, through the eight categories that the research studies were divided into, two main conclusions emerged. The first conclusion is that 3D printing is a method that was used the most in research. The second conclusion is that most research directions concern the safety of users by using sensors and recording anthropometric dimensions. Full article

This paper presents control design and implementation for a power-assisted mobile trainer that employs neuro-developmental treatment (NDT) principles. NDT is a gait rehabilitation technique for stroke patients that provides minimum intervention at critical gait events. Traditional NDT rehabilitation is an effective post-stroke treatment but is also time consuming and labor intensive for therapists. Therefore, we designed a mobile NDT trainer to automatically repeat therapists’ intervention patterns, allowing patients to receive sufficient training without increasing therapists’ workloads. Because the trainer was self-propelled, it could cause burdens to stroke patients with limited muscle strength, thereby potentially degrading the rehabilitation effects. Hence, this paper proposes a power-assisted device that can let the mobile trainer follow the user, allowing the subject to focus on the rehabilitation training. We conducted system identification and control design for the power-assisted NDT trainer. We then implemented the designed controllers and tested the trainer. Finally, we invited 10 healthy subjects and 12 stroke patients to conduct clinical experiments. After using the power-assisted NDT trainer, most participants exhibited improvements in swing-phase symmetry, pelvic rotation, and walking speed. Based on the results, the power-assisted device was deemed effective in facilitating stroke rehabilitation. Full article

The Proportional Hazards Model (PHM) under a Condition-Based Maintenance (CBM) policy is used by asset-intensive industries to predict failure rate, reliability function, and maintenance decisions based on vital covariates data. Cox’s partial likelihood optimization is a method to assess the weight of time and conditions into the hazard rate; however, parameter estimation with diverse covariates problem could have multiple and feasible solutions. Therefore, the boundary assessment and the initial value strategy are critical matters to consider. This paper analyzes innovative non/semi-parametric approaches to address this problem. Specifically, we incorporate IPCRidge for defining boundaries and use Gradient Boosting and Random Forest for estimating seed values for covariates weighting. When applied to a real case study, the integration of data scaling streamlines the handling of condition data with diverse orders of magnitude and units. This enhancement simplifies the modeling process and ensures a more comprehensive and accurate underlying data analysis. Finally, the proposed method shows an innovative path for assessing condition weights and Weibull parameters with data-driven approaches and advanced algorithms, increasing the robustness of non-convex log-likelihood optimization, and strengthening the PHM model with multiple covariates by easing its interpretation for predictive maintenance purposes. Full article

Flexible shrouded blades are commonly adopted in the last stages of steam turbines where complicated dynamical behavior can be induced by dry friction force generated on contacting interfaces between adjacent shrouds and the geometric nonlinearity due to the structural flexibility of the blades. In this paper, combination resonance caused by contact and friction forces generated on shroud interfaces is investigated, which is a concurrence of 1:3 internal resonance involving the first and second modes in the flapwise direction and the primary resonance of the first flapwise mode. The stiffness and damping at the contact interface are obtained by linearizing the contact and friction forces between shrouds through the harmonic balance method. The vibrating blade is modeled as a beam with a concentrated mass of which the responses under the combination resonance are solved through the multiple-scale method. Sensitivities of response with respect to the angle of shrouds, contact stiffness and rotation speed are illustrated, and the influences of these parameters on the periodicity and amplitudes of steady responses are demonstrated. The parametric regions where the combination resonance occurs are pointed out. Finally, parametric analyses are presented to show how the amplitude–frequency relation of the multiple-scale solutions under the combination resonance vary with detuning and design parameters. The present research provides a designing basis for improving the dynamic performance of flexible shrouded blades and suppressing vibrations of blades by adjusting structural parameters in practical engineering. Full article

This paper proposes an electro-hydraulic vibration control system based on the single-neuron PID algorithm, which improves the operating frequency of the electro-hydraulic fatigue testing machine and the control accuracy of the load force. Through mathematical modeling of the electro-hydraulic vibration system (EVS), a MATLAB/Simulink simulation, and experimental testing, this study systematically analyzes the output waveform of the EVS as well as the closed-loop situation of load force amplitude and offset under the action of the single-neuron PID algorithm. The results show that: the EVS with a 2D vibration valve as the core, which can control the movement of the spool in the two-degrees-of-freedom direction, can realize the output of an approximate sinusoidal load force waveform from 0 to 800 Hz. The system controlled by the single-neuron PID algorithm is less complex to operate than the traditional PID algorithm. It also has a short rise time for the output load force amplitude curve and a maximum control error of only 1.2%. Furthermore, it exhibits a rapid closed-loop response to the load force offset. The range variability of the load force is measured to be 1.43%. A new scheme for the design of EVS is provided in this study, which broadens the application range of electro-hydraulic fatigue testing machines. Full article

As manufacturing and assembly processes continue to require more adaptable systems for automated handling, innovative solutions for universal gripping are emerging. These grasping systems can enable the handling of wide varieties of shapes, with gripping forces varying with grasped geometries. For the efficient usage of handling systems, precise offline and online prediction models for resulting grasping forces for different objects are necessary. In previous research, a flexible vacuum-based granular gripper was developed, for which no option for predicting gripping forces is currently available. Various gripping force prediction methodologies within the current state of the art are examined and evaluated. For an assessment of grasping forces of previously untested objects for the examined gripper with limited data and low computational effort, two methodologies are proposed. An analytical, 2D-geometry-derived gripper-specific metric for geometries is compared to a methodology based on similarities of objects to a small existing dataset. The applicability and prediction quality for different object types is analyzed through validation experiments. Gripping force estimations are possible with both methodologies, with individual weaknesses towards geometric features such as air permeabilities. With further development, robust predictions of gripping forces could be achieved for a wide range of unknown object geometries with limited experimental effort. Full article

Generally, windows in greenhouses are automatically opened and closed to regulate the internal temperature. However, because the air outside during the winter in Japan is dry, opening windows to reduce the temperature causes the humidity deficit to increase above 6 g/m³, thereby inhibiting plant growth. Therefore, in this study, we developed a model that considers the effects of weather and the sampling period using a subspace (N4SID) method based on environmental data from inside and outside a greenhouse during winter. By adopting a data-driven model, models for greenhouse temperature and humidity deficits can be constructed conveniently. First, four models incorporating weather conditions were constructed over a 28-day modelling period. Moreover, the average root mean square error values from 8:00 to 16:00 during the 10-day model evaluation period were examined. Subsequently, model predictive controllers were developed from the four models with sampling periods of 1, 2, 4, and 8 min, and their performances were compared over the model evaluation period. The model predictive controller with a sampling period of 4 min was the most energy-efficient, achieving control of the humidity deficit of up to at most 6 g/m³ (close to the target value of 4.5 g/m³) while maintaining the target temperature of 26 °C. Full article

Traditionally, condition monitoring of wind turbines has been performed manually by certified rope teams. This method of inspection can be dangerous for the personnel involved, and the resulting downtime can be expensive. Wind turbine inspection can be performed using autonomous drones to achieve lower downtime, cost, and health risks. To enable autonomy, the field of drone path planning can be assisted by this research, namely machine learning that detects wind turbines present in aerial RGB images taken by the drone before performing the maneuvering for turbine inspection. For this task, the effectiveness of two deep learning architectures is evaluated in this paper both without and with a proposed fuzzy contrast enhancement (FCE) image preprocessing algorithm. Efforts are focused on two convolutional neural network (CNN) variants: VGG19 and Xception. A more traditional approach involving support vector machines (SVM) is also included to contrast a machine learning approach with our deep learning networks. The authors created a novel dataset of 4500 RGB images of size $210\times 210$ to train and evaluate the performance of these networks on wind turbine detection. The dataset is captured in an environment mimicking that of a wind turbine farm, and consists of two classes of images: with and without a small-scale wind turbine (12V Primus Air Max) assembled at Utah Valley University. The images were used to describe in detail the analysis and implementation of the VGG19, Xception, and SVM algorithms using different optimization, model training, and hyperparameter tuning technologies. The performances of these three algorithms are compared in depth alongside those augmented using the proposed FCE image preprocessing technique. Full article