Machines

Motor faults not only damage the motor body but also affect the entire production system. When the motor runs in a steady state, the characteristic frequency of the fault current is close to the fundamental frequency, so it is difficult to effectively extract the fault current components, such as the broken rotor bar. In this paper, according to the characteristics of electromagnetic force and vibration, when the rotor eccentricity and the broken bar occur, the vibration signal is used to analyze and diagnose the fault. Firstly, the frequency, order, and amplitude characteristics of electromagnetic force under rotor eccentricity and broken bar fault are analyzed. Then, the fault vibration acceleration value collected by a one-dimensional dilated convolution pair is extracted, and the SeLU activation function and residual connection are introduced to solve the problem of gradient disappearance and network degradation, and the fault motor model is established by combining average ensemble learning and SoftMax multi-classifier. Finally, experiments of normal rotor eccentricity and broken bar faults are carried out on 4-pole asynchronous motors. The experimental results show that the accuracy of the proposed method for motor fault detection can reach 99%, which meets the requirements of fault motor detection and is helpful for further application. Full article

This paper presents an analysis of the risk of neck injury in vehicle occupants as a consequence of an impact. A review of the formulation of indexes that are used in the assessment and investigation of neck injury risk is discussed with the aim of providing a new, more appropriate index using suitable sensorized equipment. An experimental analysis is proposed with a new driver monitoring device using low-cost sensors. The system consists of wearable units for the head, neck, and torso where inertial measurement sensors (IMU) are installed to record data concerning the occupant’s head, neck, and torso accelerations while the vehicle moves. Two laser infrared distance sensors are also installed on the vehicle’s steering wheel to record the position data of the head and neck, as well as an additional IMU for vehicle acceleration values. To validate both the device and the new index, experiments are designed in which different sensorized volunteers reproduce an emergency braking maneuver with an instrumented vehicle at speeds of 10, 20, and 30 km/h before the beginning of any braking action. The neck is particularly sensitive to sudden changes in acceleration, so a sudden braking maneuver is enough to constitute a potential risk of cervical spine injury. During the experiments, large accelerations and displacements were recorded as the test speed increased. The largest accelerations were obtained in the experimental test at a speed of 30 km/h with values of 19.17, 9.57, 9.28, and 5.09 m/s² in the head, torso, neck, and vehicle, respectively. In the same experiment, the largest displacement of the head was 0.33 m and that of the neck was 0.27 m. Experimental results have verified that the designed device can be effectively used to characterize the biomechanical response of the neck in car impacts. The new index is also able to quantify a neck injury risk by taking into account the dynamics of a vehicle and the kinematics of the occupant’s head, neck, and torso. The numerical value of the new index is inversely proportional to the acceleration experienced by the vehicle occupant, so that small values indicate risky conditions. Full article

Within the domain of robotic non-destructive testing (NDT) of complex structures, the existing methods typically utilise an offline robot-path-planning strategy. Commonly, for robotic inspection, this will involve full coverage of the component. An NDT probe oriented normal to the component surface is deployed in a raster scan pattern. Here, digital models are used, with the user decomposing complex structures into manageable scan path segments, while carefully avoiding obstacles and other geometric features. This is a manual process that requires a highly skilled robotic operator, often taking several hours or days to refine. This introduces several challenges to NDT, including the need for an accurate model of the component (which, for NDT inspection, is often not available), the requirement of skilled personnel, and careful consideration of both the NDT inspection method and the geometric structure of the component. This paper addresses the specific challenge of scanning complex surfaces by using an automated approach. An algorithm is presented, which is able to learn an efficient scan path by taking into account the dimensional constraints of the footprint of an ultrasonic phased-array probe (a common inspection method for NDT) and the surface geometry. The proposed solution harnesses a digital model of the component, which is decomposed into a series of connected nodes representing the NDT inspection points within the NDT process—this step utilises graph theory. The connections to other nodes are determined using nearest neighbour with KD-Tree optimisation to improve the efficiency of node traversal. This enables a trade-off between simplicity and efficiency. Next, movement restrictions are introduced to allow the robot to navigate the surface of a component in a three-dimensional space, defining obstacles as prohibited areas, explicitly. Our solution entails a two-stage planning process, as follows: a modified three-dimensional flood fill is combined with Dijkstra’s shortest path algorithm. The process is repeated iteratively until the entire surface is covered. The efficiency of this proposed approach is evaluated through simulations. The technique presented in this paper provides an improved and automated method for NDT robotic inspection, reducing the requirement of skilled robotic path-planning personnel while ensuring full component coverage. Full article

Existing transportation infrastructure and traffic control systems face increasing strain as a result of rising demand, resulting in frequent congestion. Expanding infrastructure is not a feasible solution for enhancing the capacity of the road. Hence, Intelligent Transportation Systems are often employed to enhance the Level of Service (LoS). One such approach is Variable Speed Limit (VSL) control. VSL increases the LoS and safety on motorways by optimizing the speed limit according to the traffic conditions. The proliferation of Connected and Autonomous Vehicles (CAVs) presents fresh prospects for improving the operation and measurement of traffic states for the operation of the VSL control system. This paper introduces a method for the detection of multiple congested areas that is used for state estimation for a dynamically positioned VSL control system for urban motorways. The method utilizes Q-Learning (QL) and CAVs as mobile sensors and actuators. The proposed control approach, named Congestion Detection QL Dynamic Position VSL (CD-QL-DPVSL), dynamically detects all of the congested areas and applies two sets of actions, involving the dynamic positioning of speed limit zones and imposed speed limits for all detected congested areas simultaneously. The proposed CD-QL-DPVSL control approach underwent an evaluation across six distinct traffic scenarios, encompassing CAV penetration rates spanning from $10\%$ to $100\%$ and demonstrated a significantly better performance compared to other control approaches, including no control, rule-based VSL, two Speed-Transition-Matrices-based QL-VSL configurations with fixed speed limit zone positions, and a Speed-Transition-Matrices-based QL-DVSL with a dynamic speed limit zone position. It achieved enhancements in macroscopic traffic parameters such as the Mean Travel Time and Total Time Spent by adapting its control policy to every simulated scenario. Full article

Ceramic waveguide components play a critical role in modern microwave semiconductor systems. For the first time, this work reports experimental results obtained when dielectric ceramics are abrasive-machined into waveguide components. This process will cause subsurface damage (SSD), resulting in a deviation in their working frequency which can degrade the performance of the system. For a substrate-integrated waveguide (SIW) resonator working at 10.1 GHz, SSD with a depth of 89 um can cause a maximum frequency offset of 20.2%. For a mm wave component working at 70 GHz, the corresponding frequency offset could increase to 169%. Three resonator SIW filters with SSD are studied, and the results demonstrate that the frequency offset induced by SSD can reduce the pass rate of the filters from 95.4% to 0%. A theoretical analysis is performed to reveal the mechanism and to offer a quantitative estimation of the limiting range of the offset caused by SSD. Feasible methods for reducing the offset caused by SSD, such as structure design, processing optimization, and material reinforcement, are discussed. Full article

We introduce the notion of the Lie derivative in the context of dual quaternions that represent rigid motions and twists. First we define the wrench in terms of dual quaternions. Then we show how the Lie derivative helps understand how actuators affect an end effector in parallel robots, and make it explicit in the two cases case of Stewart Platforms, and cable-driven parallel robots. We also show how to use Lie derivatives with the Newton-Raphson Method to solve the forward kinematic problem for over constrained parallel actuators. Finally, we derive the equations of motion of the end effector in dual quaternion form, which include the effect of inertia from the actuators. Full article

Gears are the most important parts of a rotary system, and they are used for mechanical power transmission. The health monitoring of such a system is needed to observe its effective and reliable working. An approach that is based on vibration is typically utilized while carrying out fault diagnostics on a gearbox. Using the Fourier–Bessel series expansion (FBSE) as the basis for an empirical wavelet transform (EWT), a novel automated technique has been proposed in this paper, with a combination of these two approaches, i.e., FBSE-EWT. To improve the frequency resolution, the current empirical wavelet transform will be reformed utilizing the FBSE technique. The proposed novel method includes the decomposition of different levels of gear crack vibration signals into narrow-band components (NBCs) or sub-bands. The Kruskal–Wallis test is utilized to choose the features that are statistically significant in order to separate them from the sub-bands. Three classifiers are used for fault classification, i.e., random forest, J48 decision tree classifiers, and multilayer perceptron function classifier. A comparative study has been performed between the existing EWT and the proposed novel methodology. It has been observed that the FBSE-EWT with a random forest classifier shows a better gear fault detection performance compared to the existing EWT. Full article

Discontinuous pulse-width-modulation (DPWM) methods have been extensively used in the industrial area to reduce overall losses, which decreases the corresponding thermal stress on the power switches of converters. However, local thermal overload can arise due to different aging conditions of semiconductor devices or failure in the cooling system. This leads to reduced reliability of the converter system due to the low expected lifespan of the most aged switches or phase legs. In this paper, the modified DPWM strategies for independent control of per-phase switching loss are introduced to deal with this matter. The proposed per-phase DPWM techniques are generated by modifying the conventional three-phase DPWM methods for reducing the switching loss in a specific leg, whereas the output performance is not degraded. This paper reports on output performance, including output current total harmonic distortion (THD) and power loss of switching devices, analysis for the various modified DPWM strategies for independent control of per-phase switching loss, which is applicable in 2-level 3-phase voltage source inverters (2L3P VSIs). The results are compared to the corresponding continuous PWM technique to verify and analyze the effectiveness and accuracy of the modified DPWM strategies for independent control of per-phase switching loss. Full article

Unequal thermal stress among the phase legs of a multiphase converter leads to a reduction in the useful lifespan and reliability of that converter in general. Increasing the converter’s lifespan by relieving the stressed phase leg, which suffers excessive thermal stress due to aging, is crucial. This paper evaluates two control concepts, including two per-phase model predictive control methods for extending the lifespan of a voltage source inverter. These two per-phase techniques alter the switching pattern to reduce the losses of the most aged phase leg. Hence, the loss and the corresponding thermal stress of the leg that has aged the most are reduced. In such a way, the lifespan and reliability of the converter are prolonged. Two per-phase model predictive control techniques are executed in both simulation and experiment environments, where the corresponding results are provided to evaluate the behavior of these control strategies, considering several operational aspects both in steady state and transient operation. In addition to static load conditions, two per-phase techniques are verified for the correct operation under dynamic load (induction motor) conditions. Full article

Resonance demodulation of vibration signals is a common method for extracting fault information from rolling bearings. Nonetheless, demodulation quality is dependent on frequency band location. Established methods such as the Fast Kurtogram, Autogram, SKRgram, etc. have achieved satisfactory results in some cases, but the results are not good in the presence of strong white Gaussian noise and random impulses. To solve these issues, an algorithm that selects the optimal demodulation frequency band (ODFB) based on the ratio of the logarithmic envelope spectrum Gini coefficient (LESGIRgram) is proposed. The core idea of this paper is to capture the difference between the LESGIgrams of health and fault signals and accordingly locate the frequency bands that contain the most fault information. Initially, the baseline is constructed by calculating the logarithmic envelope spectrum Gini coefficient matrix of the health bearing (LESGI_baseline). Next, the LESGI matrix of the fault bearing (LESGI_measured) is computed. The ratio of LESGI_measured to LESGI_baseline is calculated, and the ODFB can be selected with the maximum LESGIR. The fault signal is then filtered using this derived ODFB, and envelope analysis is performed to extract fault features. The proposed algorithm for detecting rolling bearing faults has been verified for accuracy and effectiveness through simulation and experimental data. Full article

This paper presents a novel adaptive tracked robot equipped with passive swing arms for overcoming obstacles. First, the paper introduces the overall composition of the robot and focuses on the adaptive mechanism of the passive swing arms. Second, analyzing the single-step obstacle-overcoming process of the robot reveals the relationship between the obstacle height and the geometric parameters of the passive swing arms, establishing a kinematic model. Then, a dynamic model of the robot’s obstacle-overcoming process is established by simplifying the robot into a crank–slider linkage, and the time range for the robot to overcome obstacles is analyzed. Finally, through virtual simulation and a physical prototype, the feasibility and maneuverability of the robot’s design are verified. These findings demonstrate the potential of the robot in various applications, such as search and rescue missions and homeland security. Full article

The rise of smart factories and warehouses has ushered in an era of intelligent manufacturing, with autonomous robots playing a pivotal role. This study focuses on improving the navigation and control of autonomous forklifts in warehouse environments. It introduces an innovative approach that combines a modified Linear Segment with Parabolic Blends (LSPB) trajectory planning with Model Predictive Control (MPC) to ensure efficient and secure robot movement. To validate the performance of our proposed path-planning method, MATLAB-based simulations were conducted in various scenarios, including rectangular and warehouse-like environments, to demonstrate the feasibility and effectiveness of the proposed method. The results demonstrated the feasibility of employing Mecanum wheel-based robots in automated warehouses. Also, to show the superiority of the proposed control algorithm performance, the navigation results were compared with the performance of a system using the PID control as a lower-level controller. By offering an optimized path-planning approach, our study enhances the operational efficiency and effectiveness of Mecanum wheel robots in real-world applications such as automated warehousing systems. Full article

Crop height is a vital indicator of growth conditions. Traditional drone image-based crop height measurement methods primarily rely on calculating the difference between the Digital Elevation Model (DEM) and the Digital Terrain Model (DTM). The calculation often needs more ground information, which remains labour-intensive and time-consuming. Moreover, the variations of terrains can further compromise the reliability of these ground models. In response to these challenges, we introduce G-DMD, a novel method based on Gated Recurrent Units (GRUs) using DEM and multispectral drone images to calculate the crop height. Our method enables the model to recognize the relation between crop height, elevation, and growth stages, eliminating reliance on DTM and thereby mitigating the effects of varied terrains. We also introduce a data preparation process to handle the unique DEM and multispectral image. Upon evaluation using a cotton dataset, our G-DMD method demonstrates a notable increase in accuracy for both maximum and average cotton height measurements, achieving a 34% and 72% reduction in Root Mean Square Error (RMSE) when compared with the traditional method. Compared to other combinations of model inputs, using DEM and multispectral drone images together as inputs results in the lowest error for estimating maximum cotton height. This approach demonstrates the potential of integrating deep learning techniques with drone-based remote sensing to achieve a more accurate, labour-efficient, and streamlined crop height assessment across varied terrains. Full article

Brake squeal—an audible high-frequency noise phenomenon in the range between 1 kHz and 15 kHz resulting from self-excited vibrations—is one of the main cost drivers while developing brake systems. Increasing damping is often a crucial factor in the context of self-excited vibrations. Countermeasures applied for preventing brake squeal have been investigated particularly for disk brakes in the past. However, in recent years, drum brakes have once again become more important, partly because of the issue of particle emissions. Concerning noise problems, drum brakes have a decisive advantage compared to disk brake systems in that the outer drum surface is freely accessible for applying damping devices. This paper focuses on the fundamental proving and evaluation of passive damping measures on a simplex drum brake system. To obtain a detailed understanding of the influence of additional damping on the squealing behavior of drum brakes, extensive experimental investigations are performed on a brake with an intentionally introduced high squealing tendency in the initial configuration. This made it possible to investigate the influence of different types of damping measures on their effectiveness. Techniques from the field of big data analysis and machine learning are tested to detect squeal in measured time series data. These techniques were remarkably reliable and made it possible to detect squeal efficiently even in data that was not generated on a traditional costly NVH brake dynamometer. To investigate whether the simulation method usually used for the simulation of brake squeal is applicable to depicting the influence of additional damping in drum brakes, a complex eigenvalue analysis was performed with Abaqus, and the results were compared with those from the experiments. Full article

In this paper, a simple and effective self-adaptive material interpolation scheme is proposed to solve the numerical instability problem, which may occur in topology optimization considering geometrical nonlinearity when using density-based method. The primary concept of the proposed method revolves around enhancing the deformation resistance of minimum-density or intermediatedensity elements, thus avoiding numerical instability due to excessive distortion of these elements. The proposed self-adaptive material interpolation scheme is based on the power law method, and the stiffness of minimum-density or intermediate-density elements can be adjusted by a single parameter, α. During the optimization process, the parameter α will be changed according to an adaptive adjustment strategy to ensure that elements within the design domain are not excessively distorted, while the mechanical behavior of the structure can be approximated with acceptable accuracy. Numerical examples of minimizing compliance and maximizing displacement of structure are given to prove the validity of the proposed self-adaptive material interpolation scheme. Full article

This paper proposes a new rapid and straightforward method along with a related device for finding the three basic parameters of an actual external involute spur gear. The number of teeth is easily counted, but the other two parameters—the module and the coefficient of profile shift—are difficult to identify. The method is based on the principle of inspection of the precision of gear teeth, using the dimension over pins, when the maximum distance is measured between the lateral surfaces of two cylindrical rollers of well-controlled dimensions, placed into the spaces between teeth. The dimension over pins is applied as a function of the number of teeth (odd or even) and requires experience (and this is the main disadvantage of the method) for finding the correct maximum distance between pins. The new method eliminates this drawback as it proposes a measuring scheme where four identical rollers are used in a designed inspection device. The system is statically determinate and, therefore, the dimension to be measured is unequivocally found. A new relation for the dimension to be measured is deduced and allows for finding the module and the coefficient of profile shift. The inspection device is described and a concrete case is presented for exemplifying the methodology. A further application permits finding the centre distance for an external spur gearing. Unlike the classical technique where the centre distance is obtained based on the centring surfaces of the wheels, the new method implies only dimensions measured through flank measurements, thus eliminating errors introduced by the deviations between the flanks and the centring surfaces of the wheels. Full article

In complex work domains, not all possible faults can be anticipated by designers or handled by automation. Humans therefore play an important role in fault diagnosis. To support their diagnostic reasoning, it is necessary to understand the requirements that diagnosticians face. While much research has dealt with identifying domain-general aspects of fault diagnosis, the present exploratory study examined domain-specific influences on the requirements for diagnosticians. Scenario-based interviews were conducted with nine experts from two domains: the car domain and the packaging machine domain. The interviews revealed several factors that influence the requirements for successful fault diagnosis. These factors were summarized in five categories, namely domain background, technical system, typical faults, diagnostic process, and requirements. Based on these factors, we developed the Domain Requirements Model to predict requirements for diagnosticians (e.g., the need for empirical knowledge) from domain characteristics (e.g., the degree to which changes in inputs are available as domain knowledge) or characteristics of the diagnostic process (e.g., the extent of support). The model is discussed considering the psychological literature on fault diagnosis, and first insights are provided that show how the model can be used to predict requirements of diagnostic reasoning beyond the two domains studied here. Full article

A macro–micro dual-drive motion platform is a class of key system utilized in ultra-precision instruments and equipment for realizing ultra-high-precision positioning, which relates to the fields of semiconductor manufacturing, ultra-precision testing and machining, etc. Aiming at the ultra-high-precision positioning control problem of macro–micro dual-drive systems containing mechanical backlash, this paper analyzes the combined effect of mechanical coupling and backlash, and proposes a macro–micro compound control strategy. Firstly, the system dynamic model, including mechanical coupling, is established, and a quasi-linear backlash model is also proposed. Secondly, based on the above model, a stepwise nonlinear identification method is proposed to obtain the backlash characteristic online, which is the basis of accurate backlash compensation. Then, for the macro–micro structure containing the backlash, a macro decoupling control method, combined with a micro adaptive integral sliding mode control method and backlash compensation, are designed coordinately to guarantee that the large-stroke macro–micro cooperative motion reaches micron-level accuracy. Moreover, the boundary of the positioning error is adjustable by tuning the controller parameters. Finally, both the simulation and experimental results demonstrate that the proposed identification method can estimate the time-varying backlash precisely in finite time, and the system positioning accuracy can achieve an average 20 μm with long stroke and backlash influence, which is much higher than that using the traditional method and provides theoretical guidance for high-precision positioning control of a class of dual-drive motion platform. Full article

This paper proposes a B-spline trajectory algorithm to realize multi-axis trajectory interpolation and analyzes the operating accuracy in an embedded system. However, the existing trajectory generation method needs to use computer-aided manufacturing (CAM) software to convert the interpolating trajectory into G code and download the code into the computer numerical control (CNC) system for processing. In this paper, the method of third-degree B-spline interpolation is proposed to generate a curved surface trajectory, and the trajectory generated by this algorithm can be run directly into a CNC system. The precision analysis of the ISO parameter segmentation interpolation algorithm and the theory of constant velocity motion is also presented. The significance of this project is that it designs a complete set of embedded systems, including hardware circuit design and software logic design, and uses low-cost STM32 architecture to realize a B-spline constant-speed interpolation algorithm, which is verified on CNC polishing equipment. A simulation conducted with the MATLAB software and the B-spline curve interpolation experiments performed on a multi-axis polishing machine tool demonstrate the effectiveness and accuracy of the optimized third-degree B-spline algorithm. Full article