Complex Mechanical Structure Research Articles

A Data-Driven Model with Hysteresis Compensation for I2RIS Robot.

Retinal microsurgery is a high-precision surgery performed on a delicate tissue requiring the skill of highly trained surgeons. Given the restricted range of instrument motion in the confined intraocular space, snake-like robots may prove to be a promising technology to provide surgeons with greater flexibility, dexterity, and positioning accuracy during retinal procedures such as retinal vein cannulation and epiretinal membrane peeling. Kinematics modeling of these robots is an essential step toward accurate position control. Unlike conventional manipulators, modeling these robots does not follow a straightforward method due to their complex mechanical structure and actuation mechanisms. The hysteresis problem can especially impact the positioning accuracy significantly in wire-driven snake-like robots. In this paper, we propose a data-driven kinematics model using a probabilistic Gaussian mixture model (GMM) and Gaussian mixture regression (GMR) approach with a hysteresis compensation algorithm. Experimental results on the two-degree-of-freedom (DOF) integrated robotic intraocular snake (I2RIS) show that the proposed model with the hysteresis compensation can predict the snake tip bending angle for pitch and yaw with 0.45° and 0.39° root mean square error (RMSE), respectively. This results in overall 60% and 70% improvements of accuracy for yaw and pitch over the same model without the hysteresis compensation.

Isogeometric Size Optimization Design Based on Parameterized Volume Parametric Models

Traditional structural optimization design methods are based on the finite element analysis(FEA), which makes it difficult to construct a direct relationship between the design parameters and the design objective parameters in the structural design process. The FEA method needs to convert the models back and forth between the design model and the analysis or optimization model during the design process. It is a cumbersome and time-consuming work and also affects the analysis accuracy. We propose an integrated design method that seamlessly integrates process of design, simulation and optimization based on uniformity of design models, analysis models and optimization models by benefiting the advantages of volume parameterization and isogeometric analysis(IGA). The size parameters are input as high-level parameters, then the middle parameters are obtained through hierarchical mapping. Based on these parameters, the semantic feature framework composes of feature points, feature curves and feature surfaces and even feature volume is gradually constructed. By extracting paths and sections, the geometric feature framework is generated. The paths and sections are segmented to form the volume parametric sub-patches through volume parametric mapping. These sub-patches are merged into a whole volume parametric model that can be used for IGA and size driven deformation. Based on volume parametric model, a mathematical relationship is constructed between the design objective parameters and the size design parameters. Through the mathematical relationship, the sensitivity equations are derived for sensitivity analysis. Finally, an isogeometric size optimization process is complete. Thus, an integration of design process including geometric modeling, performance analysis, and structural optimization is achieved. Taking the maximum stiffness and the minimum stress as the size optimization objectives, the integrated design examples fall into four groups including single size optimization, multi sizes non-coupled optimization, multi sizes coupled optimization, and complex mechanical structure optimization. The optimization results prove that our method is effective, and it can be applied on complex mechanical parts. The designed results do not require reconstruction, thus achieving the integrated and optimized design of mechanical structures.

A novel mechanical fault diagnosis for high-voltage circuit breakers with zero-shot learning

In recent years, data-driven methods have been widely used in the field of high-voltage circuit breakers (HVCBs) fault diagnosis. However, due to the complex mechanical structure of HVCBs and the special operating environment, it is difficult to obtain a large amount of fault samples and exhaust all fault types. The lack of fault samples and fault types typically results in significant degradation of diagnostic performance. To address this problem, we design a novel method named R-MLL for zero-shot HVCB fault diagnosis. R-MLL tries to identify unseen fault types only by training seen fault types. First, to focus on all the details of the HVCB mechanical vibration signal, the wavelet transform is used to multi-scale refine the fault data. Second, a new network (RDSCNN) is designed to extract multidimensional features based on convolutional neural network incorporating residual block and depthwise separable convolution. Third, a multi-label attribute learning network is designed, enabling the fusion of fault features and attributes and allowing attribute labels to assist fault diagnosis tasks. Extensive experiments show that R-MLL gets average accuracy of 86.2% for compound fault diagnosis without the need of using target fault samples for building the diagnostic model. Comparisons with a number of state-of-the-art techniques show the superiority of the proposed method for zero-shot HVCB diagnosis.

Extended component mode synthesis method for dynamic analysis of mechanical systems with local nonlinearities

Engineering structures are generally designed based on linear elasticity assumptions. However, it is difficult to describe the connected structures using a simple linear system, due to factors such as sliding and friction at interfaces, and variations in contact areas during vibration processes. Therefore, it is necessary to develop an approach to tackle such systems with interface nonlinearities. In this paper, we proposed an extended free-interface component mode synthesis method. According to the proposed method, the substructures are separated at the nonlinear interfaces. Then the component mode synthesis procedure is carried out. By reasonably neglecting the contributions of higher order modes to the damping and inertia terms, it is demonstrated that the nonlinear interface forces and their time derivatives can be expressed as functions of themselves, as well as modal displacements and velocities. This characteristic facilitates the synthesis of the reduced order models for substructures in the state space. Also owing to this operation, the original properties of nonlinear interface forces can be preserved as much as possible, without any linearization. This method is suitable for non-proportionally damped structures with local nonlinearities such as mechanical structures with nonlinear spring and dashpot connections, rotor-bearing systems and so on. It also may be an alternative for the dynamic analysis of jointed structures with contact nonlinearities. Two numerical examples are presented to demonstrate the capability of the proposed method: (1) an eighteen degrees of freedom spring-dashpot-mass system with cubic spring and cubic dashpot interface connections is studied and (2) a more complex mechanical structure: a dual-rotor system with deep-groove ball bearing inter-shaft support. The numerical simulation results indicate that the dynamic responses of the reduced order model match very well with that of the full model, revealing the high accuracy of the proposed method with low computational costs.

Study on the mechanism of buffer absorbing energy of double-layer heterostructure based on viscoelastic materials for MEMS devices

Under the high overload impact environment, MEMS devices are prone to failure due to their complex mechanical structure. Therefore, improving the sensor's resistance to high overload is crucial to ensure its normal service. The dynamic response model of the double-layer heterostructure is established based on high-impact accelerometers and second-order oscillation systems. The peak attenuation ratio of the inertial load was obtained to characterize the buffering and energy absorption properties of the structure. Through Ls-Dyna numerical simulation and overload impact tests, it was verified that the error was less than 6.67%. The double-layer heterostructure with an outer epoxy resin and inner polyurethane exhibited the best buffering performance among the tested structures, with a maximum improvement of 35.6% compared to the single-layer structure. The peak attenuation ratio of inertial load can reach 44%. These findings provide a theoretical foundation and technical support for the research and application of protective structures for MEMS sensors against high overload.

Remaining useful life prediction method combining the life variation laws of aero-turbofan engine and auto-expandable cascaded LSTM model

The real-time status monitoring and health management of aero-turbofan engines (ATEs) can effectively reduce the risk of engine failure and ensure aircraft flight safety. Accurate prediction of the remaining useful life (RUL) of ATE is a vital tool for effectively monitoring the engine operating condition, in which long-short term memory (LSTM) networks are often applied to RUL prediction. However, because of the complex mechanical structure and operation mode of the aero-engine, the prediction accuracy of the LSTM network is not enough to meet the actual demand. This paper proposes an auto-expandable cascaded long-short term memory (ACLSTM) prediction model that incorporates the lifetime variation laws of ATE, which is mainly applied for the degradation assessment of ATEs and the accurate prediction of RUL. The ACLSTM model adopts the network structure of multiple LSTM modules connected step by step to continuously set the prediction error of the previous module as the training outputs of the latter module. This data processing method transforms the prediction process of the original data into that of the output error, effectively reducing the prediction error and improving the prediction effect. In addition, to further improve the prediction accuracy, this paper comprehensively proposes several empirical formulas for further correction of the prediction effect obtained by the ACLSTM model. In the experimental part, the prediction effectiveness of the proposed method is tested based on four subsets of the C-MAPSS dataset published by the National Aeronautics and Space Administration. The experimental results on the four datasets show that the root mean square error (RMSE) of the ACLSTM prediction model decreases by 95.44% on average compared to the traditional LSTM network. In addition, the RMSE of the model decreases by 96.48% on average after incorporating the empirical formula. The proposed method has the lowest RMSE compared to other methods with the highest prediction accuracy. The experiments thoroughly verify that the ACLSTM model and the proposed empirical formula are feasible and effective for improving the prediction accuracy of the RUL of ATE.

Digital twin model for chiller fault diagnosis based on SSAE and transfer learning

The equipment of chiller systems is characterized by a complex mechanical structure and operating environments that vary widely, resulting in high failure rates, energy waste, and costly maintenance. Traditional fault diagnosis methods suffer from low levels of digitalization and intelligence. Therefore, timely and accurate detection and diagnosis of the operating status of chiller systems are critical. To address the above issues, this study proposes a digital twin (DT) model for chiller system fault diagnosis based on stacked sparse auto-encoder (SSAE) and transfer learning (TL). First, a chiller system digital twin mapping model is constructed utilizing digital twin technology. Then, the SSAE model is constructed to provide real-time defect diagnosis and fault result validation. Finally, to address the limited chiller system data in practical applications, the knowledge of fault diagnosis acquired in the source domain is transferred to the target domain via TL. Experimental results demonstrate that the SSAE model outperforms back propagation (BP), recurrent neural network (RNN) and long short-term memory (LSTM) model in term of accuracy. Furthermore, using TL achieves a diagnostic accuracy of over 90% for different degrees of fault severity, with a significant decrease in cross-entropy loss compared to no TL, confirming the effectiveness of the TL method.

AGATA: mechanics and infrastructures

The successful operation of AGATA requires a complex mechanical support structure for the safe and reliable operation of the detectors. Three mechanical structures were designed for the scientific campaigns at LNL, GSI and GANIL, each accommodating an increasing number of detectors. The present phase of the project, to increase the number of detectors from 60 to 180 (the 4pi spectrometer), required a new concept in mechanical support. The detectors also require a suite of associated instrumentation, infrastructures and good system design for their optimum performance. This includes the automatic liquid nitrogen filling system, high and low voltage power supplies, and a series of signal cables and distribution systems. A well-designed electromagnetic compatibility across all the sub-systems is essential. An additional requirement is an easily accessible database that records the status of the wide range of components utilised on the project. This article describes all aspects of the mechanics and infrastructures.

Innovation of Thermoelectric Conversion Stirling Device for Recovering Heat from Ship Exhaust Gas

With the implementation of the national carbon neutralization and carbon peak strategy, the technology of shipping energy conservation and emission reduction has also been concerned by the public. As there are high-power engines and generator sets in the engine room of the ship, the waste heat temperature of waste gas generated during equipment operation is extremely high, which can be as high as thousands of degrees Celsius. The device first converts waste gas waste heat into mechanical energy through Stirling machine, and then converts it into electrical energy through external generator. The whole working process produces electric energy while reducing the ambient temperature, and improves the utilization efficiency of waste heat. According to the preliminary experiment, about 3.3v electric energy can be generated when the temperature is 433°C. The device has simple structure, no complex mechanical structure, high safety and strong reliability. If multiple equipment can be used in the marine engine room at the same time, it will have a positive help to achieve the goals of carbon neutralization and carbon peak.

Accurate Position Tracking Control of SMAAs Based on Low-Complexity Self-Sensing Model and Compound Control Strategy

Shape memory alloy actuators (SMAAs) exhibit high strain, high power-to-weight ratio, and self-sensing ability, which are promising characteristics for application in micro-actuator systems. However, the complex self-sensing and nonlinear hysteresis characteristics of shape memory alloy (SMA) seriously affect the position tracking accuracy of SMAAs. In order to realize the accurate position tracking of the SMAAs without sensors, this article proposes a low-complexity self-sensing of modeling methods, which does not require complex mechanical structure, specific prestress, and complex mathematical models. Furthermore, a compound control strategy of generalized Prandtl–Ishlinskii (GPI) inverse model and fuzzy-proportional–integral derivative (PID) was presented to compensate the nonlinear hysteresis and time-varying characteristics of SMAAs. First, macroscopic and microcosmic characteristics of SMA were analyzed to explain the negative effect of the rhombohedral ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R}$ </tex-math></inline-formula> ) phase on the self-sensing characteristics of SMA. Second, a NiTi-based SMA wire without <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R}$ </tex-math></inline-formula> phase was selected as a self-sensing modeling object by comparative evaluation experiments, which has similar linear self-sensing properties under various pretension force conditions, a self-sensing model containing only quadratic polynomials is constructed by an offline identification. Third, a fuzzy-PID controller is designed based on the transfer function obtained from the SMAA’s system identification, further, a nonlinear hysteresis feedforward compensation controller is designed based on the GPI model. Finally, comparative experiments with the compound controller and fuzzy-PID controller for multistep and sinusoidal position tracking responses were performed, and the experimental results show that the proposed compound controller has a faster response and higher control accuracy in position tracking control.

Electric Drive with an Adaptive Controller and Wireless Communication System

In this paper, the problem of the remote control of electric drives with a complex mechanical structure is discussed. Oscillations of state variables and control precision are the main issues found in such applications. The article proposes a smart, IoT-enabled controller, which allows remote communication with a drive. To solve the problem of speed oscillations and to make the system robust to parameter uncertainty, an adaptive controller with two neural networks is designed. First, numerical tests are conducted in a Matlab/Simulink environment to examine the operation of the proposed control strategy. Afterwards, the obtained results are verified in a laboratory setup equipped with a 0.5 kW electric motor. Remote access is provided by a low-cost, ARM-based ESP32 microcontroller. Usually, virtual instruments used to communicate with remote devices require specific software, which may be expensive and pose compatibility problems. Therefore, the main contribution of the article is the creation of a low-cost, web-based Human-Machine Interface (HMI) with an asynchronous server utility provided by the ESP32 that allows remote control and data acquisition of electric drive state variables.

Multiscale Noise Reduction Attention Network for Aeroengine Bearing Fault Diagnosis

Aero-engine has a complex mechanical structure and its working condition are varying widely. Its fault signals are thus modulated through complex nonlinear transfer paths and influenced by non-Gaussian noises. However, popular data-driven multi-scale diagnostic model does not sufficiently consider the embedded noises, and consequently keeps the noise content in the advanced discriminative features, which decreases the diagnostic accuracy. Therefore, a multi-scale attention network with adaptive noise reduction (MANANR) is proposed in this paper for aero-engine bearing fault diagnosis. The MANANR firstly divides the original vibration signal into different scales via the average method of adjacent points. Then, two stacked multi-scale noise reduction modules B1 and B2 is designed for noise reduction. The core strategy behind B1 and B2 is the threshold noise reduction (TNR) which removes the noises from multi-scale convolution features adaptively. Based on the physical principles that global average pooling (GAP) and max pooling (MAP) can extract periodic and impulsive characteristics of the fault signals respectively, the threshold value of TRN is thus constructed through fusing GAP and MAP outputs. Furthermore, attention mechanism is employed to enhance the discriminative capability of multi-scale features by globally capturing the relations among different scales and channels. Finally, one two-layer classifier is introduced to confirm bearing fault patterns. Experimental results demonstrate that the proposed method has the feature-level noise reduction property, and more importantly achieves satisfying intelligent diagnosis precision for aero-engine bearing with the minimum peeling area of 0.5 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> under the continuous acceleration condition from 12000 rpm to 12550 rpm, which outperforms state-of-the-art intelligent diagnostic methods.

Data-driven fault detection of a 10 MW floating offshore wind turbine benchmark using kernel canonical variate analysis

Floating offshore wind turbines (FOWTs) can harvest more wind energy in deep water. However, due to their complex mechanical structure and harsh working conditions, various sensors, actuators, and components of FOWTs can malfunction and fail. To avoid serious accidents and reduce operation and maintenance costs, fault detection plays a critical role in wind-energy engineering, particularly for offshore wind energy. Because of complex characteristics, such as dynamics and nonlinearity, an accurate mathematical model cannot be easily obtained from first principles for FOWTs. In this paper, a new data-driven fault-detection method based on kernel canonical variable analysis (KCVA) is proposed for FOWTs. In the proposed method, the collected measurements are first augmented into time-lagged variables to capture the dynamics of FOWTs. The time-lagged variables are then mapped to a high-dimensional feature space to extract nonlinear features. Specifically, canonical variable analysis (CVA) is carried out to explore the correlations in high-dimensional feature space. For fault detection, two monitoring indexes including T 2 and squared prediction error () statistics are established. To verify the performance of the proposed KCVA-based fault-detection method, experiments on a high-fidelity FOWT benchmark, which was created from the National Renewable Energy Laboratory Fatigue, Aerodynamics, Structures, and Turbulence v8.0 simulator, were carried out. The results show the capability and efficiency of the proposed KCVA-based fault-detection method in comparison with other related methods.

Spontaneous electricity and electromagnetic damping control system for fitness equipment

Strength quality can reflect the physical quality, strength forging plays a positive role to maintain a strong body and abundant energy. The transmission system of traditional fitness equipment, such as electric treadmill and bicycle fitness device, are difficult to achieve the force training effect of simulating the rapid change of resistance like wind gust change and slope change automatically according to the pre-programmed curve changes in real time or speed. Combined with the current situation and existing problems of fitness equipment at home and abroad, this paper develops a self-generating power generation and electromagnetic damping control system that does not require external power supply, also has intelligent charge and discharge function, which can adjust the resistance output in real time. The system, which takes permanent magnetic synchronous generator as the resistance source, can replace the complex mechanical resistance structure and some electric resistance structure, and is widely used in the fitness equipment such as treadmill and bicycle trainer.

Application of independent‐left‐and‐right‐wheel‐driving force controller to torque vectoring differential with two‐input‐two‐output motor drive system for electrified vehicles

AbstractA torque‐difference‐amplification torque vectoring differential (TDA‐TVD), which is composed of a two‐input‐two‐output system, has a great potential for cornering maneuverability; however, the overall controllability of slip ratio or driving force control is difficult owing to its complex mechanical structure. In order to enhance the controllability of TDA‐TVD, this study proposes a design method to apply a driving force controller (DFC) with a decent slip ratio and driving force controllability, which was originally intended for independent wheel drive systems. An experimental verification on a slippery road using a real vehicle with the TDA‐TVD indicated that the DFC can be applied to the TDA‐TVD and that it improves the overall performance of the traction control.

Design, Fabrication, Testing and Simulation of a Rotary Double Comb Drives Actuated Microgripper.

This paper presents the development of a new microgripper actuated by means of rotary-comb drives equipped with two cooperating fingers arrays. The microsystem presents eight CSFH flexures (Conjugate Surface Flexure Hinge) that allow the designer to assign a prescribed motion to the gripping tips. In fact, the adoption of multiple CSFHs gives rise to the possibility of embedding quite a complex mechanical structure and, therefore, increasing the number of design parameters. For the case under study, a double four-bar linkage in a mirroring configuration was adopted. The presented microgripper has been fabricated by using a hard metal mask on a Silicon-on-Insulator (SOI) wafer, subject to DRIE (Deep Reactive Ion Etching) process, with a vapor releasing final stage. Some prototypes have been obtained and then tested in a lab. Finally, the experimental results have been used in order to assess simulation tools that can be used to minimize the amount of expensive equipment in operational environments.

Novel design of compound coupled hydro-mechanical transmission on heavy-duty vehicle for energy recycling

How to efficiently transmit power and reuse/recover energy has been the subjects of heated discussion in the high-power transmission field. Regarding heavy-duty vehicles, they used to be equipped with a gearbox as a typical type of high-power transmission to deliver power to the wheel. However, a gearbox transmission has quite complex mechanical structure for realizing a large speed-regulating range, and it has an inevitable drawback of weighing too much. In addition, it cannot reuse/recover energy. In this study, a novel design of a compound coupled hydro-mechanical transmission (CCHMT) is proposed to realize energy reuse/recovery on heavy-duty vehicles. The CCHMT has an optimal structure coupled with hydraulic and mechanical components, which makes its speed change continuously and keep the engine running with a high fuel economy. Moreover, compared with both hybrid and battery electric vehicles, vehicle with CCHMT has higher power density. To validate the transmission's feasibility and operational efficiency, simulation modeling and experiments are conducted. The CCHMT can transform unstable energy under a wide speed-regulation range, reuse/recover a vehicle's excess energy at an efficiency of 82.30%/62.94%, and provide a stable driving torque with a high efficiency of 76%, which shows a good energy management performance on heavy-duty vehicle's hydraulic system.

Biomechanical modeling of spinal ligaments: finite element analysis of L4-L5 spinal segment

The complex mechanical structure of spine is usually simplified in finite element (FE) modes. In this study, different 3D models of L4-L5 spinal segment distinguished by their ligament modelling were developed (1D truss, 2D shell and 3D space truss elements). All models could be considered validated with respect to range of motion and intradiscal pressure, although their ligament stresses/forces were substantially different. The models with 2D shell and 3D space truss ligaments showed the stress distribution and identified the potential failure/injury locations in ligaments. The model with 3D space truss ligaments showed the stress/force direction (representing collagen fiber directions).

Magnetic Gears and Magnetically Geared Machines with Reduced Rare-Earth Elements for Vehicle Applications

This paper covers a new emerging class of electrical machines, namely, Magnetic Gears (MGs) and Magnetically Geared Machines (MGMs). This particular kind of gears/machines is capable of either scaling up or down the revolutions-per-minute to meet various load profiles as in the case of mechanical gearboxes, but with physical isolation between the rotating components. This physical isolation between the rotational components leads to several advantages in favor of MGs and MGMs over mechanical gearboxes. Although MGs and MGMs can potentially provide a solution for some of the practical issues of mechanical gears, MGs and MGMs have two major challenges that researchers have been trying to address. Those challenges are the high usage of rare-earth Permanent Magnet (PM) materials and the relatively complex mechanical structure of MGs and MGMs, both of which are a consequence of the multi-airgap design. This paper presents designs that reduce the PM rare-earth content for Electric Vehicles (EVs). Additionally, the paper will ensure having practical designs that do not run the risk of permanent demagnetization. The paper will also discuss some new designs to simplify the mechanical structure.

Steering model identification and control design of autonomous ship: a complete experimental study

ABSTRACT Steering ship models are important for the study of autonomous ship manoeuverability and design of ship motion control system. It is always a difficult task to find the mathematical model by first principle as it needs prior knowledge of hydrodynamic derivatives. The input–output-based system identification theory can be used to establish system mathematical models. A solution is offered by developing a Wi-Fi-based self-propelled, autonomous system for a ship model with Internet of Things (IoT) capabilities to perform manoeuvering and seakeeping tests in indoor environment without any complex mechanical structure, viz. following bridge. The developed autonomous on-board system equipped with main computer, suitable electronics, sensors, data acquisition system and Wi-Fi-based communication system. The developed system offers a cost effective, modular and portable solution to perform hydrodynamic studies of different hull form without incorporating major changes in the system. The use of IoT makes the data accessible to a naval architecture in real-time to analyse the motion response of the ship in different wave conditions and enables to implement the digital twin to simulate the real field scenario. Input–output-based model identification experiments such as turning circle and zig-zag tests are conducted to estimate the first-order steering model parameters and is further extended to design and implementation of a classical proportional–derivative-based steering control. The design is described in this paper with details of implementation on a demonstration oceanographic coastal research vessel. It illustrates the excellent communication between shore station computer and the on-board system on a wire-free model with robust control and exhibiting all the motion behaviour and dynamic effects. Experiments performed in wave basin in different wave conditions validate the efficacy of the proffered method.