Complex Working Conditions Research Articles

Advancements in Functionalizable Metal‐Organic Frameworks for Flexible Sensing Electronics

AbstractFlexible sensing electronics, with good lightweight and flexibility, can maintain excellent sensing capability while fitting complex curved surfaces, having important applications in wearable devices, medical health monitoring, and robotics. The rapid advancement of metal‐organic frameworks (MOFs) has created the prospect of additional improvements in flexible sensors. The porous structure brings them a high specific surface area, meaning that when used as sensitive materials for sensors, excellent sensitivity and selectivity can be achieved. Meanwhile, the sensing performance and stability of MOF‐based flexible sensors can be further enhanced by modifying MOFs’ structure or compounding them with other materials, which is crucial for manufacturing flexible sensors utilized in complex working conditions. Herein, the advancement of MOFs and MOF‐based flexible sensors is systematically reviewed. First, the common series of MOFs, the preparation and modification methods, and the highly conductive MOFs are introduced. MOFs’ application in flexible sensing is then expounded, including self‐powered mechanical sensing, gas sensing, liquid analyte sensing, and multi‐target/mode sensing. It is believed that as MOFs with better response capabilities are developed and manufacturing processes advance, MOF‐based flexible sensors are expected to be more widely used in the future and promote the further development of technologies such as human‐computer interaction technology.

Rolling bearing fault diagnosis method based on MTF-MMCNN

Rolling bearing fault diagnosis method based on MTF-MMCNN

Compound fault feature separation with frequency segmentation and improved sparse filtering for rolling bearings

Due to variable and complex working conditions, rolling bearings are susceptible to compound faults. However, because a single fault plays a leading role, the weaker fault in the compound fault is difficult to monitor. If the weak fault in the compound fault can be found and identified, it is conducive to a more accurate judgment of the health state of the bearing. Several approaches have been proposed to detect compound bearing faults, but most of them are complex, inefficient and may require prior knowledge about fault characteristics such as fault period. In this article, the improved approach based on sparse filtering is proposed to detect the compound faults in rolling bearings. The proposed approach utilizes the wavelet decomposition method to divide the original vibration signal with compound faults into several segments in the frequency domain. All segmentations are employed to construct the Hankel matrix. Improved sparse filtering (ISF) is then employed to enhance the fault features by attenuating the noise. ISF performs autocorrelation on input samples (column of Hankel matrix) to improve the expressiveness of features. The penalty term is used to improve the performance of the sparse characteristic expressions of the weight matrix. The envelope spectral analysis is then finally used to detect the compound faults. The ability of sparse filtering to separate the multi-fault signal into different components is discussed in the simulation. Both simulation and experimental data with compound faults verify the effectiveness of the developed approach. The ability to distinguish different fault modes without prior knowledge of fault periods makes the proposed method advanced and suitable for compound fault diagnosis in rolling bearings. Compared with the existing methods, results show superior feature extraction performance of the proposed method.

Statistical batch-based bearing fault detection

In the domain of rotating machinery, bearings are vulnerable to different mechanical faults, including ball, inner, and outer race faults. Various techniques can be used in condition-based monitoring, from classical signal analysis to deep learning methods in diagnosing these faults. Based on the complex working conditions of rotary machines, multivariate statistical process control charts such as Hotelling’s T2 and Squared Prediction Error are useful for providing early warnings. However, these methods are rarely applied to condition monitoring of rotating machinery due to the univariate nature of the datasets. In the present paper, we propose a multivariate statistical process control-based fault detection method that utilizes multivariate data composed of Fourier transform features that are extracted for fixed-time batches. Our approach makes use of the multidimensional nature of Fourier transform characteristics, which record more detailed information about the machine’s status, in an effort to enhance early defect detection and diagnosis. Experiments with varying vibration measurement locations (Fan End, Drive End), fault types (ball, inner, and outer race faults), and motor loads (0–3 horsepower) are used to validate the suggested approach. The outcomes illustrate our method’s effectiveness in fault detection and point to possible wider uses in industrial maintenance.

Temperature-Dependent Model of Rutting Behavior for Connected Layer Mixtures in Flexible Base Asphalt Pavement.

The flexible base asphalt pavement is characterized by its excellent performance. A critical component of this structure is the connected layer, which links the surface layer to the flexible base. This layer is located in an area of high-pressure stress distribution within the pavement. The risk of rutting is heightened in the connected layer due to the effects of loads and temperature fluctuations. However, existing studies have not established a correlation between temperature and rutting prediction models for connected layer materials. Therefore, this study aims to accurately characterize the temperature dependence of the rutting behavior of these materials. Firstly, variations in dynamic stability and rutting deformation of connected layer mixtures were investigated under different temperatures and asphalt binder types. Subsequently, a temperature-dependent model of rutting behavior was developed, highlighting the influence of varying temperatures and asphalt binder types. The proposed temperature-dependent rutting model addresses the limitations of traditional models, which fail to accurately describe the rutting behavior of materials under complex working conditions, and provides a scientific basis for optimizing asphalt pavement design and enhancing rutting performance.

Research on the Tunable Optical Alignment Technology of Lidar Under Complex Working Conditions

Lidar technology is pivotal for detecting and monitoring the atmospheric environment. However, maintaining optical path stability in complex environments poses significant challenges, especially regarding adaptability and cost efficiency. This study proposes a tunable optical alignment method that is applied to the Rotating Rayleigh Doppler Wind Lidar (RRDWL) to enable precise detection of mid-to-upper atmospheric wind fields. Building on the conventional echo signal strength method, this approach calibrates the signal strength using cloud information and the signal-to-noise ratio (SNR), enabling stratified and tunable optical alignment. Experimental results indicate that the optimized RRDWL achieves a maximum detection height increase from 42 km to nearly 51 km. Additionally, the average horizontal wind speed error at 30 km decreases from 11.3 m/s to 4.4 m/s, with a minimum error of approximately 1 m/s. These findings confirm that the proposed method enhances the effectiveness and reliability of the Lidar system under complex operational and diverse weather conditions. Furthermore, it improves detection performance and provides robust support for applications in related fields.

A novel fault diagnosis method based on convolutional neural network with adaptive noise injection

Abstract In most existing intelligent fault diagnosis methods, noise is considered harmful and may decrease diagnosis accuracy. In contrast to these methods, this study proposes a novel fault diagnosis method with extra noise injection, termed an adaptive-noise-injected convolutional neural network (CNN). Noise is intentionally injected into a CNN model’s softmax layer to improve fault diagnosis accuracy. The injected noise is used in the iteration of the CNN model and adaptively adjusted according to the change in model loss. Bearing datasets from Case Western Reserve University and Paderborn University were used to validate the effectiveness of the proposed method. The robustness of the proposed method was illustrated by injecting Gaussian and uniform noise. By comparing the ablation study results with those of the state-of-the-art methods, and t-test results before and after noise injection, the effectiveness of noise injection in enhancing diagnosis accuracy was demonstrated. The proposed method performed well on small samples and in complex working conditions, and possesses good generalizability and the ability to deal with real-world datasets.

Multiple-model-based adaptive state fusion estimation of IWMD-EV using SSUKF

Abstract To improve the preciseness degree and adaptive adjustment ability of the state acquisition system under multiple operating conditions, a vehicle state fusion estimation strategy using strong-tracking suboptimal unscented Kalman filtering (SSUKF) algorithm and adaptive weights is proposed. The vehicle models were established by comprehensively characterizing the multi-parameter mapping relationship under the kinematic, dynamic, and electromechanical coupling driving relationship of in-wheel motor drive electric vehicle (IWMD-EV). To improve estimation performance of vehicle state estimation system in face of nonlinear factors such as noise and interference, a vehicle kinematic-based observer (KO) and a dynamic-based observer (DO) were developed based on the SSUKF algorithm. In addition, to enhance the overall preciseness degree and its adaptive adjustment ability for large-scale road conditions, a state fusion estimation method with adaptive weight is presented by combining redundant information of multiple model observers. The results demonstrated that the proposed method can significantly improve the estimation preciseness degree and the adaptive ability for complex working conditions.

The Time-Domain Design Stress Method for Fatigue Analysis of the Reactor Pressure Vessel in Floating Nuclear Power Plants

Nuclear power technology has rapidly advanced with the growing global demand for clean energy. As one of the core components of nuclear power plants (NPPs), the design and lifespan evaluation of reactor pressure vessels (RPVs) are critically important. However, while fatigue analysis methods for RPVs in land-based NPPs are relatively well established, the application of these methods to floating nuclear power plants (FNPPs) faces great challenges. Existing analysis methods are difficult to directly apply, and no widely accepted fatigue analysis approach currently exists for this context due to the complex working conditions in marine environments. A time-domain design stress (TDDS) method is developed in this study for the fatigue analysis of RPVs in FNPPs. This method systematically analyzes the impacts of wave loads, internal pressure, and thermal effects on the fatigue life of RPVs by simplifying the wave environment into a time-domain model of roll and pitch motions and adopting the regular wave superposition techniques. This method further adjusts the initial phases of regular waves considering the uncertainty of various load combinations, and superimposes the stress components caused by regular waves with different initial phases, thermal loads, and pressure loads. Subsequently, stress history curves are analyzed using the rainflow counting method, and combined with the damage accumulation theory, the upper and lower limits of fatigue damage are obtained. The results demonstrate that compared to traditional methods in time-domain analysis, the proposed TDDS method provides greater accuracy in evaluating the fatigue life of RPVs in FNPPs, with the average error in fatigue damage values being only 0.033%. Furthermore, the TDDS method reduces analysis time by approximately 70%, which significantly improves computational performance. These findings underscore the reliability and effectiveness of this method in practical applications.

An efficient and lightweight detection method for stranded elastic needle defects in complex industrial environments using VEE-YOLO

Deep learning has achieved significant success in the field of defect detection; however, challenges remain in detecting small-sized, densely packed parts under complex working conditions, including occlusion and unstable lighting conditions. This paper introduces YOLOv8-n as the core network to propose VEE-YOLO, a robust and high-performance defect detection model. Firstly, GSConv was introduced to enhance feature extraction in depthwise separable convolution and establish the VOVGSCSP module, emphasizing feature reusability for more effective feature engineering. Secondly, improvements were made to the model’s feature extraction quality by encoding inter-channel information using efficient multi-Scale attention to consider channel importance. Precise integration of spatial structural and channel information further enhanced the model’s overall feature extraction capability. Finally, EIoU Loss replaced CIoU Loss to address bounding box aspect ratio variability and sample imbalance challenges, significantly improving overall detection task performance. The algorithm’s performance was evaluated using a dataset to detect stranded elastic needle defects. The experimental results indicate that the enhanced VEE-YOLO model’s size decreased from 6.096 M to 5.486 M, while the detection speed increased from 179FPS to 244FPS, achieving a mAP of 0.926. Remarkable advancements across multiple metrics make it well-suited for deploying deep detection models in complex industrial environments.

Design and Analysis of Combined Vibration Absorbers for Ship Propulsion Shaft Systems

The vibration of a ship’s propulsion shaft system directly affects the ship’s lifespan, and many studies have designed vibration absorbers only for one of the natural frequencies of a ship’s propulsion shaft system without considering the influence of multiple low-order resonance frequencies. In this paper, a vibration absorber combined with a magnetorheological elastomer vibration absorber and a rubber vibration absorber in series is designed, and it can cover two torsional natural frequency band ranges to achieve better vibration reduction performances in multiple different torsional natural frequencies. The torsional natural frequency of the propulsion shafting of a 45 m fishing vessel is determined based on a multiple-degrees-of-freedom equivalent discretization model. Two natural frequencies, 22.4 Hz and 131.4 Hz, of a ship propulsion shaft system are selected as the design goal parameters of the combined vibration absorber. The magnetic field is simulated to ensure that the magnetic field generated by an energized coil can meet requirements. Then, a dynamic simulation of the ship propulsion shaft system with a combined vibration absorber is conducted via co-simulation. Afterward, the device is installed on the intermediate shaft of the ship propulsion shaft system for simulation, and the vibration reduction effect of the device is analyzed at different frequencies by controlling the current. When the device is controlled to operate at the optimal frequency point, the results show that the angular acceleration vibration amplitude reduction around the first and third torsional natural frequencies of the propulsion shaft system reaches 90% and 18%, respectively. This study provides new ideas for the intelligent and controllable vibration damping of ship propulsion shaft systems, especially for the development trend of intelligent ship equipment under complex working conditions.

Modified kernel global-local marginal fisher analysis for rolling bearing feature extraction

Abstract The vibration signals of rolling bearings usually exhibit high-dimensional, nonlinear, and non-Gaussian distribution characteristics due to long-term operation under complex working conditions. Therefore, we proposed a novel algorithm named Modified Kernel Global-Local Marginal Fisher Analysis (MKGLMFA) for bearing feature extraction and dimensionality reduction. The proposed MKGLMFA algorithm introduces the kernel function to map data into a high-dimensional space to represent data nonlinearly first. It enhances the within-class compactness and between-class dispersibility by considering spatial relationships and label information when constructing adjacency graphs and simultaneously exploits the local and global geometry of data. Furthermore, a bearing fault diagnosis approach is presented based on MKGLMFA. It first processes the original vibration signals through MKGLMFA to obtain low-dimensional manifold features. Then these characteristics were input into the K-nearest neighbor (KNN) classifier to achieve fault pattern recognition. The superiority of the proposed MKGLMFA algorithm in feature extraction is verified in comparison with some existing state-of-the-art machine learning methods on three rolling bearings datasets. And the subsequent classification diagnosis experiments indicate the effectiveness and high efficiency of the newly raised MKGLMFA algorithm. In comparison with the representative diagnosis methods, the proposed method can extract more sensitive discriminant features, and the classification accuracy of diagnosis is significantly improved in consequence.

An Adjustable Elastic Support Structure for Vibration Suppression of Rotating Machinery

Abstract The dynamic characteristic of a rotor-support system plays a crucial role in the operational efficiency and longevity of rotating machinery such as gas turbines. Traditional support structures with the fixed-parameter elastic stiffness have difficulties in adapting to the diverse and complex working conditions of flexible rotating machinery, particularly those that pass through multiple critical speeds. To address rotor resonance and enhance structural reliability, an innovative adjustable elastic support (AES) structure designed to improve the mechanical adaptability of rotors across various speed ranges is investigated in this paper. The AES structure is built upon the conventional squirrel cage elastic support by incorporating a stepper motor. By adjusting the rotation angle of the motor, the effective length of the cage bar can be modified, allowing real-time changes in the support state during rotor operation. This mechanism enables seamless transitions among multiple states: a constant stiffness elastic support, a separate state, or a variable stiffness dynamic absorber that modulates frequencies in real-time by altering the cage bar length. This dynamic capability effectively suppresses resonance peaks caused by mass imbalances. The design and implementation of the AES structure, along with a speed feedback-based adjustment scheme to accommodate the rotor's dynamic characteristics, are discussed. Experimental validation of a multisupport flexible rotor system demonstrates that a maximum vibration can be reduced up to 75%, highlighting its promising potential for engineering applications.

Thermal Design and Experimental Validation of a Lightweight Microsatellite

The multiple working modes, complex working conditions, frequent changes in external heat flux, and high-power consumption of satellites all pose great difficulties to their thermal design. In this paper, the material MB15 magnesium alloy was used, which has not been performed for the main structure of satellites. This material not only reduces the weight of the structure and the launch cost, but also its good thermal conductivity is very helpful for the thermal control design of the satellite. This paper mainly describes the design of a thermal control system for lightweight microsatellites. Firstly, the satellite structure, thermal control indices of the main equipment, and the power consumption of the equipment in different working modes are introduced. Then, the external heat fluxes are analyzed, the position of the heat dissipation surface and extreme conditions are confirmed, and detailed thermal designs of each part of the satellite are determined via the combination of active and passive thermal control. Finally, the thermal balance test is carried out for the whole satellite. It is found that the deviation between the temperature of the thermistor and thermocouple at the same moment in the thermal analysis simulation data and thermal balance test is generally within 0.5 °C, and the maximum difference is not more than 1.7 °C, which indicates that the simulation model is well established and that the thermal analysis is reliable.

A Novel Fault Diagnosis Method based on Convolutional Neural Network with Adaptive Noise Injection

Abstract In most existing intelligent fault diagnosis methods, noise is considered harmful and may decrease diagnosis accuracy. In contrast to these methods, this study proposes a novel fault diagnosis method with extra noise injection, termed an adaptive-noise-injected convolutional neural network (ANI-CNN). Noise is intentionally injected into a CNN model's softmax layer to improve fault diagnosis accuracy. The injected noise is used in the iteration of the CNN model and adaptively adjusted according to the change in model loss. Bearing datasets from Case Western Reserve University and Paderborn University were used to validate the effectiveness of the proposed method. The robustness of the proposed method was illustrated by injecting Gaussian and uniform noise. By comparing the ablation study results with those of the state-of-the-art methods, and t-test results before and after noise injection, the effectiveness of noise injection in enhancing diagnosis accuracy was demonstrated. The proposed method performed well on small samples and in complex working conditions, and possesses good generalizability and the ability to deal with real-world datasets.

A meta transfer learning fault diagnosis method for gearbox with few-shot data

Abstract The gearbox of large-scale mechanical equipment operates under complex working conditions, and its operation and maintenance are crucial to ensure safe production. This paper proposes a fault diagnosis method for gearboxes under variable operating conditions with few-shot data to address the scarcity of data for specific fault types. Firstly, several fault diagnosis tasks are constructed under variable operating conditions. These deep features of different tasks are extracted by a deep convolutional neural network. In the training process, the model adaptively adjusts its parameters and inner loop learning rate, enabling it to acquire domain invariant features. The Batch Spectral Shrinkage is presented to reduce the impact of negative transfer and catastrophic forgetting on model optimization during knowledge transfer. The loss function is reconstructed using the weight balance strategy to mitigate the distribution discrepancy between the source and target domains. Consequently, the Meta-SGD transfer neural network framework enables a fault diagnosis model for gearboxes under variable operating conditions with few-shot data. The experimental datasets of the gearbox verify the effectiveness of the proposed fault diagnosis framework.

Development Status of Dynamic Sealing Technology and Discussion on Advanced Sealing Technologies

This paper reviews the current state of dynamic sealing technologies, examining the challenges faced by conventional sealing methods under complex working conditions, such as high temperature, high pressure, and corrosive environments. It also provides a concise overview of the status and developmental trends in sealing inspection technologies. From the perspective of obstruction mechanisms, this study reinterprets the concept of sealing science by redefining the classification of sealing types based on solid-phase medium obstruction, fluid hydrostatic and hydrodynamic obstruction, fluid pumping obstruction, fluid energy dissipation obstruction, and fluid impact obstruction. Comparative analyses of sealing structures across these obstruction mechanisms are presented. The sealing technology based on fluid impact medium obstruction, newly proposed by this paper, represents an innovative sealing approach. It offers distinct advantages such as zero wear, structural simplicity, and high stability, addressing longstanding issues in high-speed, large-clearance non-contact seals, including low leakage suppression efficiency, system complexity, and poor stability. Since its introduction, this novel sealing structure has garnered significant attention and recognition from both the academic and industrial sealing communities. With the potential to revolutionize the field, this groundbreaking sealing design is poised to lead the next wave of technological advancements in sealing science.

Integrated DC arc model and DC arc detection approach based on K‐line diagram and spectrum integral difference

AbstractIn the low voltage direct current (LVDC) systems, the occurrence of DC series arc faults poses a significant threat to the safe operation of the system. This paper develops an accurate arc model for the arc fault simulation. The proposed arc model consists the steady‐state impedance, high frequency characteristics, and dynamic characteristics of the arc. Additionally, this paper proposes an arc detection algorithm combining the K‐line diagram and the spectrum integral difference of the arc current in the LVDC systems. This algorithm can identify four circuit states: normal operation, arc fault, switching action, and load mutation, which addresses the challenge of arc fault detection in complex working conditions. The STM32F407 microcontroller is utilized to design a DC series arc fault detector. Online detection tests demonstrate that the arc faults can be accurately detected and isolated within 37 ms, meeting the requirement of the UL1699B standard, with an accuracy rate of 99.33%. These achievements not only enhance the safety of the LVDC systems but also provide valuable references for the development of arc fault detection technology.

An innovative evaluation of microstructure and tribological behavior of AISI 1060 and CuZn37Pb2 under laboratory and complex working conditions

Wear is a significant industrial issue caused by the interaction of multiple complex factors rather than solely by material properties. CuZn37Pb2 and AISI 1060 steel are particularly susceptible to wear due to extensive industrial applications. This study developed a wear test tool, machined on a horizontal lathe, for testing under dry and lubricated conditions. A tribological comparison was conducted between the lathe test and a tribometer, examining factors like surface roughness, load, sliding speed, wear track diameter, track width, contact temperature, wear loss, and wear rate relative to the friction coefficient. Experiments were performed with torques ranging from 25 to 100 N, speeds of 0.30, 0.40, and 0.50 m/s, and wear track diameters of 4, 6, 8, and 10 mm. Worn surfaces and wear tracks were analyzed using optical microscopy and SEM-EDS. The influence of temperature (50 °C to 200 °C) on friction properties was studied, showing that sample morphology and test type greatly affect tribological response. Despite a wear rate calculation error below 8.12%, results indicated differences between laboratory and real-world tribological responses. This study enhances wear understanding by examining numerous previously unstudied characteristics and shows that, although wear cannot be entirely eliminated, it can be significantly minimized. Laboratory testing provides prototypes for industrial challenges, effectively linking academic research with industry needs.

Vibrating screen fault detection based on video frame prediction

ABSTRACT As a key technical equipment for improving coal quality, efficiency and promoting clean and efficient utilization, it is important for vibrating screens to detect their faults quickly and accurately. Traditional vibrating screen fault detection methods usually rely on the vibration signals collected by sensors, and identify the faults through spectral analysis, time domain feature extraction, etc. However, these methods face the problems of inaccurate detection, insufficient real-time performance and poor adaptability to environmental changes under complex working conditions. To address this limitation, this paper proposes a video frame prediction model, DST-UNet, to detect the operation of vibrating screens through video. The model combines convolutional units with different kernel sizes to simultaneously extract local detail information and global structural information in the operation of vibrating screens; uses ECA as a temporal attention mechanism to improve the model’s understanding and sensitivity to temporal sequences and employs convolutional units to generate spatio-temporal weights to dynamically fuse spatial and temporal features. In addition, an evaluation metric MTR is proposed to assess the model performance. The experimental results show that, compared with models such as SimVP, 3D convolution and PredRNN, the method improves the sensitivity, accuracy and processing speed of fault detection while reducing the model complexity and can realize efficient detection with limited data sets, which helps to identify equipment faults quickly and reduce the risk of damage.