Dynamic Vision Enabled Contactless Cross-Domain Machine Fault Diagnosis With Neuromorphic Computing

Dear Editor,This letter presents a novel dynamic vision enabled contactless cross-domain fault diagnosis method with neuromorphic computing.The event-based camera is adopted to capture the machine vibration states in the perspective of vision.

Autonomous Recommendation of Fault Detection Algorithms for Spacecraft

Dear Editor, This letter deals with the problem of algorithm recommendation for online fault detection of spacecraft. By transforming the time series data into distributions and introducing a distribution-aware measure, a principal method is designed for quantifying the detectabilities of fault detection algorithms over special datasets.

Expert Experience and Data-Driven Based Hybrid Fault Diagnosis for High-SpeedWire Rod Finishing Mills

The reliable operation of high-speed wire rod finishing mills is crucial in the steel production enterprise.As complex system-level equipment,it is difficult for high-speed wire rod finishing mills to realize fault location and real-time monitoring.To solve the above problems,an expert experience and data-driven-based hybrid fault diagnosis method for high-speed wire rod finishing mills is proposed in this paper.First,based on its mechanical structure,time and frequency domain analysis are improved in fault feature extraction.The approach of combining virtual value,peak value with kurtosis value index,is adopted in time domain analysis.Speed adjustment and side frequency analysis are proposed in frequency domain analysis to obtain accurate component characteristic frequency and its corresponding sideband.Then,according to time and frequency domain characteristics,fault location based on expert experience is proposed to get an accurate fault result.Finally,the proposed method is implemented in the equipment intelligent diagnosis system.By taking an equipment fault on site,for example,the effectiveness of the proposed method is illustrated in the system.

Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation

The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source domain.However,in engineering scenarios,achieving such high-quality label annotation is difficult and expensive.The incorrect label annotation produces two negative effects:1)the complex decision boundary of diagnosis models lowers the generalization performance on the target domain,and2)the distribution of target domain samples becomes misaligned with the false-labeled samples.To overcome these negative effects,this article proposes a solution called the label recovery and trajectory designable network(LRTDN).LRTDN consists of three parts.First,a residual network with dual classifiers is to learn features from cross-domain samples.Second,an annotation check module is constructed to generate a label anomaly indicator that could modify the abnormal labels of false-labeled samples in the source domain.With the training of relabeled samples,the complexity of diagnosis model is reduced via semi-supervised learning.Third,the adaptation trajectories are designed for sample distributions across domains.This ensures that the target domain samples are only adapted with the pure-labeled samples.The LRTDN is verified by two case studies,in which the diagnosis knowledge of bearings is transferred across different working conditions as well as different yet related machines.The results show that LRTDN offers a high diagnosis accuracy even in the presence of incorrect annotation.

Path-Following Control With Obstacle Avoidance of Autonomous Surface Vehicles Subject to Actuator Faults

This paper investigates the path-following control problem with obstacle avoidance of autonomous surface vehicles in the presence of actuator faults,uncertainty and external disturbances.Autonomous surface vehicles inevitably suffer from actuator faults in complex sea environments,which may cause existing obstacle avoidance strategies to fail.To reduce the influence of actuator faults,an improved artificial potential function is constructed by introducing the lower bound of actuator efficiency factors.The nonlinear state observer,which only depends on measurable position information of the autonomous surface vehicle,is used to address uncertainties and external disturbances.By using a backstepping technique and adaptive mechanism,a path-following control strategy with obstacle avoidance and fault tolerance is designed which can ensure that the tracking errors converge to a small neighborhood of zero.Compared with existing results,the proposed control strategy has the capability of obstacle avoidance and fault tolerance simultaneously.Finally,the comparison results through simulations are given to verify the effectiveness of the proposed method.

miR-10b通过激活KLF4/Notch-1/STAT3信号途径加速腹主动脉瘤的进展

目的:miR-10b促进动脉瘤的进展但其机制未明确,本实验通过检测miR-10b对KLF4/Notch-1/STAT3途径的影响阐明其机理。方法:双荧光素酶实验验证miR-10b的靶基因。取20只apoE-/-小鼠构建腹主动脉瘤模型,其中10只尾静脉注射miR-10b过表达的慢病毒,分别记为模型组和miR-10b组。另取10只apoE-/-小鼠作为假手术组,28 d后解剖小鼠腹主动脉行病理学染色、Western-blot实验。将血管紧张素Ⅱ(AngⅡ)作用后的THP-1巨噬细胞根据是否转染miR-10b mimic及其Nc对照、是否添加抑制剂分组:(1)Nc组;(2)miR-10b mimic组;(3)Nc+KLF4抑制组;(4)miR-10b mimic+KLF4抑制组;(5)Nc+Notch-1抑制组;(6)miR-10b mimic+Notch-1抑制组;(7)Nc+STAT3抑制组;(8)miR-10b mimic+STAT3抑制组,检测细胞内蛋白水平。结果:检测双荧光素酶活性得知miR-10b可靶向作用于基因KLF4。小鼠血管病理实验发现,与正常小鼠相比,动脉瘤模型小鼠的血管明显增粗、组织结构异常,miR-10b可加重血管病变;CD68+巨噬细胞、蛋白Notch-1、p-STAT3、MMP-2、MMP-9在假手术组、模型组与miR-10b逐渐增多,而α-SMA、蛋白KLF4逐渐减少。(1)、(2)两组细胞间,后者KLF4表达较少、而Notch-1、p-STAT3、MMP-2、MMP-9表达显著偏多;KLF4抑制剂抑制KLF4表达,而Notch-1显著增多;Notch-1被抑制而减少表达,p-STAT3也随之减少;p-STAT3被抑制表达,MMP-2与MMP-9表达量也明显降低。结论:miR-10b通过作用于KLF4/Notch-1/STAT3信号途径,引起动脉瘤组织中MMP-2、MMP-9表达增加,从而加速动脉瘤的进程。

Semi-analytical solution for mechanical analysis of tunnels crossing strike-slip fault zone considering nonuniform fault displacement and uncertain fault plane position

The tunnel subjected to strike-slip fault dislocation exhibits severe and catastrophic damage.The existing analysis models frequently assume uniform fault displacement and fixed fault plane position.In contrast,post-earthquake observations indicate that the displacement near the fault zone is typically nonuniform,and the fault plane position is uncertain.In this study,we first established a series of improved governing equations to analyze the mechanical response of tunnels under strike-slip fault dislocation.The proposed methodology incorporated key factors such as nonuniform fault displacement and uncertain fault plane position into the governing equations,thereby significantly enhancing the applicability range and accuracy of the model.In contrast to previous analytical models,the maximum computational error has decreased from 57.1%to 1.1%.Subsequently,we conducted a rigorous validation of the proposed methodology by undertaking a comparative analysis with a 3D finite element numerical model,and the results from both approaches exhibited a high degree of qualitative and quantitative agreement with a maximum error of 9.9%.Finally,the proposed methodology was utilized to perform a parametric analysis to explore the effects of various parameters,such as fault displacement,fault zone width,fault zone strength,the ratio of maximum fault displacement of the hanging wall to the footwall,and fault plane position,on the response of tunnels subjected to strike-slip fault dislocation.The findings indicate a progressive increase in the peak internal forces of the tunnel with the rise in fault displacement and fault zone strength.Conversely,an augmentation in fault zone width is found to contribute to a decrease in the peak internal forces.For example,for a fault zone width of 10 m,the peak values of bending moment,shear force,and axial force are approximately 46.9%,102.4%,and 28.7% higher,respectively,compared to those observed for a fault zone width of 50 m.Furthermore,the position of the peak internal forces is influenced by variations in the ratio of maximum fault displacement of the hanging wall to footwall and the fault plane location,while the peak values of shear force and axial force always align with the fault plane.The maximum peak internal forces are observed when the footwall exclusively bears the entirety of the fault displacement,corresponding to a ratio of 0:1.The peak values of bending moment,shear force,and axial force for the ratio of 0:1 amount to approximately 123.8%,148.6%,and 111.1% of those for the ratio of 0.5:0.5,respectively.

Estimation of Displacement and Extension due to Reverse Drag of Normal Faults: Forward Method

In the case of reverse drag of normal faulting, the displacement and horizontal extension are determined based on the established equations for the three mechanisms: rigid body, vertical shear and inclined shear. There are three sub-cases of basal detachment for the rigid body model: horizontal detachment, antithetic detachment and synthetic detachment. For the rigid body model, the established equations indicate that the total displacement on the synthetic base (Dt2) is the largest, that on the horizontal base (Dt1) is moderate, and that on the antithetic base (Dt3) is the smallest. On the other hand, the value of (Dt1) is larger than the displacement for the vertical shear (Dt4). The value of (Dt1) is larger than or less than the displacement for the inclined shear (Dt5) depending on the original fault dip δ0, bedding angle θ, and the angle of shear direction β. For all original parameters, the value of Dt5 is less than the value of Dt4. Also, by comparing three rotation mechanisms, we find that the inclined shear produces largest extension, the rigid body model with horizontal detachment produces the smallest extension, and the vertical shear model produces moderate extension.

Geometry and 3D seismic characterisation of post-rift normal faults in the Pearl River Mouth Basin,northern South China Sea

Based on high-resolution 3D seismic data acquired in the Pearl(Zhujiang)River Mouth Basin of the northern South China Sea,this study investigated the geometry,spatial extension,and throw distribution of the post-rift normal fault through detailed seismic interpretation and fault modeling.A total of 289 post-rift normal faults were identified in the study area and can be classified into four types:(1)isolated normal faults above the carbonate platform;(2)isolated normal faults cutting through the carbonate platform;(3)conjugate normal faults,and(4)connecting normal faults.Throw distribution analysis on the fault planes show that the vertical throw profiles of most normal fault exhibit flat-topped profiles.Isolated normal faults above the carbonate platform exhibit roughly concentric ellipses with maximum throw zones in the central section whereas the normal faults cutting through the carbonate platform miss the lowermost section due to the chaotic seismic reflections in the interior of the carbonate platform.The vertical throws of conjugate normal faults anomalously decrease toward their intersection region on the fault plane whereas the connecting normal faults present two maximum throw zones in the central section of the fault plane.According to the symmetric elliptical distribution model of fault throw,an estimation was made indicating that normal faults cutting through the carbonate platform extended downward between-1308 s and-1780 s(two-way travel time)in depth and may not penetrate the entire Liuhua carbonate platform.Moreover,it is observed that the distribution of karst caves on the top of the carbonate platform disaccord with those of hydrocarbon reservoirs and the post-rift normal faults cutting through the carbonate platform in the study area.We propose that these karst caves formed most probably by corrosive fluids derived from magmatic activities during the Dongsha event,rather than pore waters or hydrocarbons.

Decadal Forecasts of Large Earthquakes along the Northern San Andreas Fault System, California: Increased Activity on Regional Creeping Faults Prior to Major and Great Events

The three largest earthquakes in northern California since 1849 were preceded by increased decadal activity for moderate-size shocks along surrounding nearby faults. Increased seismicity, double-difference precise locations of earthquakes since 1968, geodetic data and fault offsets for the 1906 great shock are used to re-examine the timing and locations of possible future large earthquakes. The physical mechanisms of regional faults like the Calaveras, Hayward and Sargent, which exhibit creep, differ from those of the northern San Andreas, which is currently locked and is not creeping. Much decadal forerunning activity occurred on creeping faults. Moderate-size earthquakes along those faults became more frequent as stresses in the region increased in the latter part of the cycle of stress restoration for major and great earthquakes along the San Andreas. They may be useful for decadal forecasts. Yearly to decadal forecasts, however, are based on only a few major to great events. Activity along closer faults like that in the two years prior to the 1989 Loma Prieta shock needs to be examined for possible yearly forerunning changes to large plate boundary earthquakes. Geodetic observations are needed to focus on identifying creeping faults close to the San Andreas. The distribution of moderate-size earthquakes increased significantly since 1990 along the Hayward fault but not adjacent to the San Andreas fault to the south of San Francisco compared to what took place in the decades prior to the three major historic earthquakes in the region. It is now clear from a re-examination of the 1989 mainshock that the increased level of moderate-size shocks in the one to two preceding decades occurred on nearby East Bay faults. Double-difference locations of small earthquakes provide structural information about faults in the region, especially their depths. The northern San Andreas fault is divided into several strongly coupled segments based on differences in seismicity.

Analysing Recent Breakthroughs in Fault Diagnosis through Sensor:A Comprehensive Overview

Sensors,vital elements in data acquisition systems,play a crucial role in various industries.However,their exposure to harsh operating conditions makes them vulnerable to faults that can compromise system performance.Early fault detection is therefore critical for minimizing downtime and ensuring system reliability.This paper delves into the contemporary landscape of fault diagnosis techniques for sensors,offering valuable insights for researchers and academicians.The papers begin by exploring the different types and causes of sensor faults,followed by a discussion of the various fault diagnosis methods employed in industrial sectors.The advantages and limitations of these methods are carefully examined,paving the way for highlighting current challenges and outlining potential future research directions.This comprehensive review aims to provide a thorough understanding of current advancements in sensor fault diagnosis,enabling readers to stay abreast of the latest developments in this rapidly evolving field.By addressing the challenges and exploring promising research avenues,this paper seeks to contribute to the development of more robust and effective sensor fault diagnosis methods,ultimately improving the reliability and safety of industrial and agricultural systems.

Actuator and sensor fault isolation in a class of nonlinear dynamical systems

Fault isolation in dynamical systems is a challenging task due to modeling uncertainty and measurement noise,interactive effects of multiple faults and fault propagation.This paper proposes a unified approach for isolation of multiple actuator or sensor faults in a class of nonlinear uncertain dynamical systems.Actuator and sensor fault isolation are accomplished in two independent modules,that monitor the system and are able to isolate the potential faulty actuator(s)or sensor(s).For the sensor fault isolation(SFI)case,a module is designed which monitors the system and utilizes an adaptive isolation threshold on the output residuals computed via a nonlinear estimation scheme that allows the isolation of single/multiple faulty sensor(s).For the actuator fault isolation(AFI)case,a second module is designed,which utilizes a learning-based scheme for adaptive approximation of faulty actuator(s)and,based on a reasoning decision logic and suitably designed AFI thresholds,the faulty actuator(s)set can be determined.The effectiveness of the proposed fault isolation approach developed in this paper is demonstrated through a simulation example.

Hierarchical multihead self-attention for time-series-based fault diagnosis

Fault diagnosis is important for maintaining the safety and effectiveness of chemical process.Considering the multivariate,nonlinear,and dynamic characteristic of chemical process,many time-series-based data-driven fault diagnosis methods have been developed in recent years.However,the existing methods have the problem of long-term dependency and are difficult to train due to the sequential way of training.To overcome these problems,a novel fault diagnosis method based on time-series and the hierarchical multihead self-attention(HMSAN)is proposed for chemical process.First,a sliding window strategy is adopted to construct the normalized time-series dataset.Second,the HMSAN is developed to extract the time-relevant features from the time-series process data.It improves the basic self-attention model in both width and depth.With the multihead structure,the HMSAN can pay attention to different aspects of the complicated chemical process and obtain the global dynamic features.However,the multiple heads in parallel lead to redundant information,which cannot improve the diagnosis performance.With the hierarchical structure,the redundant information is reduced and the deep local time-related features are further extracted.Besides,a novel many-to-one training strategy is introduced for HMSAN to simplify the training procedure and capture the long-term dependency.Finally,the effectiveness of the proposed method is demonstrated by two chemical cases.The experimental results show that the proposed method achieves a great performance on time-series industrial data and outperforms the state-of-the-art approaches.

Gravity Fault Subsidence and Beach Ridges Progradation in Quinta-Cassino (RS) Coastal Plain, Brazil

Ground penetrating radar (GPR) surveys have being applied to investigate very near-surface stratification of sedimentary units in coastal plains and to define their depositional conditions. This paper presents, however, low-frequency GPR survey to investigate fault-related depositional systems at greater depths. The Quinta-Cassino area in the Rio Grande do Sul Coastal Plain (RGSCP, Brazil) shows a wide strandplain that is made off by very long, continuous, and linear geomorphic features (beach ridges). This strandplain extends for ~70 km southward. The beach ridges show low-angle truncations against the Quinta escarpment, and also truncations in the strandplain. The traditional approach points that RGSCP was developed by juxtaposition of four lagoons/barrier systems as consequence of sea level changes;previous model assumes that no deformational episode occurred in RGSCP. The geophysical and geological surveys carried out in this area showed the existence of listric fault controlling the beach ridges in the escarpments and hanging-wall blocks. The radargrams could distinguish Pleistocene basement unit anticlockwise rotation, thickening of beach ridges radarfacies close to listric normal faults, and horst structures. These deformational features indicate that the extensional zone of a large-scale gravity-driven structure controlled the mechanical subsidence, the Holocene sedimentation and its stratigraphic and geomorphic features in the Quinta-Cassino area to build up an asymmetric delta. The results point to a new approach in dealing with RGSCP Holocene evolution.

Causal temporal graph attention network for fault diagnosis of chemical processes

Fault detection and diagnosis(FDD)plays a significant role in ensuring the safety and stability of chemical processes.With the development of artificial intelligence(AI)and big data technologies,data-driven approaches with excellent performance are widely used for FDD in chemical processes.However,improved predictive accuracy has often been achieved through increased model complexity,which turns models into black-box methods and causes uncertainty regarding their decisions.In this study,a causal temporal graph attention network(CTGAN)is proposed for fault diagnosis of chemical processes.A chemical causal graph is built by causal inference to represent the propagation path of faults.The attention mechanism and chemical causal graph were combined to help us notice the key variables relating to fault fluctuations.Experiments in the Tennessee Eastman(TE)process and the green ammonia(GA)process showed that CTGAN achieved high performance and good explainability.

Complementary-Label Adversarial Domain Adaptation Fault Diagnosis Network under Time-Varying Rotational Speed and Weakly-Supervised Conditions

Recent research in cross-domain intelligence fault diagnosis of machinery still has some problems,such as relatively ideal speed conditions and sample conditions.In engineering practice,the rotational speed of the machine is often transient and time-varying,which makes the sample annotation increasingly expensive.Meanwhile,the number of samples collected from different health states is often unbalanced.To deal with the above challenges,a complementary-label(CL)adversarial domain adaptation fault diagnosis network(CLADAN)is proposed under time-varying rotational speed and weakly-supervised conditions.In the weakly supervised learning condition,machine prior information is used for sample annotation via cost-friendly complementary label learning.A diagnosticmodel learning strategywith discretized category probabilities is designed to avoidmulti-peak distribution of prediction results.In adversarial training process,we developed virtual adversarial regularization(VAR)strategy,which further enhances the robustness of the model by adding adversarial perturbations in the target domain.Comparative experiments on two case studies validated the superior performance of the proposed method.

Fault Diagnosis Scheme for Railway Switch Machine Using Multi-Sensor Fusion Tensor Machine

Railway switch machine is essential for maintaining the safety and punctuality of train operations.A data-driven fault diagnosis scheme for railway switch machine using tensor machine and multi-representation monitoring data is developed herein.Unlike existing methods,this approach takes into account the spatial information of the time series monitoring data,aligning with the domain expertise of on-site manual monitoring.Besides,a multi-sensor fusion tensor machine is designed to improve single signal data’s limitations in insufficient information.First,one-dimensional signal data is preprocessed and transformed into two-dimensional images.Afterward,the fusion feature tensor is created by utilizing the images of the three-phase current and employing the CANDE-COMP/PARAFAC(CP)decomposition method.Then,the tensor learning-based model is built using the extracted fusion feature tensor.The developed fault diagnosis scheme is valid with the field three-phase current dataset.The experiment indicates an enhanced performance of the developed fault diagnosis scheme over the current approach,particularly in terms of recall,precision,and F1-score.

Uncertainty-Aware Deep Learning: A Promising Tool for Trustworthy Fault Diagnosis

Recently,intelligent fault diagnosis based on deep learning has been extensively investigated,exhibiting state-of-the-art performance.However,the deep learning model is often not truly trusted by users due to the lack of interpretability of“black box”,which limits its deployment in safety-critical applications.A trusted fault diagnosis system requires that the faults can be accurately diagnosed in most cases,and the human in the deci-sion-making loop can be found to deal with the abnormal situa-tion when the models fail.In this paper,we explore a simplified method for quantifying both aleatoric and epistemic uncertainty in deterministic networks,called SAEU.In SAEU,Multivariate Gaussian distribution is employed in the deep architecture to compensate for the shortcomings of complexity and applicability of Bayesian neural networks.Based on the SAEU,we propose a unified uncertainty-aware deep learning framework(UU-DLF)to realize the grand vision of trustworthy fault diagnosis.Moreover,our UU-DLF effectively embodies the idea of“humans in the loop”,which not only allows for manual intervention in abnor-mal situations of diagnostic models,but also makes correspond-ing improvements on existing models based on traceability analy-sis.Finally,two experiments conducted on the gearbox and aero-engine bevel gears are used to demonstrate the effectiveness of UU-DLF and explore the effective reasons behind.

复合氨基酸Amino M-10在化妆品中的应用研究

复合氨基酸Amino M-10是由10种氨基酸科学配比复配而成,总氨基酸浓度为7.5~8.5%。通过采用理化分析、细胞模型试验、人体功效评价试验、配方应用实例、消费者调查等多种方法,研究了复合氨基酸Amino M-10的基本性能、护肤功效、护发功效及其在洗护和护肤化妆品中的应用,研究结果显示,复合氨基酸Amino M-10具有很好的保湿、舒缓、抗氧化、抗衰老、祛红血丝、护发等功效。人体斑贴试验和细胞活力检测结果也显示复合氨基酸Amino M-10温和而无刺激,可广泛应用于洗护和护肤化妆品中,具有广阔的市场前景和研究价值。

Analysis of multiple-faults of high-voltage circuit breakers based on non-negative matrix decomposition

High-voltage circuit breakers are the core equipment in power networks,and to a certain extent,are related to the safe and reliable operation of power systems.However,their core components are prone to mechanical faults.This study proposes a component separation method to detect multiple mechanical faults in circuit breakers that can achieve online real-time monitoring.First,a model and strategy are presented for obtaining mechanical voiceprint signals from circuit breakers.Subsequently,the component separation method was used to decompose the voiceprint signals of multiple faults into individual component signals.Based on this,the recognition of the features of a single-fault voiceprint signal can be achieved.Finally,multiple faults in high-voltage circuit breakers were identified through an experimental simulation and verification of the circuit breaker voiceprint signals collected from the substation site.The research results indicate that the proposed method exhibits excellent performance for multiple mechanical faults,such as spring structures and loose internal components of circuit breakers.In addition,it provides a reference method for the real-time online monitoring of high-voltage circuit breakers.