Jiawei Xiang

Wenzhou University · College of Mechanical and Electrical Engineering

High-performance finite element research has always been a major focus of finite element method studies. This article introduces isogeometric analysis into the finite element method and proposes a new isogeometric finite element method. Firstly, the physical field is approximated by uniform B-spline interpolation, while geometry is represented by n...

Fault diagnosis is essential to guarantee the reliability and safety of rolling bearings. However, the current research mainly focuses on a large amount of data and neglects the problem of insufficient sample size of rolling bearings in actual engineering. Therefore, this study investigates a novel small sample fault diagnosis method for rolling be...

To improve the control performance of a permanent magnet synchronous motor (PMSM) under external disturbances, an improved active disturbance rejection control (IADRC) algorithm is proposed. Since the nonlinear function in the conventional ADRC algorithm is not smooth enough at the breakpoints, which directly affects the control performance, an inn...

This paper presents a novel data-driven predictive maintenance scheduling framework for aircraft engines based on remaining useful life (RUL) prediction. First, a deep learning ensemble model is proposed to effectively predict aircraft engine RUL, including a one-dimensional convolutional neural network (CNN) and a bidirectional long short-term mem...

A limitation of the Simultaneous Localization and Mapping (SLAM) is the lack of consideration of dynamic objects in the environment, resulting in degradation of map quality and positioning accuracy. This paper introduces a 2D tightly-coupled LiDAR-Inertial Odometry and Mapping framework DY-LIO to remove dynamic objects for a better SLAM. The main i...

Surface roughness is of great significance in maintaining mechanical performance and improving the reliability of the equipment. However, fast surface roughness evaluations that are sufficiently stable and efficient for engineering in-situ use have not yet been realized. To address this issue, an image-driven roughness intelligent method is propose...

In order to improve the accuracy of stress intensity factors (SIFs) calculated by traditional boundary element methods (BEM), the multi-domain wavelet boundary element method (WBEM) is proposed. Firstly, by adjusting the nodes of the B-spline wavelet element on the interval, crack-tip elements are constructed. Since B-spline wavelet on the interval...

Recently, the intelligent fault diagnosis models gains increasing attention due to the development of artificial intelligent and state monitoring technology. However, obtaining massive defect data in advance in the actual diagnostic environment is difficult. Constructing diagnostic models on small sample datasets will easily lead to serious over fi...

The Supervisory Control and Data Acquisition (SCADA) system is the standard installation on large wind turbine (WT) to monitor all major WT sub-components. By analyzing SCADA data, the anomaly of the WT can be timely identified. However, the complex coupling relationship between different sensors poses a great challenge to the high accuracy of WT a...

Existing deep learning-based infrared image analysis methods have been widely utilized in gearbox fault. However, these methods still face problems such as insufficient amount of effective data and poor adaptability under variable working conditions. In this paper, a deep prototype network model combined with multi-scale module and coordinate atten...

Dangerous driving behavior is a serious issue leading to harm drivers and further increase traffic burden. You Only Look Once (YOLO) model is a commonly used fast detection model suitable for real-time dangerous driving behavior detection with poor detection performance. To address this problem, a lightweight object detection model called multiple...

Bearing fault diagnosis is critical for ensuring mechanical reliability and operational safety. Industrial internet of things (IIoT) sensors provide real-time monitoring data, advancing research in data-driven approaches to bearing fault diagnosis. However, current studies overlook two key challenges: susceptibility to noise interference during fau...

With the increasing application numbers of RFID multitags in recent years, the position distribution of RFID multitag has a significant impact on the reading performance of RFID system. The poor reading performance of RFID multitag will lead to the loss of important information stored in the tags. In this paper, a novel synergistic topology graph c...

Health monitoring (HM) in rotatory machinery is a process of developing a mechanism to determine its state of deterioration. It involves analyzing the presence of damage, locating the fault, determining the severity of the problem, and calculating the amount of time that the machine can still be used effectively by making use of signal processing m...

Hydraulic multi-way valves as core components are widely applied in engineering machinery, mining machinery, and metallurgical industries. Due to the harsh working environment, faults in hydraulic multi-way valves are prone to occur, and the faults that occur are hidden. Moreover, hydraulic multi-way valves are expensive, and multiple experiments a...

Artificial intelligence (AI), which has recently gained popularity, is being extensively employed in modern fault diagnostic research to preserve the reliability and productivity of machines. The effectiveness of AI is influenced by the quality of the labeled training data. However, in engineering scenarios, available data on mechanical equipment a...

Intelligent Maintenance Systems (IMS) data set with faulty bearings vibrations original version: Prognostics Center of Excellence - Data Repository, https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository Data used in: A. Kumar et al., Non-parametric Ensemble Empirical Mode Decomposition for extracting weak features to identify b...

Dear Esteemed Researchers, Welcome to the 8th International Conference on Condition Monitoring in Non-Stationary Operations (CMMNO 2024). After 7 successful sessions of CMMNO held globally since 2011, Destiny has selected the prestigious location of Wenzhou, China, for its upcoming event. It is being jointly organized by Wenzhou University, China,...

In the field of RFID application, the locations of RFID multi-tags have a great influence on the reading distance of RFID system. In order to improve the reading distance of RFID multi-tags, a novel image processing and deep normalized CNN (DNCNN) for location measurement and reading distance prediction of RFID multi-tags is proposed. Firstly, in v...

Renewable energy has increased in recent years with a consequential increase in equipment maintenance. Maintenance costs can be reduced by structural health monitoring techniques especially for wind turbine (WT) blade damages. However, the majority are not suitable for on-line measurements and quantitative detections. A quantitative damage detectio...

Structural damage detection based on vibration signals has gained significant attention from researchers due to its ease of implementation. However, while the detection of damage using natural frequencies and mode shapes is effective for static structures, it is not directly applicable to structures in operation. To address this limitation, a two-s...

Idiopathic pulmonary fibrosis (IPF) is a severe interstitial lung disease with poor prognosis and high mortality rate. In the process of IPF, inflammatory dysregulation of macrophages and massive fibroblast aggregation and proliferation destroy alveoli, which cause pulmonary dysfunction, and ultimately lead to death due to respiratory failure. In t...

Time–frequency ridge not only exhibits the variable process of non-stationary signal with time changing but also provides the information of signal synchronous or non-synchronous components for subsequent detection research. Consequently, the key is to decrease the error between real and estimated ridge in the time–frequency domain for accurate det...

Intelligent fault diagnosis of roller bearings is facing two important problems, one is that train and test datasets have the same distribution, and the other is the installation positions of accelerometer sensors are limited in industrial environments, and the collected signals are often polluted by background noise. In the recent years, the discr...

Strong noise interference or compound fault coupling phenomenon may lead to the failure of fault diagnosis technology. This paper focuses on weak feature extraction and compound faults detection for rotating machinery fault diagnosis and proposes adaptive symplectic geometric mode decomposition (SGMD) using cycle kurtosis entropy. Firstly, an index...

This work is dedicated to develop a novel discrete probabilistic entropy-based health indicator (HI) and long short-term memory (LSTM)-based method to forecast bearing health. The desired discrete probabilistic entropy measure is resulted from the proclaimed symmetric information discrimination measure between two discrete probability distributions...

In this paper, a nonequidistant T-shaped sensor cluster used for localizing the impact on structures is presented. The normal T-shaped sensor cluster configuration, in which all the sensors are equidistantly set, can effectively remove blind areas because it is a combination of two impact-localization techniques: equidistant linear sensor array bea...

This paper proposes a novel tangent hyperbolic fuzzy entropy measure-based method for determining the highly (most) sensitive frequency band to easily identify defective components in an axial piston pump. The validation of proposed scheme was done by experimental and simulation analysis. In the proposed method, initially, the raw vibration signal...

The state of charge (SOC) for a lithium-ion battery is a key index closely related to battery performance and safety with respect to the power supply system of electric vehicles. The Kalman filter (KF) or extended KF (EKF) is normally employed to estimate SOC in association with the relatively simple and fast second-order resistor-capacitor (RC) eq...

Robust spring energy state identification of the operating mechanism is of great significance for monitoring the overall performance of the circuit breakers. However, rapid monitoring of the spring energy storage state based on the acquired current signal during the service period has not yet been realized. To address this problem, this research pu...

Short-time Fourier transform (STFT)-based methods are widely applied in industrial areas. However, these methods are inadequate to process non-stationary signals under variable operation conditions. An improved general linear chirplet transform method is developed by iteratively upgrading the instantaneous frequency and introducing a synchrosqueezi...

In recent years, deep learning- based methods have attracted much attention and achieved remarkable results for intelligent fault diagnosis of rotating machinery. However, in many actual industrial scenes, there are few labeled and large unlabeled samples that can be collected for learning due to the cost of experiments, which affect seriously the...

The healthy state of bearings directly affects the safety and performance of rotating machinery. Developing effective fault diagnosis techniques is pivotal for the maintenance of rotating machinery. Given its adaptive decomposition capability, intrinsic time-scale decomposition (ITD) has gained wide acceptance and utilization in fault diagnosis. Ho...

Using one-dimensional (1D) scaling functions of B-spline wavelet on the interval (BSWI) as the interpolation functions, a wavelet boundary element method (WBEM) is presented to solve stress intensity factors (SIFs) for two-dimensional (2D) plates with singular stress fields. Firstly, to discrete the geometrical boundary, 1D wavelet-based elements a...

Industrial sulfuric acid wastewater treatment using vacuum membrane distillation (VMD) technology has attracted worldwide attention. However, high energy loss and low membrane flux seriously hinder the industrialization of VMD technology. This paper novelly introduced an entransy theory to improve the VMD system performance. Mathematical models wer...

The existing fault diagnosis methods of rotating machinery constructed with both shallow learning and deep learning models are mostly based on vibration analysis under steady rotating speed. However, the rotating speed frequently changes to meet practical engineering needs. The shallow learning models largely depend on domain experience of feature...

A piston wear fault is a major failure mode of axial piston pumps, which may decrease their volumetric efficiency and service life. Although fault detection based on machine learning theory can achieve high accuracy, the performance mainly depends on the detection model and feature selection. Feature selection in learning has recently emerged as a...

Stimulating immunogenic cell death (ICD) is the key to tumor immunotherapy. However, traditional chemoradiotherapy has limited effect on stimulating immunity and often requires repeated administration, which greatly reduces the tumor-killing effect. In this article, we created a sodium alginate hydrogel sustained-release system containing low-dose...

Idiopathic pulmonary fibrosis is a fatal interstitial disease characterized by fibroblast proliferation and differentiation and abnormal accumulation of extracellular matrix, with high mortality and an increasing annual incidence. Since few drugs are available for the treatment of pulmonary fibrosis, there is an urgent need for high-efficiency ther...

In this paper, a new repairable k-out-of-n system model is proposed, in which the deviation cost of the system is taken into account. Firstly, the standard k-out-of-n system is replaced at planned time or at the failure time. Next, replacement policies are studied at planned time or periodic time when k is a constant. Thirdly, replacement policies...

Intelligent fault diagnosis methods can obtain promising results in ensuring the safety and reliability of key parts of rotating machinery. However, the problems are the insufficient amount of data during equipment acceptance period and the assumption that the collected data are high quality which directly affects the reliability of promising resul...

This study aims to establish a novel entropy-based sparsity measure for two main purposes: first is for the prognosis of bearing defects, secondly it is employed to construct sparsogram for identifying sensitive filtering band to carry out envelope analysis for identifying defects in the complex hydraulic machinery (axial piston pump). The newly de...

Transfer learning (TL) technology have been successfully applied to address the domain adaptation (DA) problem in machinery fault diagnosis. However, partial DA problem is more suitable for industrial applications, where the target data only covers a subset of the source classes, which makes it difficult to know where to transfer the target data. T...

Artificial intelligence (AI) models are widely used in every field of life to classify and predict the abnormal conditions. The key to activating AI models is to obtain enough training samples. The lumped parameter model is the one of the best choices to provide sufficient samples. In this study, the lumped parameter model is used in the numerical...

Recently, some adversarial transfer learning (TL) approaches have been developed for addressing the partial domain adaptation (DA) problems in machinery fault diagnosis. However, these existing methods generally follow the partial DA framework of multiple sub-domain discriminators, which causes the overly complex model in dealing with many source c...

The Aviation Safety Reporting System (ASRS) data is an important information source for risk identification in civil aviation. However, ASRS data has the characteristics of high dimensionality, unstructured and data imbalance. This brings great challenges for risk identification. In this paper, a model fusion strategy is proposed for identifying th...

Cavitation will increase the leakage and discharge pressure fluctuation of axial piston pumps. In particular, specific cavitation damage may aggravate the pressure impact and performance degradation. The influence of the specific cavitation damage on the discharge pressure is unclear, and the need for fault detection of this damage is urgent. In th...

As an artificial periodic material, phononic crystals (PnCs) are suitable for energy harvesting from vibration and noise environments of equipments. A two-dimensional (2D) PnC with a point defect is presented to design a piezoelectric energy-harvesting (PEH) device. Using finite element (FE) simulations, bandwidth and electric power of the initial...

In this paper, the band gap (BG) properties of the phononic crystal (PC) involving bonding layers described by the ring-like structure are investigated. Based on the generalized Maxwell model, the effect of the bonding layers on BGs dependence is analyzed. Combing the numerical simulation and physical experiment, BG characteristic of the ring-like...

The development of bearing fault detection methods is of great significance for the performance maintenance of axial piston pumps. However, the reciprocating movement induced strong natural periodic impulses that completely submerged the fault characteristic frequencies of the axial piston pump. To solve this problem, a finite element method (FEM)-...

In order to alleviate the freshwater resource shortage worldwide, the multiple vacuum membrane distillation (VMD) modules were drawn into a mechanical vapor recompression (MVR) system to recover industrial wasterwater in this paper. An experimental platform was constructed and several experiments were conducted under multiple working conditions. Th...

This study propounds a novel methodology for automatic identification of rotor defects severity, when the machine is operated at constant speed, through maximal overlap discrete wavelet packet transforms (MODWPT) and proposed cross entropy measures of bipolar neutrosophic sets, single valued neutrosophic sets and fuzzy sets respectively. After the...

In intelligent fault diagnosis, the success of artificial intelligence (AI) models is highly dependent on labeled training samples, which may not be obtained in real-world applications. Recently, a finite element method (FEM) simulation-based personalized diagnosis method was developed to overcome the problems of insufficient and incomplete labeled...

Rotor-bearing systems play a vital role in machine tools, aero engines, and wind turbines. Generally, worn-induced degradation quantities and manufacturing errors of components are the main error sources that influence the precision reliability of rotor-bearing systems. The current precision reliability evaluation models are focusing on several err...

The underlying study proposes a novel procedure for automated testimony of rotor defects through maximal overlap discrete wavelet packet transforms (MODWPT) and the proposed neutrosophic cubic cross measure, fuzzy cross entropy and single valued neutrosophic cross entropy measures consecutively. Discrete wavelet transform is an efficient data acqui...

To eliminate the sensor array based impact localization bias on stiffened composite structures, a finite element method (FEM) simulation based adaptive sensors array errors compensation method for impact monitoring is introduced. Firstly, detailed analysis and comparison discussion about Lamb wave propagation errors between the simple and stiffened...

The vibration signal of faulty rolling bearing of rotating machine carries a large amount of information reflecting its fault categories. However, compound fault features are easily mixed together, and can cause missed diagnosis and misjudgment, which is still a challenging task in mechanical fault diagnosis. A compound fault detection method using...

Structural health monitoring (SHM) is the process of establishing a damage identification method for engineering infrastructure. It entails evaluating the presence of damage, locating the fault, estimating the seriousness of the problem, and lastly, predicting the structure's remaining useful life. A structure can be as big as that bridge for sever...

Because the hydraulic directional valve usually works in a bad working environment and is disturbed by multi-factor noise, the traditional single sensor monitoring technology is difficult to use for an accurate diagnosis of it. Therefore, a fault diagnosis method based on multi-sensor information fusion is proposed in this paper to reduce the inacc...

Rotor-bearing systems play a vital role in machine tools, aero engines, and wind turbines. Generally, worn-induced degradation quantities and manufacturing errors of components are the main error sources that influence the precision reliability of rotor-bearing systems. The current precision reliability evaluation models are focusing on several err...

Due to the complex working medium of oil in construction engineering, the waterproof valve in mixing machinery can easily cause different degrees of failure. Moreover, under adverse working conditions and complicated noise backgrounds, it is very difficult to detect the fault of waterproof valves. Thus, a fault diagnosis method is proposed, especia...

A bearing's health state is closely linked to the reliable operation of rotating machinery. In this context, dynamic time warping (DTW) is an excellent fault classifier due to its outstanding distance measurement ability. However, DTW alone cannot obtain acceptable results when it is employed to handle signals with a certain degree of noise. An enh...

Concrete slab cracks monitoring of modern high-speed railway is important for safety and reliability of train operation, to prevent catastrophic failure, and to reduce maintenance costs. This paper proposes a curvature filtering improved crack detection method in concrete slabs of high-speed railway via graph-based anomalies calculation. Firstly, l...

Energy conservation and emission reduction in the field of industrial wastewater treatment have attracted worldwide attention. This study focused on the concentration and recovery of industrial sulfuric acid waste by a vacuum membrane distillation system based on mechanical vapor recompression (VMD-MVR). Mathematical models were built considering t...

This work is dedicated to the establishment of state-space modelling combined with a novel probabilistic entropy-based health indicator (HI), needed to assess the dynamic degradation monitoring and estimation of remaining useful life (RUL) of rolling element bearing. The classical statistical HI kurtosis exclusively fails to hold the understanding...

In this paper, a novel phoxonic crystal (PxC) structure composed of silicon, with optimal dual phononic band gap (PNBG) and photonic band gap (PTBG), is presented. Using the finite element analysis method, both the transmission characteristics and dispersion relation of PNBG and PTBG are calculated, and the existence of dual BGs is demonstrated by...