As the botnet makes the way for many illegal activities, it is considered as the most critical threats to cybersecurity. Although many detection models have been presented by the researchers, they couldn’t detect the botnets in an early stage. So to overcome this issue, an enhanced support vector neural network (ESVNN) is presented as the detection model in this paper. For enhancing the classification

The hob forms key component in gear hobbing machine and its health condition directly affects the reliability and safety of entire machine. This paper proposes a multiscale feature extraction scheme based on spectral graph wavelet transform combined with improved random forest, forming a novel hob fault diagnosis technique and realizing multi-scale analysis of vibration signals from the perspective

The article deals with a piezoelectric force sensor, that bases on the use of piezo ceramics PZK 850. These types of sensors were used due to characteristic of the piezo-material (i.e. crystal) particularly for dynamic force measurement only. The developed sensor is suitable to measure static forces up to 50 N; the principle of the measurement of static forces bases on the crystal vibration being damped

Infrared thermography is employed by different methodologies and techniques to perform characterisations and evaluations in solids, typically related to fracture mechanics and damage, based on monitoring heat generation or temperature changes. In this study, the limits of the current technology for Thermoelastic Stress Analysis (TSA), usually employed under low-frequency loads, are explored to characterise

For tunnels with large cross-sections, demolition of the temporary shoring of the tunnel can pose a very serious threat to the safety of the structure; Therefore it is very important to ascertain the deformation behavior of the tunnel in this stage. In this study, a tunnel settlement inversion model, based on the strain measurements of distributed optical fiber, has been proposed and the feasibility

In recent years, many artificial intelligence - based approaches are proposed for remaining useful life (RUL) prediction of bearings. However, most existing studies neglected the following problems: (1) Run-to-failure data of bearings of are generally less available; (2) Degradation trends of bearings under different working conditions are diverse; (3) Unlabeled data of bearings acquired in the online

For the application of deep learning in the field of fault diagnosis, its recognition accuracy is limited by the size and quality of the training samples, such as small size samples, low signal-to-noise ratio and different working conditions. In order to solve above problems, one novel method for fault classification is proposed based on a Bidirectional Long Short-Term Memory (Bi-LSTM) and a Capsule

This study investigates the performance and robustness of regression machine-learning models in the presence of variability in the experimental database. The main objective of this work is to predict the ultimate load of circular concrete-filled steel tubes. The simulations were designed by combining size of the learning dataset, random realizations and prediction models. The variability (i.e. probability

If the system model or the statistical characteristics of noise are inaccurate, the past measurements will directly affect the accuracy of current state estimation or even lead to filtering divergence. To overcome above difficulties, a multiple fading factors-based strong tracking variational Bayesian adaptive Kalman filter is proposed. Firstly, the inverse Wishart distribution is adopted to model

The fringe -reflection technology has been widely applied in the fields of industrial inspection and measurements. In the present study, three-step phase-shifting is developed using the color fringe-reflection approach to inspect a work-piece surface with rotational symmetry characteristics, and an improved experiment is proposed. In addition, by combining Robert gradient with fringe modulation, a

Partial discharge (PD) diagnostics is a reliable technique for the health assessment of electrical motors. This paper presents a unique methodology for the failure risk assessment of the low voltage motors containing various insulation defects based on PD measurements. The severity of PD in the motors increases with the time during their operation due to electrical and environmental stresses. In this

To cope with the complex bridge crack detection environment, we developed a flexible crack identification system. Firstly, the acquired images are processed by sliding window technology to construct a bridge crack dataset. Then we propose a trainable context encoder network which uses the recurrent residual convolutional neural network (RRCNN) to improve the encoder structure to better extract low-level

Vibrating screens are equipment used in various industries, such as mining, steel and food. Although it is simple to install vibratory equipment on a support structure, it is necessary to consider the static and dynamic forces caused by vibration. The present work aims to perform a dynamic analysis of a vibrating screen and measure the dynamic forces transmitted to its metallic support structure, by

Polyurethane grouting materials are new repairing and impermeable materials for infrastures especilly in rush to repair. Aiming at dynamic viscoelastic properties of the new-type mateials, firstly, the dynamic viscoelastic properties of polyurethane grouting materials was studied by Dynamic Mechanical Thermal Analysis. Then the influential factors for these properties were studied and the mechanism

The performance degradation assessment (PDA) of rolling element bearings is a necessary link to ensure the reliability of high-end equipment. However, traditional health indicators (HIs) are not sensitive to early defects, and there are often large local fluctuations in the later stage of degradation. Hence, this paper propose a novel PDA method to obtain HIs with early warning capability and monotone

No doubt that the measurement is an important matter in the precise and accurate control of automotive subsystems to improve the performance and efficiency. The key tool to achieve high degree of precision and accuracy in control applications is sensor. In recent years, the rapid technological advances have revolutionized the automotive industry and a great variety of new sensors are introduced to

In this paper, a topographic data stitching method is firstly introduced for compensating and optimizing the tilt errors derived from the lateral guide of the stage of a confocal laser scanning microscope. Afterwards, a novel evaluation process, including both simulated verification and experimental verification, is proposed to analyze the stitching precision and its applicability. At last, the evaluation

The bearings are the crucial components of rotating machines in an industrial firm. Unplanned failure of these components not only increases the downtime, but also leads to production loss. This paper presents a non-invasive thermal image-based method for bearing fault diagnosis in rotating machines. Thermal images of rolling-element bearing in six conditions have been considered, including one healthy

In this paper, we design a method to use a low-cost IMU (Inertial Measurement Unit) sensor for the absolute positioning of an oscillating object and for improvement of the kinematic RTS (Robotic Total Station) high-precision trajectory tracking accuracy. Typically when using standard methods for position estimation based on IMU measurements, a drift of several hundred meters occurres after only 1 minute

The long-term safe operation of rotating machinery is closely related to the stability of rolling bearings. This paper proposes a rolling bearing fault diagnosis method based on refined composite multiscale fuzzy entropy (RCMFE), topology learning and out-of-sample embedding (TLOE), and the marine predators algorithm based-support vector machine (MPA-SVM). First, the RCMFE algorithm is used to extract

An analysis was performed for examining the consequences of sample deformation in longitudinal axis in the sample-loaded non-resonant waveguide techniques. Vertical and horizontal deformations were first simulated to understand the change in field distributions and corresponding scattering parameters. Then, effects of sample length, dielectric constant, and loss factor for these deformations on extracted

This paper presents a critical review of the main sensing techniques considered for online monitoring of the integrity of alloys steel-made equipment used in process industry. This includes pipelines, tanks, electrical transformers and other metallic structures used in construction of buildings and bridges. Radio frequency (RF), Magnetic flux leak (MFL), ultrasonic-acoustic, fiber brag grating (FBG)

This paper develops a real-time dual-channel speckle interferometry system based on an improved dual-observation configuration to address the issue of simultaneous measurement of the out-of-plane displacement and slope. A dual-observation configuration combined with an additional light path is used to provide the possibility to create two measurement channels corresponding to the out-of-plane displacement

This research paper presents design of a gripper finger mechanism and a new curvature measurement method of fingertip trajectory. Firstly, the conditions of a grasp provided by gripper, which was designed from a spherical mechanism, was explained. Therefore, trajectory length of coupler point was modeled as objective function which is maximized. Ball-Burmester theory was used to form constraints which

A novel bearing fault diagnosis on basis of empirical mode decomposition (EMD) and improved Chebyshev distance is presented. After normalization, each group of sample data is divided into 10 equal parts on average. EMD is used to decompose the equal part signal into several eigenmode functions, and the first five intrinsic mode function (IMF) components are retained and transformed into symmetrical

In order to solve the problems of low detection efficiency, high labor intensity and poor working environment of large pressure vessel cylinder, the pulsed eddy current (PEC) nondestructive testing technology is used to detect the defects in the welding seam of the cylinder. The PEC sensor composed of rectangular probe coil and Hall sensor is installed on the mobile inspection platform to test two

In today’s world, electromagnetic compatibility (EMC) tests are of primary significance, increasing the importance of accurate measuring of EMC antenna factors. The standards for antenna calibrations require near-ideal conditions as empty space above ground plane with very large dimensions. We investigate the possibility to use the open-area test site (OATS) of Turkish Standard Institution (TSE) for

Sparse representation based on the matching pursuit (MP) algorithm is an effective method for fault feature extraction involving rolling element bearings. However, in the sparse decomposition stage, the MP algorithm is extremely susceptible to both noise in the residual signal and excessive iterations selecting some atoms that are not related to the fault impulses, thus causing interference components

Diabetes is one of the leading health problem all over the world. Microaneurysms (MAs) are the initial diabetic retinopathy (DR) signs that are detectable at the clinical level in human. Diagnosis and treatment of DR is an important task to be performed. Many methods for identifying microaneurysms, such as: ANN, CNN and AI, have been previously suggested. In this work, we propose machine learning based

Handgrip strength can be easily measured; however, lack of a quantitative relationship with other force types restricts its usefulness. The aim of this study was to analyse the relationship between handgrip force and moments of force in upper and lower limb joints in reference to age and sex. Multivariate regression analysis of the measurements resulted in models of decrees of moments of force according

Vibration signal of internal combustion engine is excited by the cylinder pressure and other excitations. The response signals caused by different excitations couple together in the time domain and frequency domain, which makes it difficult to extract combustion information from vibration signal. To determine the main excitation sources and the contribution level of the most important excitation that

In this study, an eco-friendly and accurate analytical protocol was proposed for the determination of cadmium at trace levels. Prior to analysis in flame atomic absorption spectrophotometry (FAAS), an ultraviolet based photochemical vapor generation (PVG) method as a sample introduction system was employed to eliminate matrix effects. PVG system was combined with an online preconcentration technique

Measuring distance is essential for health monitoring and condition assessment of civil engineering structures. This paper reviews recent advances in remote sensing technologies for measuring distance based on electromagnetic waves. Specifically, four families of technologies are reviewed, which are the Global Navigation Satellite Systems, microwave radars, laser-based methods, and vision-based methods

Deep learning, especially transfer learning, has made a great deal of extraordinary achievements in intelligent fault diagnosis. In practical situations, data shift problem is inevitable due to complicated and changeable working conditions. Ignoring this problem may result in considerable degradation of diagnostic accuracy. Thus, domain distance is measured in only one metric space, and the result

The paper describes the design and mathematical model of a 50 mA coaxial current shunt developed at Silesian University of Technology (SUT) in Gliwice, Poland. The AC-DC transfer difference of the shunt transimpedance and its phase displacement were calculated based on the mathematical model of the shunt. The results of AC-DC transfer difference calculation were validated by calibration of the shunt

Recent developments in the field of machine learning have led to drastic improvements in medical diagnosis. Identification of different medical conditions with high accuracy is possible through machine learning, specifically deep learning. Convolutional Neural Networks are a subset of deep neural networks, used in investigating visual images. In this study, a method to identify bacterial pneumonia

Blade tip timing (BTT) technique has become a promising way of online damage diagnosis for compressor and turbine blades by monitoring their vibrations. However, there are some disadvantages in previous algorithms for BTT signals, including not high precision, strict sensor layout, signal bandwidth limit, and weak anti-noise ability. To solve these problems, a novel interpolation method for wideband

Conveyor belt tear detection is a very important part of coal mine safety production. In this paper, a new method of detecting conveyor belt damage named audio-visual fusion (AVF) detection method is proposed. The AVF method uses both a visible light CCD and a microphone array to collect images and sounds of the conveyor belt in different running states. By processing and analyzing the collected images

Rolling bearing is a vital part of the machinery, whose remaining useful life (RUL) estimation plays a critical role in ensuring the safety and maintenance decision-making. However, in most industrial applications, it is difficult to obtain run-to-failure data under complex operating conditions, which is inefficient for deep learning approaches. To solve the above problem, a new approach using transfer

In the paper experimental results of the static characteristics of laser triangulation sensors are presented. In order to determine the uncertainty of length measurement using a measurement system equipped with a laser triangulation sensor, the procedure of calibration and estimation of static characteristics coefficients was applied. The obtained results were subjected to statistical analysis, and

The increased system complexity in electronic products brings challenges in a system level reliability assessment and lifetime estimation. Traditionally, the graph model-based reliability block diagrams (RBD) and fault tree analysis (FTA) have been used to assess the reliability of products and systems. However, these methods are based on deterministic relationships between components that introduce

This paper proposes a system for a laser ultrasonic inspection of fixed non-planar composite structures. A pulse-echo configuration is used by which both the sensing and excitation lasers hit the same point on the structure, utilizing bulk waves for a through-the-thickness defect evaluation. A high signal-to-noise ratio is ensured by mounting a laser Doppler vibrometer (LDV), used as the sensing laser

The orthogonal basis functions (OBFs) detector is a detection method widely used in the aerial magnetic measurement. However, OBFs detector works ineffectively under non-Gaussian noise and colored noise. This paper proposes an OBFs detector based on fast convergence wavelet artificial neural network (FC-W-ANN), which can detect abnormal magnetic signals under low SNR. First, the magnetic anomaly signal

Time synchronization is a fundamental requirement of radio navigation systems, and it is essential for real-time high-precision positioning in emergency applications such as fire scenes and military operations. For urgent positioning requirements without effective positioning means, a radio kinematic navigation system, which can be quickly deployed, is designed based on the Global Navigation Satellite

To improve the anti-jamming ability of infrared attitude measurement methods, the infrared focal plane array (IRFPA) fault diagnosis method was investigated with regard to small-region and full-region infrared interference, respectively. An adaptive fault-tolerant extended Kalman filter algorithm is proposed to actively remove the infrared interference data in the case of small-region infrared interference

RUL prediction of bearings plays an essential role in avoiding unwanted downtime and improving machines' reliability. A linear reliability indicator approach for RUL prediction is proposed. Multiple deep auto-encoder models are established to extract deep features, and a clustering method is employed to select the original feature set. Monotonicity is set as a criterion for evaluating and selecting

The growing number of cellular devices and the introduction of high-speed data affect the network performance of mobile communication systems. Evaluation of passive intermodulation (PIM) phenomenon in the field of microwaves and mobile communications improve system performance. Typically, the PIM of the device-under-test (DUT) is measured using a set-up that involves two power amplifiers (PA), making

The surface defects of the workpiece affect the workpiece quality. In order to detect workpiece surface defects more accurately, an automatic detection convolutional neural networks-based method is proposed in this paper. Firstly, a convolution network classification model (SCN) with symmetric modules is proposed, which is used as backbone of our method to extract features. And then, three convolution

Electric trains use mechanical devices such as pantographs to collect current from overhead lines. This research aims to define a critical speed for the train overhead system properly and identify its significant contributors. The wave propagation speed, in addition to the cable vibratory properties, is identified through laboratory measurements. An analytical model is developed, and the results are

In this paper, a universal probability-based method is proposed for health stage division based on the feature exacted from different rolling element bearings. A new monitoring feature called Amplitude Difference of Strong and Carpet Impulse (ADSCI) is adopted to obtain the degradation information of bearings. Specifically, the high frequency information is utilized to get the degradation status, and

In the present study, a classification system of four meteorological indices were introduced to classify the sky types from sunny to cloudy. These meteorological indices are; cloud cover (Cc), sunshine hour (S), clearness index (Kt) and diffuse fraction (K). Frequency of occurrence and cumulative frequency distribution of each sky indices were established to interpret the prevailing sky conditions

This paper introduces a novel image processing methodology to enable an extension of the range of applicability of the back-lit imaging method to provide the simultaneous measurement of size and velocity in turbulent flows with a high concentration of objects having various shapes and sizes. The new image processing methodology applies an interrogation-window method similar to that used in particle

Remaining useful life (RUL) using exponential model (EM) prediction has been a hot research topic in the construction of prognostics health management (PHM) systems. However, in RUL prediction of rolling bearings, the EM 1) depends on the appropriate first prediction time (FPT), 2) requires reliable methods to optimize the model. Therefore, an improved EM is developed to predict the RUL of rolling

Recently, with the desperate demand for data-driven deep learning methods in practical industrial applications, increasing popularity of deep learning methods for machine fault diagnosis under noisy environments has been observed. However, the existing studies have encountered two limitations: 1) the domain shift problem has not been researched enough to ensure a functional adaptation model across

High impedance Arcing faults (HIAFs) are normally caused by ruptured conductors, leaning of a tree with high impedance, and/or the presence of a high impedance object between the conductor and earth. In such cases, protections available in the microgrid may not be capable of detecting the HIAFs. Hence, to increase the safety level and reliability of the microgrid, it is essential to take action for

This paper aims at discussing an automated measurement system for detecting carbonation depth in concrete sprayed with phenolphthalein. Image processing and Convolutional Neural Networks strategies are exploited to accurately separate the carbonated and non-carbonated areas and to remove those aggregates on the carbonation front that could bring to a wrong evaluation of the carbonation depth. Very

To solve the contradiction between the trafficability and the power of traditional heavy trucks, the study proposes a coordinated control of hydraulic hub-motor auxiliary system for heavy truck to make full use of the coefficient of ground aadhesion and engine power. With hydraulic hub-motor auxiliary system (HHMAS) in the non-drive front wheels, the front wheels drive the truck on bad roads to ensure

Cancer is a kind of non-communicable disease, progresses with uncontrolled cell growth in the body. The cancerous cell forms a tumor that impairs the immune system, causes other biological changes to malfunction. The most common kinds of cancer are breast, prostate, leukemia, lung, and colon cancer. The presence of the disease is identified with the proper diagnosis. Many screening procedures are suggested