Frequency Of Defects Research Articles

Active control of Kagome lattice defect mode using point-ring defect engineering

Kagome lattice defect modes with a point-ring structure are explored theoretically in two-dimensional germanium cylinder photonic crystals using plane wave expansion and supercell methods. The number and degeneracy of the transverse magnetic field defect mode are governed by the defect setting. By varying the distance and direction of the point-ring defects, we can manipulate and control the frequency width and coupling between defect modes and the defect frequency in the bandgap. Additionally, the intensity distribution localized in the defect position is related to the parity of defect structure symmetry axes. This characteristic is highly beneficial to applications such as in resonators and narrow band filters.

MORPHOLOGICAL ANALYSIS OF 3D MR IMAGES OF PF JOINTS IN ASYMPTOMATIC SUBJECTS

Understanding the pathogenesis of patellofemoral osteoarthritis (PFOA) will be enhanced by elucidating the onset and progression in asymptomatic PFOA. Demonstrating of the cartilage defect of 3D MR images, obtained from a cohort population, in order from the smallest to the largest, can mimic the morphological progression process of PFOA. The purpose of this study was to clarify the frequency, classification, and morphological progression process of PFOA by observing MRI 3D images in a cohort population of subjects without a history of visiting hospitals for knee disease. The subjects were 561 at 30–79 years old who were registered in the Kanagawa Knee Study. The main exclusion criteria were those who have a past history of consecutive visits to the hospital for more than 3 months for knee symptoms. We obtained 3D MR images of the PF joints using 3D image analysis system volume analyzer (SYNAPSE 3D, Fujifilm Corporation, Tokyo). The cartilage defects were classified as medial and lateral type. MR images of the PF joints were displayed with cartilage area ratios of the patella and presented in order of highest to lowest, separated into the medial and lateral type. Of the 561 subjects, cartilage defects were detected on 3D images of the patella in 37 subjects (6.6%). Medial lesion was detected in 26 subjects (4.6%) and lateral lesion in 11 subjects (2.0%), showing that medial lesion was significantly more frequent (p=0.01). For the medial type, the patellar cartilage defect was located in the center of the medial facet, expanded to the surrounding area, and further distally and medially. 3D MR images of the patellofemoral joint showed the cartilage defect of the patella expanded along with the medial ridge of the femoral trochlea. For the lateral type, the patellar cartilage defect was located in the center of the lateral facet, expanded to the surrounding area, and further distally and laterally. 3D MR images of the patellofemoral joint showed the cartilage defect of the patella expanded along with the lateral ridge of the femoral trochlea (Fig. 1). According to 3D MR images, the frequency of the patella cartilage defect was 6.6% in 561 asymptomatic subjects. Patellar cartilage defects occurred more frequently on the medial facet than on the lateral facet. The patellar cartilage defect occurred at the center of each facet and expanded along with each side of the ridge of the femoral trochlea. Fujifilm Corporation and Japan Society for the Promotion of Science Grant JP22K09349 I Sekiya received a research grant from Fujifilm Corporation including lending of the analysis software used in this study. Kimiko Takanashi is to be thanked for technical assistance. CORRESPONDENCE ADDRESS: sekiya.arm@tmd.ac.jp

A Practical Approach to Using Quality Indicators in Continuous Quality Improvement Processes in a Medical Laboratory.

The obligation of implementing the terms "risk management" and "quality indicators" led us to understand and apply them in the best possible way. The purpose of our study was to establish a guide of selection and usage of quality indicators (QIs)/key performance indicators (KPIs) in a Romanian medical laboratory. The study group consisted of all analysis requests received by the Biochemistry Department of the laboratory between January 1st, 2015, and December 31st, 2018. The first stage of the study took place between January 1st, 2015, and December 31st, 2015, when the QIs/KPIs were selected for the pre-analytical process by risk management techniques and evaluation guide for the QIs/KPIs was developed. After their establishment, we checked, using data of the pre-analytical QIs of a previous stage, if the claims and the initially established limits were specific, objective, and attainable. In the second stage of the study the data were collected prospectively. Monthly values, percentages, and sigma values of selected QIs/KPIs were calculated. QI percentages were compared to the IFCC performance specifications and the limits established by the laboratory. The frequency of total defects of samples was 3.45%, the Six Sigma value was 3.4 for the Biochemistry Department. The highest rates were observed for the lipemic (1.92%) and hemolyzed samples (1.06%) in the Biochemistry Department. Implementing risk management and QIs can increase the pre-analytic process performance by decreasing the risk level either through stepping up the measures of detection, or through reducing of the frequency of occurrence of nonconformities.

Recursive Singular Spectrum Analysis for Induction Machines Unbalanced Rotor Fault Diagnosis

One of the major challenges of diagnosing rotor symmetry faults in induction machines (IMs) is severe modulation of fault and supply frequency components. In particular, existing techniques are not able to identify fault components in the case of low slips. In this article, this problem is tackled by proposing a novel approach. First, a new use of singular spectrum analysis (SSA), as a powerful spectrum analyzer, is introduced for fault detection. Our idea is to treat the stator current signature of the wound rotor IM as a time series. In this approach, the current signature is decomposed into several eigenvalue spectra (rather than frequency spectra) to find a subspace where the fault component is recognizable. Subsequently, the fault component is detected using some data-driven filters constructed with the knowledge about characteristics of supply and fault components. Then, an inexpensive peak localization procedure is applied to the power spectrum of the fault component to identify the exact frequency of the fault. The fault detection and localization methods are then combined in a recursive regime to further improve the diagnosis’ performance particularly at high rotor speeds and small rotor faults. The proposed approach is data-driven and is directly applied to the raw signal with no suppression or filtration of the frequency harmonics with a low computational complexity. The numerical results obtained with real data at several rotation speeds and fault severities unveil the effectiveness and real-time feature of the proposed approach.

Feature Enhancement Based on Regular Sparse Model for Planetary Gearbox Fault Diagnosis

The planetary gearbox, widely used in many machinery fields, suffers from harmful vibration excited by bearings fault, which always causes machine breakdowns. Thus, fault diagnosis is the necessary approach to keeping machines safe, which often takes fault features, which are extracted from signals, as critical information. However, limited to various interferences caused by gear meshing and background noise, fault characteristic information is always weak and difficult to identify, bringing an increasing emphasis on feature enhancement. In this paper, based on the feature enhancement problem under strong interferences, a sparse regular diagnosis algorithm is studied. Firstly, a new fault sensitivity indicator is constructed to distinguish different periodic impulse signals and enhance the energy of the fault impulse. Combined with tunable Q-factor wavelet transform multi-scale representation, the guided enhanced vector is introduced to guide the directional gathering of the fault frequency band. Then a weighted regular term is constructed to establish the Guided Enhanced Regular Sparse (GERS) model. Iterative soft threshold algorithm is employed to solve the model. Compared with state-of-the-art methods, through numerical simulation and fault experiment, the effectiveness and superiority of the proposed algorithm is verified successfully.

Improved Multivariate Hierarchical Multiscale Dispersion Entropy: A New Method for Industrial Rotating Machinery Fault Diagnosis

This paper proposes an Improved Multivariate Multiscale Dispersion Entropy(IMMDE) combined with Hierarchical Entropy(HE) for vibration signal feature extraction. The traditional coarse-grained calculation is missing the relationship between neighboring sample points in the shift operation, which may lead to missing fault information. Secondly, as the scale increases, the original sequence is gradually shortened, leading to instability and inaccuracy in entropy estimation when dealing with short-term sequences. The improved coarse-grained calculation method overcomes its limitations to improve stability, and the HE method extracts deep fault frequency information from the high and low-frequency components of the multivariate signal. Then, the extracted features are dimensioned using the Max-Relevance Min-Redundancy (mRMR) to create a new set of fault features to improve diagnosis efficiency. Finally, the Support Vector Machine(SVM) determines the degree and type of fault. Experiments were conducted with three examples, the results show that IMHMDE can effectively extract the feature information according to mechanical faults’ features and improve the efficiency of fault diagnosis.

One Dimensional Convolutional Neural Networks Using Sparse Wavelet Decomposition for Bearing Fault Diagnosis

This paper proposes a novel algorithm for bearing fault diagnosis using sparse wavelet decomposition for feature extraction combined with a multi-scale one dimensional convolutional neural network (1-D CNN). The proposed algorithm consists of three stages. The first stage determines bearing fault frequency bands according to bearing physical parameters and constructs a sparse wavelet decomposition model. The second stage decomposes a raw bearing signal into multi-resolution signals based on a decomposition structure achieved at the second stage. Finally, the decomposed multi-resolution signal features are fed into the sub-neural networks according to the multi-scale 1-D CNN (MSCNN) network, and then the outputs of the final convolutional/polling layers are concatenated into a single channel which is further used as the input to a fully connected layer. In comparison with the other bearing fault diagnosis methods, our proposed algorithm can achieve a higher classification accuracy of 99.85% using the Case Western Reserve University (CWRU) bearing dataset. The proposed algorithm is successfully validated via our designed experiments.

An Adaptive Demodulation Band Segmentation Method to Optimize Spectral Boundary and Its Application for Wheelset-Bearing Fault Detection

Wheelset-bearing working states directly affect the stability of bogies, and existing defects may threaten the running safety of high-speed trains. Thus, the fault detection of wheelset-bearing is of great importance. In this paper, a novel wheelset-bearing fault detection method, named adaptive autocorrelated kurtogram (AAK), is proposed based on the developed autocorrelated kurtosis and the presented spectral segmentation method. Autocorrelated kurtosis is designed to reduce the unrelated components interference and increase the signal-to-noise ratio (SNR). The spectral segmentation method is proposed to obtain the fault zone as completely as possible. Based on the feature that rotation frequency is related to bearing fault frequency, the rotation frequency-based window size selection and extension strategy is proposed. With different frequency levels, the AAK is formed. The proposed method is validated by simulated and experimental data. The results show that this method can automatically and adaptively search for a reasonable demodulation bandwidth according to the fault information feature and resonance region position. The method can estimate the center frequency and bandwidth and avoid unrelated component interference. The AAK can provide accurate detection results and possesses excellent performance. Thus, it is suitable for wheelset-bearing fault detection.

A novel signal denoising method using stationary wavelet transform and particle swarm optimization with application to rolling element bearing fault diagnosis

In this paper, a novel signal denoising method based on Stationary Wavelet Transform and Particle Swarm Optimization Algorithm (SWT-PSO) is proposed. The threshold values for wavelet denoising are selected using the meta-heuristic algorithm of particle swarm optimization such that the denoised signal has maximum weighted kurtosis. In many faulty rotating machinery elements such as rolling element bearings and gears, impact-type vibration signals are produced. Such impact type vibration signals have a high value of kurtosis. The proposed SWT-PSO scheme can be used for the enhancement of fault signature in rotating machinery by increasing the kurtosis of such signals. The proposed algorithm has been applied on simulated as well as experimental defective rolling element bearing signal. Vibration signals from rolling element bearing with inner race and outer race defects are acquired by accelerometers on a Machinery Fault Simulator (MFS) setup. Then the proposed methodology is applied successfully to denoise the signals at various noise levels. Further, Spectrum envelope of the denoised vibration signal is used to obtain the characteristic defect frequencies. The developed methodology is finally compared with widely used Minimum Entropy Deconvolution (MED) algorithm. It has been demonstrated that the proposed algorithm has better capability of extracting the impulsive characteristic as well as preserving the signal geometrical structure than compared to MED.

Dimensional Amplitude Response Analysis of Vibrations Produced by Defects in Rolling Contact Bearings

Usage of rolling contact bearings in variety of rotor-dynamic applications has put forth a need to develop a detailed and easy to implement techniques for the assessment of damage related features in these bearings so that before mechanical failure, maintenance actions can be planned well in advance. In accordance to this, a method based on dimensional amplitude response analysis and scaling laws is presented in this paper for the diagnosis of defects in different components of rolling contact bearings in a dimensionally scaled rotor-bearing system. Rotor, bearing, operating and defect parameters involved are detailed for dimensional analysis using frequency domain vibration data. A defect parameter for modeling all the three dimensions of the defect as well as the different shapes like square, circular, rectangular is put forth which takes into account the volume as well as the surface area of the defect. Experimental data set is generated for the ‘model’ bearing (designated as SKF30205J2/Q) using Box-Behnken design of response surface methodology for solution of the theoretical model by factorial regression approach. Obtained metamodel is then used for the prediction of the objective variable, i.e., Vibration acceleration amplitude at the defect frequency component for other types of ‘test’ bearings (designated as SKF 30305C and SKF 22220 EK) using the developed scaling laws. Confirmation experiments showed that the computable relationship amongst objective variable and the dimensionless parameters can be forecast and correlated.

Experimental Studies on Outer Race Fault Diagnosis of Rolling Element Bearings

Rolling element bearing condition monitoring and fault diagnosis can predict the condition of bearing and its components such as outer race, inner race, rolling elements and cage at any given instant. Online condition monitoring is more advanced and preferred over conventional methods. Advanced signal processing techniques and statistical analysis techniques aid in the component level fault prediction, its severity, interaction of different defect frequencies and remaining life of the component in a given system. However, the basic concept in most of the techniques is vibration data acquisition and identification of amplitude of vibration at defect frequencies. In this paper, the basic concepts of bearing defect frequencies are discussed and shown experimentally the presence of these frequencies. In experimentation 50mm diameter bearings with different faults are mounted on a simple test rig and vibration data is acquired under different loads at 2500 rpm. A simple frequency domain has been carried out to identify the defect.

CLINICAL CHARACTERISTICS OF PREGNANT WOMEN WITH ISOLATED CONGENITAL HEART DEFECTS IN THE FETUS

Relevance. Cardiovascular pathology today remains one of the main causes of morbidity, mortality and early disability in populations; occupies a leading place among developmental defects in children (up to 30 %). Received data from European and domestic studies indicate an increase in the frequency of the congenital heart defects, which is in the range of 8 -9 per 1,000 live births, depending on geographical differences. Despite significant progress in understanding the mechanisms that determine the formation and development of the heart, causes of the isolated congenital heart defects, in most cases, remain uncertain; they require study of the interaction of many factors, both in the prenatal and in the postnatal period.
 Objective. Assessment of clinical diagnostic criteria of pregnant women with isolated congenital heart defects in the fetus for determination of the stages and multidisciplinary approach of their management. 
 Materials and methods. For the period 2020-2021, 30 pregnant women aged 17 to 39 years were examined on the basis of a hospital sample, with various forms of isolated congenital heart defects who were directed from different regions of Ukraine. The work was conducted on the basis of clinical prospective studies on the basis of an obstetric department CCE KCMH № 5. The selection criteria included: somatically healthy women in term 18-39 weeks of pregnancy, with unencumbered pregnancy, natural fertilization, the presence of an isolated heart defect in the fetus, the absence of a risk for chromosomal pathology in the fetus. Stages in the management of pregnant women using a multidisciplinary approach were determined. Common clinical, instrumental and statistical methods were used during the study.
 Results. The average age of women with heart defects in the fetus ranged 28.36±5.08. The weight of women with isolated heart defects in the fetus averaged 70.26±10.09, the height of pregnant women corresponded to 164.86±5.39 cm. In 73% of cases pregnant women were referred to the obstetric hospital by cardiologists, ultrasound specialists and in 27% by obstetricians-gynecologists on an outpatient basis. The average gestational age at which the fetus was first detected by ultrasound was isolated from 22.23±5.04 weeks. The total number of ultrasounds in pregnant women with congenital heart defects in the fetus was 4.80±0.92 times. In 54% of cases women had first pregnancy. 
 Conclusions. Congenital heart defects are not directly dependent on the age of the pregnant woman and her anthropometric data. For the first time, isolated congenital heart defects were determined in second trimester of pregnancy (22 weeks). Considering multifactorial etiology of congenital heart defects, there is a need in a further study of this problem using multidisciplinary approach in diagnosis.

Fault Frequency Identification of Rolling Bearing Using Reinforced Ensemble Local Mean Decomposition

The vibration signal of rolling bearing exhibits the characteristics of energy attenuation and complex time-varying modulation caused by the transmission with multiple interfaces and complex paths. In view of this, strong ambient noise easily masks faulty signs of rolling bearings, resulting in inaccurate identification or even totally missing the real fault frequencies. To overcome this problem, we propose a reinforced ensemble local mean decomposition method to capture and screen the essential faulty frequencies of rolling bearing, further boosting fault diagnosis accuracy. Firstly, the vibration signal is decomposed into a series of preliminary features through ensemble local mean decomposition, and then the frequency components above the average level are energy-enhanced. In this way, principal frequency components related to rolling bearing failure can be identified with the fast spectral kurtosis algorithm. Finally, the efficacy of the proposed approach is verified through both a benchmark case and a practical platform. The results show that the selected fault characteristic components are accurate, and the identification and diagnosis of rolling bearing status are improved. Especially for the signals with strong noise, the proposed method still could accurately diagnose fault frequency.

Ensembles of probabilistic LSTM predictors and correctors for bearing prognostics using industrial standards

Probabilistic prediction of the remaining useful life (RUL) of bearings is critically important, especially in an industrial setting where unplanned maintenance needs, unscheduled equipment downtime, or catastrophic failures can cost a company millions of dollars and threaten worker safety. Current research in the field of bearing prognostics clearly shows the advantage of a deep learning-based solution, but the reliability of purely data-driven predictions is questionable in harsh industrial environments with varying operational conditions. To make this work industrially relevant, we adopt ISO guidelines to determine bearing failure thresholds (specifically ISO 10816), which are defined in the velocity domain, while considering characteristic bearing fault frequencies defined by the geometry of each bearing. We propose a two-stage Long Short-Term Memory (LSTM) model ensemble which includes: (1) a predictor step to forecast and (2) a corrector step to offset the RUL prediction. Each LSTM model within the ensemble is customized to include a Gaussian layer that captures the aleatoric uncertainty in the forecasted parameter, and the ensemble of all the individual LSTM models provides the epistemic uncertainty in the RUL prediction. We demonstrate the implementation of the proposed model on the publicly available Xi'an Jiaotong University and Changxing Sumyoung Technology Co., Ltd. (XJTU-SY) bearing dataset and establish the superiority of the model, both in terms of accuracy as well as uncertainty quantification, when compared against other commonly used techniques in the field of bearing prognostics. The ensemble model tends to explore multiple functional/forecast modes providing better uncertainty estimates when compared to Bayesian counterparts.

Vibration characteristics of the gear shaft-bearing system with compound defects under variable operating conditions

Due to the variable working conditions, there are compound defects in the gear shaft-bearing system easily, vibration signals are very complex, and the fault diagnosis of the system becomes more difficult. Thus, a 36 degrees of freedom (36-DOFs) dynamic model is established for discussing the vibration characteristics of the gear shaft-bearing system, the gear pair spalling defect is considered, there are localized defects on the inner raceway and outer raceway of the supporting bearing, the work conditions contain variable speed, variable load, speed fluctuation, and load fluctuation. The obtained vibration signal is processed by the short-time Fourier transform for the time–frequency distribution map. When the gear shaft-bearing system with compound defects operates under variable conditions, roller passing outer raceway frequency, roller passing inner raceway frequency, gearing meshing frequency, and the relative harmonic frequencies can also be found. The defect frequencies and frequency amplitude are increasing with the speed while the system makes the accelerated movement. While the load acting on the system increases, the defect frequencies remain unchanged, but the frequency amplitude becomes larger. If there are fluctuations of the speed and load, the apparent defect frequency fluctuation and amplitude fluctuation is generated. The mathematical model and the analysis results are verified by the experiment, which will provide the theoretical basis for the fault diagnosis of the gear shaft-bearing system.

Prevalence of Venovenous Shunting and High-Output State Quantified with 4D Flow MRI in Patients with Fontan Circulation.

To assess the ability of four-dimensional (4D) flow MRI to quantify flow volume of the Fontan circuit, including the frequency and hemodynamic contribution of systemic-to-pulmonary venovenous collateral vessels. In this retrospective study, patients with Fontan circulation were included from three institutions (2017-2021). Flow measurements were performed at several locations along the circuit by two readers, and collateral shunt volumes were quantified. The frequency of venovenous collaterals and structural defects were tabulated from concurrent MR angiography, contemporaneous CT, or catheter angiography and related to Fontan clinical status. Statistical analysis included Pearson and Spearman correlation and Bland-Altman analysis. Seventy-five patients (mean age, 20 years; range, 5-58 years; 46 female and 29 male patients) were included. Interobserver agreement was high for aortic output, pulmonary arteries, pulmonary veins, superior vena cava (Glenn shunt), and inferior vena cava (Fontan conduit) (range, ρ = 0.913-0.975). Calculated shunt volume also showed strong agreement, on the basis of the difference between aortic and pulmonary flow (ρ = 0.935). A total of 37 of 75 (49%) of the patients exhibited shunts exceeding 1.00 L/min, 81% (30 of 37) of whom had pulmonary venous or atrial flow volume step-ups and corresponding venovenous collaterals. A total of 12% of patients (nine of 75) exhibited a high-output state (>4 L/min/m2), most of whom had venovenous shunts exceeding 30% of cardiac output. Fontan flow and venovenous shunting can be reliably quantified at 4D flow MRI; high-output states were found in a higher proportion of patients than expected, among whom venovenous collaterals were common and constituted a substantial proportion of cardiac output.Keywords: Pediatrics, MR Angiography, Cardiac, Technology Assessment, Hemodynamics/Flow Dynamics, Congenital Supplemental material is available for this article. © RSNA, 2021.

Should clinical automated perimetry be considered for routine functional assessment of early/intermediate age-related macular degeneration (AMD)? A systematic review of current literature.

PurposeThere is growing interest in functional testing for early/intermediate age‐related macular degeneration (iAMD). However, systematic evaluation of existing clinical functional tests is lacking. This systematic review examines evidence for using clinical automated perimetry in routine assessment of early/iAMD.Recent findingsPubMed, Web of Science Core Collection, and Embase were searched from inception to October 2020 to answer, is there evidence of visual field defects in early/iAMD, and if so, are early/iAMD visual field defects linked to real‐world patient outcomes? Articles using clinical automated perimetry (commercially accessible and non‐modified devices/protocols) were included. Microperimetry was excluded as this has yet to be incorporated into clinical guidelines. The primary outcome was global visual field indices including mean deviation (MD), pattern standard deviation (PSD), mean sensitivity (MS) and frequency of defects. The secondary outcome was any real‐world patient outcome including quality of life and/or activities of daily living indices. Twenty‐six studies were eligible for inclusion and all studies were observational. There was consistent evidence of worsened MD, PSD, MS and frequency of defects for early/iAMD compared to normal eyes under photopic, low‐photopic and scotopic conditions. Meta‐analysis of studies using standard automated perimetry (SAP) under photopic conditions revealed worsened MD (−1.52dB [−2.27, −0.78 dB]) and MS (−1.47dB [−2, −0.94 dB]) in early/iAMD compared to normal eyes, representing large statistical effect sizes but non‐clinically meaningful reductions. There was insufficient data for meta‐analyses regarding other clinical automated perimetry protocols. Only one study assessed a real‐world patient outcome (on‐road driving performance), with no significant link to visual field outcomes in early/iAMD.SummarySignificant reduction of global visual field indices is present in early/iAMD, but not clinically meaningful using SAP under photopic conditions. Translational relevance of visual field outcomes to patient outcomes in early/iAMD remains unclear. Thus, SAP under photopic conditions is unlikely to be useful for routine assessment of early/iAMD.

GATA-1 mutation alters the spermatogonial phase and steroidogenesis in adult mouse testis

GATA-1 is a transcription factor from the GATA family, which features zinc fingers for DNA binding. This protein was initially identified as a crucial regulator of blood cell differentiation, but it is currently known that the Gata-1 gene expression is not limited to this system. Although the testis is also a site of significant GATA-1 expression, its role in testicular cells remains considerably unexplored. In the present study, we evaluated the testicular morphophysiology of adult ΔdblGATA mice with a mutation in the GATA-1 protein. Regarding testicular histology, GATA-1 mutant mice exhibited few changes in the seminiferous tubules, particularly in germ cells. A high proportion of differentiated spermatogonia, an increased number of apoptotic pre-leptotene spermatocytes (Caspase-3-positive), and a high frequency of sperm head defects were observed in ΔdblGATA mice. The main differences were observed in the intertubular compartment, as ΔdblGATA mice showed several morphofunctional changes in Leydig cells. Reduced volume, increased number and down-regulation of steroidogenic enzymes were observed in ΔdblGATA Leydig cells. Moreover, the mutant animal showed lower serum testosterone concentration and high LH levels. These results are consistent with the phenotypic and biometric data of mutant mice, i.e., shorter anogenital index and reduced accessory sexual gland weight. In conclusion, our findings suggest that GATA-1 protein is an important factor for germ cell differentiation as well as for the steroidogenic activity in the testis.

One-bit mMIMO with defective RF chain over Ricean fading in beyond 5G networks

In this paper, we investigate one-bit massive multiple-input multiple-output (mMIMO) systems with defective radio frequency (RF) chains over Ricean fading channels in beyond 5G networks. The approximate achievable rate expressions are derived in closed-form for the scenarios with ideal and non-ideal channel state information by utilizing the Bussgang decomposition. Furthermore, the scaling laws for transmit powers and RF hardware impairments are established when the number of antennas, M, at the base station (BS) grows indefinitely. Specially, the power scaling laws under the case of a large number of Internet of Things (IoT) devices (users) are also investigated. It is revealed that, as M increases, the systems can yield a non-vanishing rate while cutting down the transmit powers of the IoT devices, and/or scaling up the RF impairments of the BS. Moreover, it is also found that the relationship between the number of IoT devices N and M can influence the results of the power scaling laws. Numerical results are conducted to demonstrate our analysis. It is also revealed that the appropriate M can contribute to better energy efficiency.

Adaptive MOMEDA based on improved advance-retreat algorithm for fault features extraction of axial piston pump

The fault information of axial piston pump bearings is inevitably submerged by violent natural periodic impulses. Therefore, an accurate extraction of fault impulses remains a challenging problem. A hybrid method of MOMEDA and TEO is proposed to extract periodic impulses in this study. Firstly, the deconvolution periods of multiple periodic components in the original vibration signals are analysed using Kurtosis. Then, an advance-retreat algorithm is used to optimize the filter length of MOMEDA. After multiple input parameters are determined adaptively, the MOMEDA is used to enhance the various periodic impulses respectively. Finally, TEO demodulation is employed to further obtain fault frequencies. Experimental vibration data is used to verify the advantages of this method for periodic impulses extraction. The results are then compared with traditional deconvolution and decomposition techniques to prove the superior performance of the proposed approach in terms of its better accuracy and reduced processing time.