Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.

The W transform and its improved methods for time-frequency analysis of seismic data

The conventional linear time-frequency analysis method cannot achieve high resolution and energy focusing in the time and frequency dimensions at the same time,especially in the low frequency region.In order to improve the resolution of the linear time-frequency analysis method in the low-frequency region,we have proposed a W transform method,in which the instantaneous frequency is introduced as a parameter into the linear transformation,and the analysis time window is constructed which matches the instantaneous frequency of the seismic data.In this paper,the W transform method is compared with the Wigner-Ville distribution(WVD),a typical nonlinear time-frequency analysis method.The WVD method that shows the energy distribution in the time-frequency domain clearly indicates the gravitational center of time and the gravitational center of frequency of a wavelet,while the time-frequency spectrum of the W transform also has a clear gravitational center of energy focusing,because the instantaneous frequency corresponding to any time position is introduced as the transformation parameter.Therefore,the W transform can be benchmarked directly by the WVD method.We summarize the development of the W transform and three improved methods in recent years,and elaborate on the evolution of the standard W transform,the chirp-modulated W transform,the fractional-order W transform,and the linear canonical W transform.Through three application examples of W transform in fluvial sand body identification and reservoir prediction,it is verified that W transform can improve the resolution and energy focusing of time-frequency spectra.

基于MUSIC算法特征值损伤因子的板状结构损伤程度评估

研究了多重信号分类(MUSIC)算法在板状结构损伤检测中的应用,提出一种基于MUSIC算法特征值的损伤因子,为基于MUSIC算法的板状结构损伤成像技术提供了一种可靠的损伤程度评估理论。首先利用MUSIC算法计算的高精度特征值和Lamb波损伤散射信号幅值的相关性,采用Abaqus有限元仿真软件模拟不同程度的损伤,将板状结构中的损伤成像定位之后,根据散射信号将结构损伤程度转化为特征值变化量,根据特征值计算损伤因子,建立损伤评估模型预测损伤程度,并通过试验验证其正确性。试验结果表明,在合适的激励频率下,特征值损伤因子随着损伤程度的增加呈现出线性变化,能较好地反映损伤程度;该方法具有较高的准确性和稳定性,在一定损伤程度内能够有效地反映结构损伤程度。

MUSIC学习动机模型在仪器分析教学中的实践与研究

基于MUSIC(Empowerment-Usefulness-Success-Interest-Caring)学习动机模型开展仪器分析教学工作,是目前调动学生学习积极性和锻炼自主学习能力的重要依托。文章立足MUSIC学习动机模型赋权分析、有用研究、成功探索、兴趣指引、关怀体验步骤,深入分析实践应用准则,并围绕现有教学不足确定优化方向,通过明晰学习任务目标、探寻知识需求、构建多样实践活动、立足教材整合资源、师生深度互动评价等策略,促进高职学生良性成长。

基于卡尔曼滤波与MUSIC算法的传感阵列定向方法

对局放源信号的准确测量与有效识别是局部放电检测、定位及分析的关键。受电磁传感器方向特性灵敏度局限,现有基于到达角(AOA)定位的局部放电定位方法主要运用于声音信号。为此,提出一种卡尔曼滤波算法与多信号分类器(MUSIC)算法相结合的特高频传感阵列定向方法,即首先采用卡尔曼滤波算法能够有效处理电磁幅值信号,减小信号波动性及测量误差,大大提升信号测量精度;然后针对单一传感器建立了传感器方向性的参考矩阵,对任意来波信号使用MUSIC算法进行数据匹配,从而得到精确的来波方向;最后经过试验验证,所提算法可将传感器阵列的方向识别结果误差减小至5°以内,提升了测量精度。

基于DSP的快速MUSIC测角算法

基于阵列信号处理的MUSIC算法,由于其超高的角分辨率和角精度,受到广泛的关注。但是其高运算复杂度,较大地限制了算法的硬件实现。为此,基于MUSIC算法的基本原理,文中分析了该算法在DSP(TI1843)硬件系统实现中关键子步骤的运行时长。针对最为耗时和占用内存最大的空间谱构建和谱峰搜索子步骤,分别提出了便于工程实现的简化方法,并通过硬件平台移植验证了算法的可行性,对比发现该方法大大减少了运算时间和内存空间,具有较高实际应用价值。

改进MUSIC算法的超声波测风方法研究

针对传统超声波测风装置测风精度不高、抗噪声能力弱,提出了一种改进多重信号分类(multiple signal classification,MUSIC)算法的超声波测风方法。采用一种弧形6阵元超声波传感器阵列的测风结构,推导其阵列流型;在此基础上,添加小波阈值降噪算法提高信号信噪比,降低噪声信号协方差矩阵的秩;再使用PHAT加权广义互相关时延估计算法以提高时延估计的准确性,同时根据时延关系对传统MUSIC算法矢量矩阵进行改进;最后通过MUSIC算法实现对风速风向的测量。理论分析与仿真结果表明:改进后的MUSIC算法具有较好的抗噪性能和较高的风参数测量精度,测量风速绝对误差达到0.15 m/s,风向绝对误差达到2°,可以应用于对风参数要求较高的场景。

基于弧形阵列的局部放电Dir-MUSIC定位算法

局部放电是衡量电力设备绝缘状态的重要指标,局放检测需要解决局放源定位问题。多重信号分类(multiple signal classification,MUSIC)采用全向天线作为接收阵列,可实现多源信号的超分辨率空间谱估计,但要求高信号采样率,且在低信噪比情况下抗干扰能力不足。为此,提出基于弧形阵列的Dir(directional)-MUSIC算法,采用定向天线接收信号的强度信息,实现低信噪比下的局放源波达方向估计。设计了接收局放信号的Vivaldi天线阵列,并在不同信噪比下对算法的有效性进行仿真验证。结果表明:在低信噪比-10 dB来波方向5°下角度误差为0.14°,优于MUSIC算法;阵列在信噪比10 dB,测向范围[-80°,80°]内定位均方根误差小于1.5°。证明了基于弧形阵列的Dir-MUSIC算法有效提高了局放定位精度,且对噪声具有良好的鲁棒性,具有用于局放检测的潜力。

A reliability-oriented genetic algorithm-levenberg marquardt model for leak risk assessment based on time-frequency features

Since leaks in high-pressure pipelines transporting crude oil can cause severe economic losses,a reliable leak risk assessment can assist in developing an effective pipeline maintenance plan and avoiding unexpected incidents.The fast and accurate leak detection methods are essential for maintaining pipeline safety in pipeline reliability engineering.Current oil pipeline leakage signals are insufficient for feature extraction,while the training time for traditional leakage prediction models is too long.A new leak detection method is proposed based on time-frequency features and the Genetic Algorithm-Levenberg Marquardt(GA-LM)classification model for predicting the leakage status of oil pipelines.The signal that has been processed is transformed to the time and frequency domain,allowing full expression of the original signal.The traditional Back Propagation(BP)neural network is optimized by the Genetic Algorithm(GA)and Levenberg Marquardt(LM)algorithms.The results show that the recognition effect of a combined feature parameter is superior to that of a single feature parameter.The Accuracy,Precision,Recall,and F1score of the GA-LM model is 95%,93.5%,96.7%,and 95.1%,respectively,which proves that the GA-LM model has a good predictive effect and excellent stability for positive and negative samples.The proposed GA-LM model can obviously reduce training time and improve recognition efficiency.In addition,considering that a large number of samples are required for model training,a wavelet threshold method is proposed to generate sample data with higher reliability.The research results can provide an effective theoretical and technical reference for the leakage risk assessment of the actual oil pipelines.

Research on Low Voltage Series Arc Fault Prediction Method Based on Multidimensional Time-Frequency Domain Characteristics

The load types in low-voltage distribution systems are diverse.Some loads have current signals that are similar to series fault arcs,making it difficult to effectively detect fault arcs during their occurrence and sustained combustion,which can easily lead to serious electrical fire accidents.To address this issue,this paper establishes a fault arc prototype experimental platform,selects multiple commonly used loads for fault arc experiments,and collects data in both normal and fault states.By analyzing waveform characteristics and selecting fault discrimination feature indicators,corresponding feature values are extracted for qualitative analysis to explore changes in timefrequency characteristics of current before and after faults.Multiple features are then selected to form a multidimensional feature vector space to effectively reduce arc misjudgments and construct a fault discrimination feature database.Based on this,a fault arc hazard prediction model is built using random forests.The model’s multiple hyperparameters are simultaneously optimized through grid search,aiming tominimize node information entropy and complete model training,thereby enhancing model robustness and generalization ability.Through experimental verification,the proposed method accurately predicts and classifies fault arcs of different load types,with an average accuracy at least 1%higher than that of the commonly used fault predictionmethods compared in the paper.

一种奇异值分解与子空间加权联合的改进MUSIC算法

在低信噪比、小快拍数等非理想条件下,经典DOA估计算法对邻近目标的分辨率严重下降,甚至失去分辨能力。针对这一问题,提出了一种将重构的接收信号协方差矩阵进行奇异值分解并与改进的加权子空间方法相结合的改进算法。该算法充分利用互相关信息构建新的接收信号协方差矩阵,并对噪声子空间信息采用新的校正方法,对噪声特征值进行改造,之后对噪声子空间进行加权,最后与信号子空间加权技术相联合。仿真实验证明,改进算法在低信噪比和小快拍数条件下可以分辨间隔4°的相邻目标,统计分析表明该算法的分辨率明显优于经典MUSIC算法。

利用改进MUSIC方法进行洋流方位估计

洋流能够调节全球热量的分布,降低航运成本。以往的洋流方位估计方法未对洋流信号源之间的相关性进行完全消除,导致估计效果较差。因此,本文设计了基于改进MUSIC方法的洋流方位估计方法。首先,以采集到的洋流信号为基础,构建洋流阵列信号模型,并对洋流信号进行降噪处理,提高信号数据的质量。然后,为减少洋流信号源之间相关性对估计结果的影响,利用改进MUSIC方法及协方差矩阵,对洋流信号源相关性进行消除,通过计算信号源的相关参数,构建洋流方位估计模型。最后,通过对洋流信号源的转换,实现洋流方位的估计。在仿真试验中,以南海部分海域为试验对象,对不同谱点下估计方法的估计效果进行评价,与以往的洋流方位估计方法相比,设计的基于改进MUSIC方法的洋流方位估计方法精度高达97.2%,应用效果更好。

降维长矢量MUSIC算法的高速并行FPGA实现

在极化敏感圆阵通过降维长矢量多重信号分类(DRLV-MUSIC)算法实现DOA估计的现场可编程门阵列(FPGA)实现时,为了进一步缩短计算时间的同时节省硬件占用资源,提出一种特征值与特征向量并行计算的FPGA实现结构,同时利用矩阵相乘的重复性,提出一种并行矩阵运算方法,缩短FPGA的计算时间同时节约硬件资源。该FPGA方案由预处理模块、协方差矩阵计算模块、并行Jacobi算法计算特征值模块、并行特征向量计算模块组成。实验结果表明,与非并行的DRLV-MUSIC算法相比,该方案在减少计算时间、降低硬件占用资源的同时保证了测角精度。

The Role of Music Therapy in Supporting Intellectually Disabled Youth in Senegal

Introduction: Music therapy is a practice for helping and supporting people with intellectual and relational difficulties. This study illustrated the benefits of music therapy for young people living with intellectual disabilities (YLID) in an African context. Methodology: This study investigated six young individuals with intellectual disabilities who had undergone three years of music therapy. They were participants in the inclusive non-academic training program at the National School of Arts in Dakar from 2017 to 2019. Data collection utilized individual interviews with the youths, evaluation grids from teachers and psychiatrists. Guardians provided informed consent along with the assent of the young participants. Results: The six young were aged between 18 and 30 years old, with an average age of 24.6 years. Four of the YLID were male. Three young people with intellectual disabilities had delayed psychomotor development. Observations revealed the beneficial influence of music therapy on the health and well-being of young individuals. Music played a role in alleviating stress and anxiety among youth with intellectual disabilities (YLID), enhancing their mood and mental health. It assisted in navigating challenging situations and heightened alertness among YLID. Additionally, music therapy contributed to improvements in dyslexia, fine and gross motor skills, and memory development among intellectually disabled youth, ultimately facilitating their integration into society. Conclusion: In light of our results, music therapy makes a major contribution to the empowerment of YLID. Engaging in musical activities helps young people connect with others through instrumental expression and a sense of accomplishment. By facilitating music therapy, it becomes possible to combat discrimination and stigmatization, thus promoting the social inclusion of intellectually disabled youth. Therefore, it is important to promote music therapy in Senegal to meet the needs of YLID.

Effectiveness of Music-Based Therapeutic Intervention on People with Dementia: A Rapid Review

Background: Dementia is a condition with progressive cognitive dysfunction and manifestation of both behavioral and psychosocial symptoms. Non-pharmacological measures such as music therapy are gaining importance since efficacy and safety of people with dementia have been questionable for pharmacological measures. Patient’s response to music is persistent even in the later stage of dementia. Aim: This rapid review aims to identify, analyze, evaluate, and summarize the best available evidence on the effectiveness of music-based therapeutic interventions among people with dementia. Method: CINAHL Cochrane Library, internet websites of rapid review producers, and reference lists were searched to identify articles for inclusion. Two reviewers independently screened the literature search results. Effectiveness, music-based therapeutic intervention, dementia, Alzheimer’s disease, systematic review and systematic review with meta-analysis terms were used to abstract data from included studies. Main Findings: 11 SRs and SRs with meta-analysis were reviewed which revealed positive effect of music therapy on five major outcomes with 9 studies effect on behavioral outcome, 6 studies with positive effect on psychosocial outcome reducing anxiety, 6 with improved cognition, 1 study revealed with improved quality of life and 1 study revealed effect on physiological outcomes. Conclusion: Music therapy has positive effect on treatment of dementia but further studies with larger sample size and specified to single intervention should be conducted to provide generalisable and precise results on this topic.

局部放电定向超声阵列Dir-MUSIC测向算法仿真

超声波检测方法在电力设备绝缘状态检测定位中应用广泛。针对局部放电超声测向MUSIC算法存在的采样率要求高、计算复杂度大等不足,提出基于定向超声阵列信号强度信息的定向多重信号分类(directional multiple signal classification,Dir-MUSIC)算法。在阐述该算法理论模型和应用条件基础上,针对均匀圆盘超声阵列,仿真研究了不同增益方向图主瓣宽度、不同信噪比条件下Dir-MUSIC算法的测向精度。仿真结果表明8阵元阵列在-5 dB信噪比、方向图主瓣宽度为90°~120°时测向精度最高,均方根误差小于2°。最后基于研制的微型机电系统麦克风(microelectro-mechanical system,MEMS)定向超声阵列进行了测向试验,结果表明8阵元圆盘超声阵列测向均方根误差最小为2.76°,测向标准差最小为2.72°,验证了Dir-MUSIC算法的有效性与准确性。

A Time-Frequency Associated MUSIC Algorithm Research on Human Target Detection by Through-Wall Radar

In this paper,a time-frequency associated multiple signal classification(MUSIC)al-gorithm which is suitable for through-wall detection is proposed.The technology of detecting hu-man targets by through-wall radar can be used to monitor the status and the location information of human targets behind the wall.However,the detection is out of order when classical MUSIC al-gorithm is applied to estimate the direction of arrival.In order to solve the problem,a time-fre-quency associated MUSIC algorithm suitable for through-wall detection and based on S-band stepped frequency continuous wave(SFCW)radar is researched.By associating inverse fast Fouri-er transform(IFFT)algorithm with MUSIC algorithm,the power enhancement of the target sig-nal is completed according to the distance calculation results in the time domain.Then convert the signal to the frequency domain for direction of arrival(DOA)estimation.The simulations of two-dimensional human target detection in free space and the processing of measured data are com-pleted.By comparing the processing results of the two algorithms on the measured data,accuracy of DOA estimation of proposed algorithm is more than 75%,which is 50%higher than classical MUSIC algorithm.It is verified that the distance and angle of human target can be effectively de-tected via proposed algorithm.

DQ变换和MUSIC算法在ITER磁体电源信号间谐波检测中的应用

随着国际热核聚变实验堆(International Thermonuclear Experimental Reactor,ITER)计划的逐步开展,保证ITER磁体电源系统的稳定运行显得尤为重要。文章采用将DQ变换和多信号分类(multiple signal classification,MUSIC)算法相结合的方法进行间谐波频率检测,信号的幅度和相位由最小二乘法来估计。DQ变换可以消除大幅度ITER基波分量,MUSIC算法可以通过矩阵特征分解检测出短数据条件下的谐波和间谐波,适用短时平稳的间谐波检测,两者相结合可以有效检测出大幅度基波附近存在小幅度间谐波。仿真实验表明,计算经DQ变换后检测出的ITER信号谐波频率时,取中间信号计算真实频谱较为正确,两侧信号则有较大的误差。

Classification of musical hallucinations and the characters along with neural-molecular mechanisms of musical hallucinations associated with psychiatric disorders

BACKGROUND Musical hallucinations(MH)involve the false perception of music in the absence of external stimuli which links with different etiologies.The pathomechanisms of MH encompass various conditions.The etiological classification of MH is of particular importance and offers valuable insights to understand MH,and further to develop the effective treatment of MH.Over the recent decades,more MH cases have been reported,revealing newly identified medical and psychiatric causes of MH.Functional imaging studies reveal that MH activates a wide array of brain regions.An up-to-date analysis on MH,especially on MH comorbid psychiatric conditions is warranted.AIM To propose a new classification of MH;to study the age and gender differences of MH in mental disorders;and neuropathology of MH.METHODS Literatures searches were conducted using keywords such as“music hallucination,”“music hallucination and mental illness,”“music hallucination and gender difference,”and“music hallucination and psychiatric disease”in the databases of PubMed,Google Scholar,and Web of Science.MH cases were collected and categorized based on their etiologies.The t-test and ANOVA were employed(P<0.05)to compare the age differences of MH different etiological groups.Function neuroimaging studies of neural networks regulating MH and their possible molecular mechanisms were discussed.RESULTS Among the 357 yielded publications,294 MH cases were collected.The average age of MH cases was 67.9 years,with a predominance of females(66.8%females vs 33.2%males).MH was classified into eight groups based on their etiological mechanisms.Statistical analysis of MH cases indicates varying associations with psychiatric diagnoses.CONCLUSION We carried out a more comprehensive review of MH studies.For the first time according to our knowledge,we demonstrated the psychiatric conditions linked and/or associated with MH from statistical,biological and molecular point of view.

一种改进的MUSIC算法在干扰源定位中的应用

在外场开展系统级电磁兼容性测试时,对于电磁发射类测试项目,为了将EUT信号与干扰信号区分开,需要对干扰源进行定位。利用基于阵列旋转的MUSIC算法(Multiple Signal Classification,多重信号分类)求解多信号的DOA(Direc-tion of Arrival,来波方向),能通过增加虚拟等效阵元的方式突破经典MUSIC算法信号数必须小于阵元数的限制,使MUSIC算法的应用范围扩大。