Shafting misalignment fault diagnosis by means of motor speed signal and SVD-HT method

Aiming at the deficiency of diagnosis method based on vibration signal,a novel method based on speed signal with singular value decomposition and Hilbert transform(SVD-HT)is proposed.The fault diagnosis mechanism based on the speed signal is obtained by constructing the shaft misalignment fault model firstly.Then the SVD-HT method is applied to the processing of the speed signal.The accuracy of the SVD-HT method is verified by comparing the diagnosis results of the order spectrum method and the SVD-HT method.After that,the diagnosis results based on vibration signal and speed signal under no-load and load patterns are compared.Under the no-load pattern,the amplitudes of the speed signal components f_(r),2f_(r) and 4f_(r) are linear with the misalignment.In addition,under the load pattern,the amplitudes of the speed signal components f_(r),2f_(r) and 4f_(r) have a linear relationship with the load.However,the diagnosis result of the vibration signal does not have the above characteristics.The comparison results verify the robustness and reliability of the speed signal and SVD-HT method.The method presented in this paper provides a novel way for misalignment fault diagnosis.

Demonstration of the Source of Motor Program Signal: Study on the Correlation between Muscle Strength and sEMG Signal in Normal Children and Adults

To investigate the relationship between muscle strength and sEMG of biceps brachii during elbow flexion by measuring the maximum muscle strength and sEMG value of normal children and adults, and to analyze their sources, so as to lay a theoretical foundation for the method of motor program reconstruction to restore the function after brain injury, 30 healthy children aged 9 - 10 years and 30 adults aged 20 - 30 years were randomly selected. The muscle strength and sEMG of biceps brachii during elbow flexion were detected and recorded, and the data were statistically analyzed. The muscle strength of children was significantly lower than that of adults (P < 0.001), and the sEMG value of biceps brachii was significantly lower than that of adults (P < 0.001), but the sEMG value per kilogram force of children was significantly higher than that of adults (P < 0.01). The results show that there was a very significant difference in pull (efficiency) between adults and children when there was no significant difference in SEMG signal intensity. This is because although children’s central nervous system has matured, the muscle tissue has not been well trained, resulting in insufficient muscle strength. The muscle strength of adults is significantly higher than that of children, because they have been exercising for a long time after the development of the central nervous system. It is proved that sEMG signal is not produced by muscle contraction itself, but comes from the motor program signal of central nervous system which drives muscle contraction, and it is produced before muscle contraction.

Controller Design for Induction and Brushless Motors Using Matlab with Digital Signal Processor (DSP)

The automation process is a very important pillar for Industry 4.0.One of the first steps is the control of motors to improve production efficiency and generate energy savings.In mass production industries,techniques such as digital signal processing(DSP)systems are implemented to control motors.These systems are efficient but very expensive for certain applications.From this arises the need for a controller capable of handling AC and DC motors that improves efficiency and maintains low energy consumption.This project presents the design of an adaptive control system for brushless AC induction and DC motors,which is functional to any type of plant in the industry.The design was possible by implementing Matlab software and tools such as digital signal processor(DSP)and Simulink.Through an extensive investigation of the state of the art,three models needed to represent the control system have been specified.The first model for the AC motor,the second for the DC motor and the third for the DSP control;this is done in this way so that the probability of failure is lower.Subsequently,these models have been programmed in Simulink,integrating the three main models into one.In this way,the design of a controller for use in AC induction motors,specifically squirrel cage and brushless DC motors,has been achieved.The final model represents a response time of 0.25 seconds,which is optimal for this type of application,where response times of 2e-3 to 3 seconds are expected.

Regulation of specific abnormal calcium signals in the hippocampal CA1 and primary cortex M1 alleviates the progression of temporal lobe epilepsy

Temporal lobe epilepsy is a multifactorial neurological dysfunction syndrome that is refractory,resistant to antiepileptic drugs,and has a high recurrence rate.The pathogenesis of temporal lobe epilepsy is complex and is not fully understood.Intracellular calcium dynamics have been implicated in temporal lobe epilepsy.However,the effect of fluctuating calcium activity in CA1 pyramidal neurons on temporal lobe epilepsy is unknown,and no longitudinal studies have investigated calcium activity in pyramidal neurons in the hippocampal CA1 and primary motor cortex M1 of freely moving mice.In this study,we used a multichannel fiber photometry system to continuously record calcium signals in CA1 and M1 during the temporal lobe epilepsy process.We found that calcium signals varied according to the grade of temporal lobe epilepsy episodes.In particular,cortical spreading depression,which has recently been frequently used to represent the continuously and substantially increased calcium signals,was found to correspond to complex and severe behavioral characteristics of temporal lobe epilepsy ranging from gradeⅡto gradeⅤ.However,vigorous calcium oscillations and highly synchronized calcium signals in CA1 and M1 were strongly related to convulsive motor seizures.Chemogenetic inhibition of pyramidal neurons in CA1 significantly attenuated the amplitudes of the calcium signals corresponding to gradeⅠepisodes.In addition,the latency of cortical spreading depression was prolonged,and the above-mentioned abnormal calcium signals in CA1 and M1 were also significantly reduced.Intriguingly,it was possible to rescue the altered intracellular calcium dynamics.Via simultaneous analysis of calcium signals and epileptic behaviors,we found that the progression of temporal lobe epilepsy was alleviated when specific calcium signals were reduced,and that the end-point behaviors of temporal lobe epilepsy were improved.Our results indicate that the calcium dynamic between CA1 and M1 may reflect specific epileptic behaviors corresponding to different grades.Furthermore,the selective regulation of abnormal calcium signals in CA1 pyramidal neurons appears to effectively alleviate temporal lobe epilepsy,thereby providing a potential molecular mechanism for a new temporal lobe epilepsy diagnosis and treatment strategy.

基于互信息与自适应图卷积的运动想象脑电信号识别

针对运动想象脑电信号非线性特征提取困难、难以有效捕获脑电通道间功能连接关系的问题,提出一种基于互信息和自适应图卷积的运动想象脑电信号分类识别方法。首先,对原始运动想象脑电信号进行子频带划分,提取频域信息;然后,采用互信息神经估计方法构建邻接矩阵,获取脑电信号的非线性关系;最后,设计一种结合CBAM的自适应图卷积网络捕获各通道间的动态关联强度,实现空频特征提取。在BCICompetitionⅣ2a和BCICompetitionⅢ3a数据集上,分别达到83.14%和88.19%的平均准确率,结果表明本文方法能有效获得脑电通道间功能连接关系,为运动想象脑电信号解码提供新思路。

THREE-PHASE BRIDGE INVERTER FOR 9kW DOUBLY SALIENT PERMANENT MAGNET MOTOR

The three-phase bridge inverter is used as the converter topology in the power controller for a 9 kW doubly salient permanent magnet （DSPM） motor. Compared with common three-phase bridge inverters, the proposed inverter works under more complicated conditions with different principles for special winding back EMFs, position signals of hall sensors, and the given mode of switches. The ideal steady driving principles of the inverter for the motor are given. The working state with asymmetric winding back EMFs, inaccurate position signals of hall sensors, and the changing input voltage is analyzed. Finally, experimental results vertify that the given anal ysis is correct.

Design and Development of the DTC Induction Motor Drive for Electric Vehicle

The design and development of the traction controller for electric vehicle is introduced, which is based on the induction motor. This drive is developed by using a digital signal processor at low cost and carried out with the module design concept of both software and hardware. Nevertheless, a scheme of the sensorless direct torque control is based on the developed hardware, of which the feasibility is tested by a trial program. Additionally, both the interface function of the drive hardware and the feasibility of its software are proved to be good by the trail programs. A test motor can run about 18?r/min by a variable frequency program with the space vector pulse width modulation technology, of which the torque is visible pulsatile. In this presentation, based on the theoretical approach, the sensorless torque control is to be studied and applied to electric vehicles, of which the quick, smooth and stable torque response is emphasized because it quite benefits improving the drive performance of electric vehicles.

基于转子位置误差解耦阻抗建模的永磁同步电机电感在线辨识方法

传统永磁同步电机电感在线辨识算法大多基于电机交直轴模型实现,其依赖准确的电机转子位置信息。当无位置传感器控制或位置传感器存在偏置误差时,传统电感辨识方法精度难以保证。为降低转子位置误差影响,该文提出一种基于转子位置误差解耦阻抗建模的永磁同步电机电感参数在线辨识方法。根据电机电感在不同转子位置下的数值变化关系特性,构建转子位置误差解耦的虚拟轴系阻抗模型。进而,通过基于高频正弦信号注入的电感辨识方法,实现不受电机转子位置误差影响的电感辨识。为确保所提出方法在不同运行工况的鲁棒性和准确性,对虚拟轴系的构建方法及注入信号参数选取进行分析。所提出的方法在2.2 kW永磁同步电机平台进行验证,证明了电感参数在线辨识的有效性。

重复性经脊髓磁刺激促进脊髓损伤小鼠运动功能恢复

背景:研究表明重复性经脊髓磁刺激能够抑制脊髓损伤炎症反应,在脊髓区域施加磁场刺激,以调节神经元的兴奋性和突触传递,从而促进神经系统的可塑性和修复。目的:观察重复性经脊髓磁刺激对脊髓损伤后小鼠Toll样受体4/核因子κB/NLRP3信号通路的影响,探讨其对运动功能恢复的机制。方法:将SPF级雄性C57BL/6J小鼠随机分为假手术组、脊髓损伤组、重复性经脊髓磁刺激组,后2组小鼠麻醉后采用咬骨钳剔除T9椎板,暴露脊髓,使用小型动脉瘤夹夹持脊髓20 s建立脊髓损伤模型。重复性经脊髓磁刺激组小鼠脊髓损伤的第1天开始进行为期21 d的重复性经脊髓磁刺激干预。每天持续10 min,每周刺激5 d,休息2 d。于脊髓损伤后1,3,7,14,21 d进行小鼠运动功能BMS评分,利用Western Blot检测脊髓损伤处的水通道蛋白AQP4、凋亡因子Bax、Bcl-2、CL-Caspase-3、炎症因子肿瘤坏死因子α、干扰素γ、白细胞介素6、白细胞介素4和Toll样受体4/核因子κB/NLRP3信号通路相关蛋白的表达,氧化应激试剂盒检测脊髓损伤处超氧化物歧化酶、谷胱甘肽过氧化物酶的活性及丙二醛浓度,免疫荧光染色法检测神经元核抗原的表达。结果与结论:(1)重复性经脊髓磁刺激组小鼠的BMS评分高于脊髓损伤组(P<0.05);(2)与脊髓损伤组相比,重复性经脊髓磁刺激组脊髓含水量降低、AQP4蛋白表达降低;丙二醛浓度、Bax、CL-Caspase-3、肿瘤坏死因子α、干扰素γ、白细胞介素6与Toll样受体4/核因子κB/NLRP3信号通路相关蛋白的表达均降低(P<0.05),而超氧化物歧化酶活性、谷胱甘肽过氧化物酶活性、Bcl-2、白细胞介素4与神经元核抗原的表达均升高(P<0.05);(3)结果说明,重复性经脊髓磁刺激能下调Toll样受体4/核因子κB/NLRP3信号通路相关蛋白的表达,缓解脊髓损伤后的脊髓水肿、氧化应激、凋亡反应和炎症反应等,发挥神经保护作用,从而促进脊髓损伤后小鼠后肢运动功能的恢复。

基于双通道Transformer模型的多维信号故障诊断方法

感应电机在现代工业中有十分重要的作用。然而,电机长时间运行后会变得疲劳从而导致灾难性后果。由于电机故障诊断本质是对电机的时间信号分类,该研究提出双通道Transformer模型,该模型利用电流和振动信号进行诊断,并通过连续小波变换提取频域特征作为输入。双通道Transformer模型将数据的时域和频域信号分别通过Transformer模型,这种替代不仅可以提取时间特征,还可以提取空间特征。实验结果表明,所提出的模型可以提供高达95.36%的诊断准确率,证明其在电机故障诊断中的有效性。与传统的单信号故障诊断方法相比,该模型具有更好的鲁棒性和准确性。

An Intelligent Harmonic Synthesis Technique for Air-Gap Eccentricity Fault Diagnosis in Induction Motors

Induction motors （IMs） are commonly used in various industrial applications. To improve energy con- sumption efficiency, a reliable IM health condition moni- toring system is very useful to detect IM fault at its earliest stage to prevent operation degradation, and malfunction of IMs. An intelligent harmonic synthesis technique is pro- posed in this work to conduct incipient air-gap eccentricity fault detection in IMs. The fault harmonic series are syn- thesized to enhance fault features. Fault related local spectra are processed to derive fault indicators for IM air- gap eccentricity diagnosis. The effectiveness of the pro- posed harmonic synthesis technique is examined experi- mentally by IMs with static air-gap eccentricity and dynamic air-gap eccentricity states under different load conditions. Test results show that the developed harmonic synthesis technique can extract fault features effectively for initial IM air-gap eccentricity fault detection.

Position Sensorless Control for Permanent Magnet Synchronous Motor Using Sliding Mode Observer

An approach of position sensorless control for permanent magnet synchronous motor （ PMSM ） is put forward based on a sliding mode observer. The mathematical model of PMSM in a stationary αβ reference frame is adopted, and the system is controlled by the digital signal processor （ DSP; TMS320LF2407 according to the control achieve closed loop operation of the motor, the stator theory of sliding mode observer. In order to magnetic field should be vertical with the rotor magnetic field and be synchronous with rotor rotating, so the position and speed of PMSM is estimated in real time and the estimated position is modified continuously. The simulation results indicate that the proposed observer has high precision is more robust to the parametric variation and load in estimation of PMSM position and speed, and torque disturbance.

Fault Detection and Identification Using Deep Learning Algorithms in Induction Motors

Owing to the 4.0 industrial revolution condition monitoring maintenance is widely accepted as a useful approach to avoiding plant disturbances and shutdown.Recently,Motor Current Signature Analysis(MCSA)is widely reported as a condition monitoring technique in the detection and identification of individual andmultiple Induction Motor(IM)faults.However,checking the fault detection and classification with deep learning models and its comparison among them selves or conventional approaches is rarely reported in the literature.Therefore,in this work,wepresent the detection and identification of induction motor faults with MCSA and three Deep Learning(DL)models namely MLP,LSTM,and 1D-CNN.Initially,we have developed the model of Squirrel Cage induction motor in MATLAB and simulated it for single phasing and stator winding faults(SWF)using Fast Fourier Transform(FFT),Short Time Fourier Transform(STFT),and Continuous Wavelet Transform(CWT)to detect and identify the healthy and unhealthy conditions with phase to ground,single phasing and in multiple fault conditions using Motor Current Signature Analysis.The faults impact on stator current is presented in the time and frequency domain(i.e.,power spectrum).The simulation results show that the scalogram has shown good results in time-frequency analysis for fault and showing its impact on the energy of current during individual fault and multiple fault conditions.This is further investigated with three deep learning models(i.e.,MLP,LSTM,and 1D-CNN)for checking the fault detection and identification(i.e.,classification)improvement in a three-phase induction motor.By simulating the three-phase induction motor in various healthy and unhealthy conditions in MATLAB,we have collected current signature data in the time domain,labeled them accordingly and created the 50 thousand samples dataset for DL models.All the DL models are trained and validated with a suitable number of architecture layers.By simulation,the multiclass confusion matrix,precision,recall,and F1-score are obtained in several conditions.The result shows that the stator current signature of the motor can be used to detect individual and multiple faults.Moreover,deep learning models can efficiently classify the induction motor faults based on time-domain data of the stator current signature.In deep learning(DL)models,the LSTM has shown better accuracy among all other three models.These results show that employing deep learning in fault detection and identification of induction motors can be very useful in predictive maintenance to avoid shutdown and production cycle stoppage in the industry.

An Adaptive EMD Technique for Induction Motor Fault Detection

Reliable induction motor (IM) fault detection techniques are very useful in industries to diagnose IM defects and improve operational performance. An adaptive empirical mode decomposition (EMD) technology is proposed in this paper for rotor bar fault detection in IMs. As the characteristic fault frequency will change with operating conditions related to load and speed, the proposed adaptive EMD technique correlates fault features over different frequency bands and intrinsic mode function (IMF) sidebands. The adaptive EMD technique uses the first IMF to detect the fault type and the second IMF as an indicator to predict the fault severity. It can overcome the problems of the sensitivity of sideband frequencies related to the speed and load oscillations. The effectiveness of the proposed adaptive EMD technique is verified by experimental tests under different motor conditions.

Wavelet Transform and Neural Networks in Fault Diagnosis of a Motor Rotor

In the motor fault diagnosis technique, vibration and stator current frequency components of detection are two main means. This article will discuss the signal detection method based on vibration fault. Because the motor vibration signal is a non-stationary random signal, fault signals often contain a lot of time-varying, burst proper- ties of ingredients. The traditional Fourier signal analysis can not effectively extract the motor fault characteristics, but are also likely to be rich in failure information but a weak signal as noise. Therefore, we introduce wavelet packet transforms to extract the fault characteristics of the signal information. Obtained was the result as the neural network input signal, using the L-M neural network optimization method for training, and then used the BP net- work for fault recognition. This paper uses Matlab software to simulate and confirmed the method of motor fault di- agnosis validity and accuracy

33% Classification Accuracy Improvement in a Motor Imagery Brain Computer Interface

A right-hand motor imagery based brain-computer interface is proposed in this work. Such a system requires the identification of different brain states and their classification. Brain signals recorded by electroencephalography are naturally contaminated by various noises and interferences. Ocular artifact removal is performed by implementing an auto-matic method “Kmeans-ICA” which does not require a reference channel. This method starts by decomposing EEG signals into Independent Components;artefactual ones are then identified using Kmeans clustering, a non-supervised machine learning technique. After signal preprocessing, a Brain computer interface system is implemented;physiologically interpretable features extracting the wavelet-coherence, the wavelet-phase locking value and band power are computed and introduced into a statistical test to check for a significant difference between relaxed and motor imagery states. Features which pass the test are conserved and used for classification. Leave One Out Cross Validation is performed to evaluate the performance of the classifier. Two types of classifiers are compared: a Linear Discriminant Analysis and a Support Vector Machine. Using a Linear Discriminant Analysis, classification accuracy improved from 66% to 88.10% after ocular artifacts removal using Kmeans-ICA. The proposed methodology outperformed state of art feature extraction methods, namely, the mu rhythm band power.

Processing Human Colonic Pressure Signals by Using Overdetermined ICA

Independent component analysis （ICA） is a widely used method for blind source separation （BSS）. The mature ICA model has a restriction that the number of the sources must equal to that of the sensors used to collect data, which is hard to meet in most practical cases. In this paper, an overdetermined ICA method is proposed and successfully used in the analysis of human colonic pressure signals. Using principal component analysis （PCA）, the method estimates the number of the sources firstly and reduces the dimensions of the observed signals to the same with that of the sources; and then, Fast- ICA is used to estimate all the sources. From 26 groups of colonic pressure recordings, several colonic motor patterns are extracted, which riot only prove the effectiveness of this method, but also greatly facilitate further medical researches.

Industrial Fault Signals Propagation and Current Signature Analysis

Motor current signature analysis provides good results in laboratory environment. In real life situation, electrical machines usually share voltage and current from common terminals and would easily influence each other. This will result in considerable amount of interferences among motors and doubt in identity of fault signals. Therefore, estimating the mutual influence of motors will help identifying the original signal from the environmental noise. This research aims at modelling the propagation of signals that are caused by faults of induction motors in power networks. Estimating the propagation pattern of fault signal leads to a method to discriminate and identify the original source of major events in industrial networks. Simulation results show that source of fault could be identified using this approach with a higher certainty than anticipated output coming of any individual diagnosis.

Fault Detection for PMSM Motor Drive Systems by Monitoring Inverter Input Currents

This paper has proposed a fault detecting method for DC supplied permanent magnet synchronize motor(PMSM)drive systems by monitoring the drive DC input current.This method is based on the fault signal propagation from the torque disturbance on the motor shaft to the inverter input currents.The accuracy of this fault signal propagation is verified by the Matlab simulation and experiment tests with the emulated faulty conditions.The feasible of this approach is shown by the experimental test conducted by the Spectra test rig with the real gearbox fault.This detection scheme is also suitable for monitoring other drive components such as the power converter or the motor itself using only one set of current transducers mounted at the DC input side.

The Change of Spectral Energy Distribution of Surface EMG Signal During Forearm Action Process

Spectral energy distribution of surface EMG signal is often used but difficultly and effectively control artificial limb, because the spectral energy distribution changes in the process of limb actions. In this paper, the general characteristics of surface EMG signal patterns were firstly characterized by spectral energy change. 13 healthy subjects were instructed to execute forearm supination （FS） and forearm pronation （FP） with their right foreanns when their forearm muscles were ＂fatigue＂ or ＂relaxed＂. All surface EMG signals were recorded from their right forearm flexor during their right forearm actions. Two sets of surface EMG signals were segmented from every surface EMG signal appropriately at preparing stage and acting stage. Relative wavelet packet energy （symbolized by pnp and pna respectively at preparing stage and acting stage, n denotes the nth frequency band） of surface EMG signal firstly was calculated and then, the difference （Pn = Pna-Pnp） were gained. The results showed that Pn from some frequency bands can effectively characterize the general characteristics of surface EMG signal patterns. Compared with Pn in other frequency bands, P4, the spectral energy change from 93.75 to 125 Hz, was more appropriately regarded as the features.