Angular Signal Research Articles

A fault diagnosis method for rotating machinery with variable speed based on multi-feature fusion and improved ShuffleNet V2

Using deep learning to classify the time-frequency images of bearing vibration signals has become a mainstream method in the field of fault diagnosis. Most studies, however, assume a constant rotational speed, and the accuracy and reliability of the diagnosis model diminishes once the rotational speed changes. Moreover, due to the large model size and high computational complexity, the convolutional neural networks are not suitable for industrial applications. This paper proposes a novel fault diagnosis method for rotating machinery with variable speed based on multi-feature fusion and improved ShuffleNet V2. First, complementary ensemble empirical mode decomposition is used to denoise the time-domain signal. Then the denoised time domain signal is converted into an angular domain signal using the resampling technique, while the envelope spectrums of the angular domain signals are obtained by the Hilbert transform, and the three signals are fused into an red-green-blue image form to enhance the sample features. Finally, to perform fast and accurate classification of the features, the squeeze and excitation blocks are placed after the branch splicing operation of ShuffleNet V2 to enhance the recognition accuracy, and the rectified linear unit activation function is replaced by the HardSwish activation function to avoid necrosis. The experimental results show that the fault recognition accuracy of the proposed method for rolling bearings under variable speed is 96.4%, and the size of the fault diagnosis model is 7.82 MB, indicating that the method can effectively improve the accuracy and ensures that the model size does not increase significantly.

Sensorless Brushless Motor Phase Compensation Speed Control

This paper proposes angular velocity vector feedback speed control system, the amplitude and phase signals of diagonal velocity are extracted, and signals are converted into current and voltage vector signals to achieve high efficiency and speed precision. Brushless motor rotational stability on speed and torque are classified, comparative characteristics analysis of dynamic speed and torque between conventional method and proposed method are simulated, results consisting of speed accuracy, the waveform of stator current and back electromotive force of each phase are all compared and analyzed, verifying fitness and validation of the proposed method.

A Digital Control Structure for Lissajous Frequency-Modulated Mode MEMS Gyroscope

This article proposes a digital control structure for a doubly decoupled gyroscope working in the Lissajous frequency-modulated (LFM) mode based on digital phase-locked loops (PLLs), which demodulates the angular rate signal directly from the readable digital gyroscope resonance frequency, eliminating the need for specific frequency readout circuits. The resonance frequencies of the LFM gyroscope working modes contain a mode mismatch frequency modulated by input angular rate, respectively, which are tracked by two digital PLLs followed by subsequent digital demodulation and filtering. A linearized model of the digital PLL is built to analyze noise and control characteristics for different PI parameters. The impact of different amplitude–phase extraction architectures on the extracted phase signal is also addressed in this article. Contrast experiments are carried out using the same gyroscope with large internal thermal stress due to the silicon-glass bonding process and no stress relief structure around the sensing element, working in the traditional amplitude-modulated (AM) mode and the LFM mode. Overall, the LFM working mode maintains its low-temperature sensitivity and high stability in spite of the large internal thermal stress in the gyroscope compared to AM working modes. The maximum scale factor variation over the temperature range from 10 °C to 50 °C is 1000 ppm for the LFM mode compared to 51 900 ppm for the AM closed mode. The maximum zero rate output drift over the same temperature range is 0.1248 °/s for the LFM mode and 10.7139 °/s for the AM closed mode. The scale factor nonlinearity is 329 ppm with an angular rate input range of ±50 °/s for the LFM mode compared to 1902 ppm for the AM closed mode. The LFM mode zero-bias fluctuation for ten days is less than 0.12 °/s. The angle random walk (ARW) and the bias instability (BI) of the LFM gyroscope are 0.316 °/ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\surd \text{h}$ </tex-math></inline-formula> and 2 °/h, respectively.

Model and Data-Driven Combination: A Fault Diagnosis and Localization Method for Unknown Fault Size of Quadrotor UAV Actuator Based on Extended State Observer and Deep Forest.

The rotor is an essential actuator of quadrotor UAV, and is prone to failure due to high speed rotation and environmental disturbances. It is difficult to diagnose rotor faults and identify the fault localization simultaneously. In this paper, we propose a fault diagnosis and localization scheme based on the Extended State Observer (ESO) and Deep Forest (DF). This scheme can accurately complete the fault diagnosis and localization for the quadrotor UAV actuator without knowing the fault size by combining the model-based and the data-driven methods. First, we obtain the angular acceleration residual signal of the quadrotor UAV by using ESO. The residual signal is the difference between the observed state of ESO and the true fault state. Then, we design the residual feature analysis method by considering the position distribution of the quadrotor UAV actuator. This method can embed the actuator fault localization information into the fault data by simultaneously considering pitch and roll of the quadrotor UAV. Finally, we complete the fault diagnosis and localization of the quadrotor UAV actuator by processing the fault data by using DF. This scheme has the advantages of straightforward observer modeling, strong generalization ability, adaptability to small sample data, and few hyperparameters. Our simulation results indicate that the accuracy of the proposed scheme reaches more than 99% for the unknown size of the quadrotor UAV actuator fault.

Object Composition Identification by Measurement of Local Radio Frequency Magnetic Fields with an Atomic Magnetometer

Proof of principle of object composition identification based on inductive measurements with an atomic magnetometer has been demonstrated in highly engineered laboratory conditions. Progress in the development of portable miniaturised magnetometers has encouraged on the parallel development of the measurement technologies involving this sensor, in particular concepts that would enable operation in complex test scenarios. Here, we explore the problem of material identification in the context of measurements performed with variable distance between the object and the primary radio-frequency field source and sensor. We identify various aspects of the measurement affected by variable distance and discuss possible solutions, based on the signal phase analysis, a combination of frequency and angular signal dependencies and the implementation of a pair of excitation coils.

Wavefront Sensing with a Coupled Cavity for Torsion-Bar Antenna

Torsion-Bar Antenna (TOBA) is a ground-based gravitational wave detector using torsion pendulums. TOBA can detect intermediate-mass black hole binary mergers, gravitational wave stochastic background, and Newtonian noise, and is useful for earthquake early warning. A prototype detector Phase-III TOBA with 35 cm-scale pendulums is under development to demonstrate noise reduction. The target strain sensitivity is set to $1\times10^{-15}\,{/\sqrt{\rm Hz}}$ between 0.1 Hz--10 Hz. A new scheme of wavefront sensing with a coupled cavity was proposed to measure the pendulum rotation as low as $5\times10^{-16}\,{{\rm rad}/\sqrt{\rm Hz}}$ for Phase-III TOBA. In our method, an auxiliary cavity is used to enhance the first-order Hermite--Gaussian mode in a main cavity. Experimental demonstration is ongoing to confirm the feasibility of angular signal amplification and establish a method for locking a coupled cavity. We evaluated the performance of the coupled cavity and concluded that angular signal amplification would be feasible with this sensor. The coupled cavity was successfully locked to the resonance by the Pound--Drever--Hall technique with two modulation frequencies.

Dynamic response of a coal rock caving impact tail beam for hydraulic support

Based on the two-way coupling technology of Discrete Element Method-Multi Flexible Body Dynamics (EDM-FMBD), a virtual caving coal wall is established by using the discrete element software, EDEM. The rigid flexible coupling model of the tail beam of caving supports is established by using multibody dynamics software, RecurDyn. The stiffness of the oil cylinder is calculated by using the solid–liquid spring coupling theory and is replaced by a spring. By simulating the process of a coal rock collapse impacting the tail beam, the dynamic signal from the coal rock collapse impacting the tail beam to crushing in the coal caving stage of the comprehensive caving working face is studied, and the test is carried out underground. The angular acceleration at the hinge point of the tail beam is the largest and shows a variation pattern of "large at both ends and small in the middle". The definition of a "low amplitude band" on the surface of the tail beam is proposed. The force signal at the hinge point of the front link is the strongest and is the best measurement point for the force sensor; the angular acceleration signal at the hinge point of the tail beam is the strongest and it is the best measurement point for the angular acceleration sensor. The results have practical implications for the identification of the coal gangue and the adaptive control of support for integrated top coal mining.

Research on Degradation State Recognition of Axial Piston Pump under Variable Rotating Speed

Under the condition of variable rotating speed, it is difficult to extract the degradation characteristics of the axial piston pump, which also reduces the accuracy of degradation recognition. To address these problems, this paper proposes a degradation state recognition method for axial piston pumps by combining spline-kernelled chirplet transform (SCT), adaptive chirp mode pursuit (ACMP), and extreme gradient boosting (XGBoost). Firstly, SCT and ACMP are proposed to deal with the vibration signal instability and high noise of the axial piston pump under variable rotating speed. The instantaneous frequency (IF) of the axial piston pump can be extracted effectively by obtaining the accurate time-frequency distribution of signal components. Then, stable angular domain vibration signals are obtained by re-sampling, and multi-dimensional degradation characteristics are extracted from the angular domain and order spectrum. Finally, XGBoost is used to classify the selected characteristics to recognize the degradation state. In this paper, the vibration signals in four different degradation states are collected and analyzed through the wear test of the valve plate of the axial piston pump. Compared with different pattern recognition algorithms, it is verified that this method can ensure high recognition accuracy.

Adaptive high-resolution order spectrum for complex signal analysis of rotating machinery: Principle and applications

Order spectrum analysis is commonly used in rotating machinery vibration signal analysis under nonstationary conditions, because of its capability in detecting the rotating frequency and harmonics. Nevertheless, the conventional order spectrum suffers from some inherent drawbacks. For example, its application is limited by the inconvenience of rotary encoder or tachometer installation on running machines. Meanwhile, it is subjected to disturbances by speed-irrelative components, such as resonances and background noise, and angular resampling errors as well. More importantly, it is obtained by Fourier transforming the angular resampled signal, but the amplitude envelope after angular resampling is still nonstationary under time-varying conditions, leading to spectral blur of frequency orders and low resolution. To address the above issues, we propose an adaptive high-resolution order spectrum method, by exploiting the capability of multiple order probabilities approach (MOPA) in estimating rotating speed and that of adaptive iterative generalized demodulation (AIGD) in identifying true signal components of interest respectively. Firstly, the MOPA is utilized to extract the rotating speed from vibration signals, instead of using rotary encoders or tachometers. Then, the AIGD is adopted to identify true speed-related mono-components adaptively, thus eliminating interferences by speed-irrelative components. Finally, the order spectrum is constructed based on the average amplitude envelope and frequency order of each true component, instead of through angular resampling and Fourier transform. By doing so, the proposed order spectrum can adaptively identify rotating frequency orders in high resolution. The adaptability lies in that it does not need rotary encoders or tachometers, but adaptively estimates speed and identify rotating frequency harmonics, and therefore is more convenient in applications. The high resolution lies in that the energy highly concentrates at true frequency order, because it does not involve Fourier transform and angular resampling, thus being free of spectral blur, interferences by speed-irrelative components and resampling errors. The proposed method is illustrated and validated by analyses of numerical simulation and typical rotating machinery signals, including lab experiment and in-situ measurement of planetary gearboxes, and on-site measurement of a hydraulic turbine.

A Novel Method for Detecting the Two-Degrees-of-Freedom Angular Displacement of a Spherical Pair, Based on a Capacitive Sensor

The spherical pair has an important role in the inner frame of the stabilization mechanism of the aviation optoelectronic pod. However, its two-degrees-of-freedom (2-DOF) angular displacement signal is difficult to detect, seriously restricting its application in aviation optoelectronic pods. Therefore, this study proposes a new method to measure a spherical pair’s 2-DOF angular displacement using a spherical capacitive sensor. The capacitive sensor presented by this method realizes the measurement of the 2-DOF angular displacement of the spherical pair by integrating the spherical electrode groups in the ball head and the ball socket of the spherical pair. First, based on the geometric structure of the spherical pair, the structure of the capacitive sensor is designed, and the mathematical model for the capacitive sensor is deduced. Then, the sensor’s output capacitance, in different directions, is simulated by Ansoft Maxwell software. Finally, an experiment device is built for the measurement experiments. The simulation analysis and experimental results show that the spherical capacitive sensor has an approximately linear output in different directions, and the measured output capacitance is as high as of the theoretical value. Compared with the existing sensors that measure the 2-DOF angular displacement signal of the ball pair, the sensor proposed in this study has an integrated structure, which can be integrated into the spherical pair. That makes it possible to apply the spherical pair to the inner frame of the aviation optoelectronic pod.

Coherent angular signal amplification using an optical cavity.

Precision angular sensing is an essential technology in physical experiments. Unlike length sensing with a laser beam, it has been thought that sensitivity to the angular motion cannot be enhanced with the help of an optical cavity. A method of angular signal amplification using an optical cavity, called the cavity-amplified angular sensor (CAAS), is proposed. By adjusting or compensating for the Gouy phase of the cavity, the electric field of the laser generated in proportion to the target rotation is coherently stacked in the proposed method. The advantage of this method over other angular sensors is its high sensitivity with the small sensing spot size. Three possible optical configurations are considered, of which two experimentally available ones are investigated. The angular signal amplification is demonstrated for both of them. Based on the theoretical calculation for a realistic model, the fundamental angular sensing noise level is expected to be as low as 10-15rad/Hz1/2, with a 1 mm laser beam size and 10 mW laser power.

Phase Demodulation of Rotor Torsional Vibration Measurement under Time-Varying Speed

When torsional vibration is measured with the zebra-tape method, the modulation signals can be contaminated by electrical noise, environmental noise, step noise, sensor nonlinearities, and so on. Although the amplitudes of these types of noise are very weak relative to the amplitude of the ideal pulse test signal, they may introduce large-scale random broad-spectrum noise to the signal phases. Such noise is difficult to remove using frequency domain, time-frequency-domain, or threshold sampling methods and seriously affects the accuracy of torsional vibration measurements. This paper presents a phase demodulation algorithm based on downsampling and local resampling (DSLR) to improve the accuracy of torsional vibration measurements. To verify the proposed DSLR algorithm, torsional vibration simulations and experiments are conducted under a time-varying rotation speed. The results show that the angular displacement signal obtained from the torsional vibration test using zebra tapes is the superposition of the dynamic and static angular displacements, with the latter generated because of the varying rotation speed with respect to time. The DSLR algorithm effectively reduces the phase noise and demodulates the pulse signal phases. The work in this paper provides a method for obtaining reference torsional vibration measurements of rotors based on the zebra-tape method under time-varying speeds.

Adaptive discrete-time NN controller design for automobile steer-by-wire systems with angular velocity observer

As one of steering systems, the steer-by-wire (SbW) system plays a significant role in the automobile. This paper investigates the study of the adaptive discrete-time neural network (NN) steering tracking control of the SbW system based on angular velocity observer. First, the continuous second-order SbW system is discretized by the Euler approximation approach. Before the control design, an adaptive discrete-time angular velocity observer is constructed to estimate the value of angular velocity signal. Then, an adaptive discrete-time control via a backstepping scheme is proposed to achieve the exact steering tracking performance, where the unknown nonlinear function, as well as the control coefficient, are incorporated into the lumped uncertainty and approximated by one NN. The main advantages of the proposed control scheme can be concluded that: (1) without using the exact prior knowledge of system uncertainty and angular position signal, the discrete-time tracking control is achieved and (2) the noncausal problem is overcome without transforming the original system into a predictor form. Finally, the Lyapunov stability theory shows that the tracking error converges to a small neighborhood of zero. Simulations and experiments illustrate the effectiveness and superiority of the proposed adaptive discrete-time NN control.

Civil Aircraft Fault Tolerant Attitude Tracking based on Extended State Observers and Nonlinear Dynamic Inversion

For the problem of sensor faults and actuator faults in aircraft attitude control, this paper proposes a fault tolerant control (FTC) scheme based on extended state observer (ESO) and nonlinear dynamic inversion (NDI). First, two ESOs are designed to estimate sensor faults and actuator faults respectively. Second, the angular rate signal is reconstructed according to the estimation of sensor faults. Third, in angular rate loop, NDI is designed based on reconstruction of angular rate signals and estimation of actuator faults. The FTC scheme proposed in this paper is testified through numerical simulations. The results show that it is feasible and has good fault tolerant ability.

로봇팔의 시간지연 제어기에서 각 가속도 추정에 대한 추종 성능 비교연구

This paper presents a comparative study of tracking performances between several methods of estimating acceleration signals for a time-delayed controller(TDC) of a robot manipulator. To implement the time-delayed controller completely, there are three requirements: 1) Fast sampling time, 2) constant inertia value, and 3) angular acceleration signals. This paper focuses on the acceleration estimation for TDC. Several methods for angular acceleration estimation are tested: 1) Finite difference method with low pass filtering, 2) Kalman filtering, 3) desired angular acceleration, 4) control input, 5) Jacobian relationship. We experimented five methods on one arm of a dual arm robot to follow a circular trajectory and their tracking performances are compared.

Neuromuscular Fatigue Affects Calf Muscle Activation Strategies, but Not Dynamic Postural Balance Control in Healthy Young Adults.

Neuromuscular fatigue could negatively affect postural balance, but its effects on dynamic postural regulation are still debated. This study aimed to investigate whether a fatigue protocol on calf muscle could affect muscle activation strategies and dynamic balance performance. Seventeen male adults (age 24.1 ± 4.6 years; height 183.9 ± 7.2 cm; weight 80.2 ± 7.2 kg) volunteered in the study. They performed a dynamic test on an instrumented platform, which provided anterior-posterior oscillations on the sagittal plane, before and after a localized fatigue protocol. High-density surface electromyographical (EMG) signals were recorded bilaterally from the soleus and the medial gastrocnemius muscles. The fatigue protocol, consisting of two quasi-isometric tiptoe standing exercise to failure with a fixed load, did not affect the global dynamic balance performance. Conversely, the frequency value corresponding to 95% of the total power spectrum density of the angular displacement signal increased after fatigue (from 1.03 ± 0.42 to 1.31 ± 0.42 Hz; p < 0.05). The EMG analysis showed a significant difference in the PRE/POST fatigue ratio of the root-mean-square (RMS) between the soleus and the gastrocnemius medialis muscles. No differences were detected for the coefficient of variation and the barycenter coordinates of the RMS EMG values between muscles and sides. The variations in the frequency content of the angular displacement and EMG activity across muscles may be related to an increase in the calf muscles stiffness after fatigue. The role of neuromechanical calf muscle properties seems to be relevant in maintaining the dynamic postural performance after a quasi-isometric fatigue protocol until failure.

Preferred orientation and its effects on intensity-correlation measurements.

Intensity-correlation measurements allow access to nanostructural information on a range of ordered and disordered materials beyond traditional pair-correlation methods. In real space, this information can be expressed in terms of a pair-angle distribution function (PADF) which encodes three- and four-body distances and angles. To date, correlation-based techniques have not been applied to the analysis of microstructural effects, such as preferred orientation, which are typically investigated by texture analysis. Preferred orientation is regarded as a potential source of error in intensity-correlation experiments and complicates interpretation of the results. Here, the theory of preferred orientation in intensity-correlation techniques is developed, connecting it to the established theory of texture analysis. The preferred-orientation effect is found to scale with the number of crystalline domains in the beam, surpassing the nanostructural signal when the number of domains becomes large. Experimental demonstrations are presented of the orientation-dominant and nanostructure-dominant cases using PADF analysis. The results show that even minor deviations from uniform orientation produce the strongest angular correlation signals when the number of crystalline domains in the beam is large.

Design of a high precision converter for multi-sensor information fusion

The converter is a measuring device and is used together with the displacement sensor. In view of the existing sensor transform device is susceptible to error and temperature drift effects acquisition accuracy is not high, we design a high precision transducer, multi-sensor information fusion for vehicle steering gear shaft angular displacement signal measurement, signal transformation and digital transmission. The converter has the characteristics of high precision, miniaturization and low cost. Multi-sensor information fusion high-precision converter adopts front-end signal amplifier circuit, following filter processing circuit and embedded software of microprocessor for online compensation to satisfy the requirements of high-precision transformation. The microcontroller is used as the main control chip to meet the requirements of 8-channel bipolar analog signal acquisition. Two 14-bit, 6-channel A/D chips are used to convert the bipolar analog signal in the range of ±10V, and the RS422 hardware interface circuit performs digital transmission according to the time sequence specified by the central programmer. The experimental results show that the conversion accuracy of the device can reach 0.06%, the digital signal transmission is stable, and it can be widely used in industrial production.

An Enhanced Multifeature Fusion Method for Rotating Component Fault Diagnosis in Different Working Conditions

Mechanical rotating components such as bearing and gear are widely used in various industrial occasions. Fault diagnosis of rotating component can guarantee operational reliability and reduce maintenance cost of system. Although many fault diagnosis methods have been developed based on artificial intelligence methods, most of them ignore the existing signal processing knowledge in rotating element fault diagnosis domain. Meanwhile, these methods just assume that working conditions such as load or rotating speed keep constant, which may cause low accuracy once operating condition changes. To address these problems, an enhanced intelligent fault diagnosis method for rotating component is proposed based on multifeature and convolutional neural network (CNN). The vibration signals are first transformed into angular domain by resampling technique. Then, the angular domain signals are converted to obtain corresponding envelope and squared envelope spectrum features, which are fused into red–green–blue color image form to enhance sample features and enlarge differences among various health states. Finally, a CNN is constructed to accomplish fault recognition. Experimental results show that the average diagnosis accuracies of planetary gearbox and rolling element bearing datasets can reach 96.7% and 95.65%, which demonstrate that the multifeature approach is effective in fault diagnosis of rotating components in scenarios with different rotating speeds.

DStripe: Slice artefact correction in diffusion MRI via constrained neural network.

MRI scanner and sequence imperfections and advances in reconstruction and imaging techniques to increase motion robustness can lead to inter-slice intensity variations in Echo Planar Imaging. Leveraging deep convolutional neural networks as universal image filters, we present a data-driven method for the correction of acquisition artefacts that manifest as inter-slice inconsistencies, regardless of their origin. This technique can be applied to motion- and dropout-artefacted data by embedding it in a reconstruction pipeline. The network is trained in the absence of ground-truth data on, and finally applied to, the reconstructed multi-shell high angular resolution diffusion imaging signal to produce a corrective slice intensity modulation field. This correction can be performed in either motion-corrected or scattered source-space. We focus on gaining control over the learned filter and the image data consistency via built-in spatial frequency and intensity constraints. The end product is a corrected image reconstructed from the original raw data, modulated by a multiplicative field that can be inspected and verified to match the expected features of the artefact. In-plane, the correction approximately preserves the contrast of the diffusion signal and throughout the image series, it reduces inter-slice inconsistencies within and across subjects without biasing the data. We apply our pipeline to enhance the super-resolution reconstruction of neonatal multi-shell high angular resolution data as acquired in the developing Human Connectome Project.