Showing papers in "IEEE Transactions on Instrumentation and Measurement in 2015"

Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression

Abstract: The detection, diagnostic, and prognostic of bearing degradation play a key role in increasing the reliability and safety of electrical machines, especially in key industrial sectors. This paper presents a new approach that combines the Hilbert-Huang transform (HHT), the support vector machine (SVM), and the support vector regression (SVR) for the monitoring of ball bearings. The proposed approach uses the HHT to extract new heath indicators from stationary/nonstationary vibration signals able to tack the degradation of the critical components of bearings. The degradation states are detected by a supervised classification technique called SVM, and the fault diagnostic is given by analyzing the extracted health indicators. The estimation of the remaining useful life is obtained by a one-step time-series prediction based on SVR. A set of experimental data collected from degraded bearings is used to validate the proposed approach. The experimental results show that the use of the HHT, the SVM, and the SVR is a suitable strategy to improve the detection, diagnostic, and prognostic of bearing degradation.

Domain Adaptation Extreme Learning Machines for Drift Compensation in E-Nose Systems

Abstract: This paper addresses an important issue known as sensor drift, which exhibits a nonlinear dynamic property in electronic nose (E-nose), from the viewpoint of machine learning. Traditional methods for drift compensation are laborious and costly owing to the frequent acquisition and labeling process for gas samples’ recalibration. Extreme learning machines (ELMs) have been confirmed to be efficient and effective learning techniques for pattern recognition and regression. However, ELMs primarily focus on the supervised, semisupervised, and unsupervised learning problems in single domain (i.e., source domain). To our best knowledge, ELM with cross-domain learning capability has never been studied. This paper proposes a unified framework called domain adaptation extreme learning machine (DAELM), which learns a robust classifier by leveraging a limited number of labeled data from target domain for drift compensation as well as gas recognition in E-nose systems, without losing the computational efficiency and learning ability of traditional ELM. In the unified framework, two algorithms called source DAELM (DAELM-S) and target DAELM (DAELM-T) are proposed in this paper. In order to perceive the differences among ELM, DAELM-S, and DAELM-T, two remarks are provided. Experiments on the popular sensor drift data with multiple batches collected using E-nose system clearly demonstrate that the proposed DAELM significantly outperforms existing drift-compensation methods without cumbersome measures, and also bring new perspectives for ELM.

Real-Time RFID Indoor Positioning System Based on Kalman-Filter Drift Removal and Heron-Bilateration Location Estimation

Abstract: This paper proposes Kalman-filter drift removal (DR) and Heron-bilateration location estimation (LE) to significantly reduce the received signal strength index (RSSI) drift, localization error, computational complexity, and deployment cost of conventional radio frequency identification (RFID) indoor positioning systems without any sacrifice of localization granularity and accuracy. By means of only one portable RFID reader as the targeted device and only one pair of active RFID tags as the border-deployed landmarks, this paper develops a real-time portable RFID indoor positioning device and cost-effective scalable RFID indoor positioning infrastructure, based on Kalman-filter DR, Heron-bilateration LE, and four novel preprocessing/postprocessing techniques. Experimental results reveal that the proposed Kalman-filter DR method is faster and better to converge the distance measurement (DM) error than conventional probability/statistics in terms of various relative distances under certain RSSI drift effect condition, and the proposed Heron-bilateration LE method is also faster and better to converge the LE error than conventional proximity pattern matching and trilateration in terms of three or more landmarks under certain DM error condition. On the other hand, a portable RFID indoor positioning device is smoothly implemented on an Android smartphone platform attached with a portable Bluetooth-based RFID reader.

Detection and Classification of Power Quality Disturbances Using Sparse Signal Decomposition on Hybrid Dictionaries

Abstract: Several methods have been proposed for detection and classification of power quality (PQ) disturbances using wavelet, Hilbert transform, Gabor transform, Gabor-Wigner transform, S transform, and Hilbert-Haung transform. This paper presents a new method for detection and classification of single and combined PQ disturbances using a sparse signal decomposition (SSD) on overcomplete hybrid dictionary (OHD) matrix. The method first decomposes a PQ signal into detail and approximation signals using the proposed SSD technique with an OHD matrix containing impulse and sinusoidal elementary waveforms. The output detail signal adequately captures morphological features of transients (impulsive and oscillatory) and waveform distortions (harmonics and notching). Whereas the approximation signal contains PQ features of fundamental, flicker, dc-offset, and short- and long-duration variations (sags, swells, and interruptions). Thus, the required PQ features are extracted from the detail and approximation signals. Then, a hierarchical decision-tree algorithm is used for classification of single and combined PQ disturbances. The proposed method is tested using both synthetic and microgrid simulated PQ disturbances. Results demonstrate the accuracy and robustness of the method in detection and classification of single and combined PQ disturbances under noiseless and noisy conditions. The method can be easily expanded for compressed sensing based PQ monitoring networks.

A Key Management Scheme for Secure Communications of Information Centric Advanced Metering Infrastructure in Smart Grid

Abstract: Advanced metering infrastructure (AMI), as the totality of systems and networks to measure, collect, store, analyze, and use energy usage data, is supposed to be the core component in smart grid. In AMI, there are numerous challenges among which cyber security is a major one that needs to be addressed with priority. The information centric networking (ICN) is a promising architecture for the future Internet that disseminates content based on named data instead of named hosts. The congestion control and self-security can enable more scalable, secure, collaborative, and pervasive networking, these make the ICN a potential network architecture for smart grid. This paper aims to apply the ICN approach on AMI system, which we termed as information centric AMI (ICN-AMI). To the best of our knowledge, this is the first attempt to distribute contents (or requests for contents) based on ICN in AMI system. Moreover, a simulation-based performance evaluation is employed to evaluate the effectiveness of the proposed ICN-AMI approach in traffic control for developing AMI system in smart grid. In addition, we proposed a novel key management scheme (KMS) for a large number of smart meters in this system to ensure confidentiality, integrality, and authentication. To validate the scheme, the security analysis, comparisons are done to demonstrate that the proposed information centric KMS (ICN-KMS) is possible and a promising solution for ICN-AMI system.

PALDi: Online Load Disaggregation via Particle Filtering

Abstract: Smart metering and fine-grained energy data are one of the major enablers for future smart grid and improved energy efficiency in smart homes. Using the information provided by smart meter power draw, valuable information can be extracted as disaggregated appliance power draws by non-intrusive load monitoring (NILM). NILM allows to identify appliances according to their power characteristics in the total power consumption of a household, measured by one sensor, the smart meter. In this paper, we present an NILM approach, where the appliance states are estimated by particle filtering (PF). PF is used for nonlinear and non-Gaussian disturbed problems and is suitable to estimate the appliance state. ON/OFF appliances, multistate appliances, or combinations of them are modeled by hidden Markov models, and their combinations result in a factorial hidden Markov model modeling the household power demand. We evaluate the PF-based NILM approach on synthetic and on real data from a well-known dataset to show that our approach achieves an accuracy of 90% on real household power draws.

Initial Alignment by Attitude Estimation for Strapdown Inertial Navigation Systems

Abstract: This paper derives a novel initial alignment method for the strapdown inertial navigation system (SINS), which transforms the attitude alignment into an attitude estimation problem. The process model of the proposed initial alignment method by attitude estimation is established by decomposition of the attitude matrix. The measurement model is constructed based on a generalized velocity integration formula that can integrate the inertial measurements over certain fixed time intervals. The contributions of the work presented here are twofold. First, the attitude estimation-based structure enables the proposed method to estimate the gyroscope biases other than the attitude quaternion, which is celebrated for the low-cost SINS. The second is the application of the proposed generalized velocity integration formula to attenuate the accumulated errors in vector observations caused by the traditional velocity integration formula. Experimental road tests are performed with a low-cost SINS, which validate the efficacy of the proposed method.

Heterogeneous Data Fusion Algorithm for Pedestrian Navigation via Foot-Mounted Inertial Measurement Unit and Complementary Filter

Abstract: This paper proposes a foot-mounted Zero Velocity Update (ZVU) aided Inertial Measurement Unit (IMU) filtering algorithm for pedestrian tracking in indoor environment The algorithm outputs are the foot kinematic parameters, which include foot orientation, position, velocity, acceleration, and gait phase The foot motion filtering algorithm incorporates methods for orientation estimation, gait detection, and position estimation A novel Complementary Filter (CF) is introduced to better pre-process the sensor data from a foot-mounted IMU containing tri-axial angular rate sensors, accelerometers, and magnetometers and to estimate the foot orientation without resorting to GPS data A gait detection is accomplished using a simple states detector that transitions between states based on acceleration and angular rate measurements Once foot orientation is computed, position estimates are obtained by using integrating acceleration and velocity data, which has been corrected at step stance phase for drift using an implemented ZVU algorithm, leading to a position accuracy improvementWe illustrate our findings experimentally by using of a commercial IMU during regular human walking trials in a typical public building Experiment results show that the positioning approach achieves approximately a position accuracy around 04% and improves the performance regarding recent works of literature

An Integrated Probabilistic Approach to Lithium-Ion Battery Remaining Useful Life Estimation

Abstract: Estimating lithium-ion battery remaining useful life (RUL) is a key issue in an intelligent battery management system. This paper presents an integrated prognostic approach that unifies two types of health indices (HIs), battery capacity and time interval of equal discharging voltage difference series, to perform direct and indirect RUL estimation for lithium-ion battery. To satisfy different practical requirements, a data-driven monotonic echo state networks (MONESNs) algorithm is adopted to track the nonlinear patterns of battery degradation. The main contributions of this paper are: 1) to enhance the predictive capability of each HI and identify its failure threshold by implementing an HI correlation model and cycle life threshold transformation and 2) to increase the computational stability of the proposed approach through the ensemble of MONESN submodels that can also describe the prognostic uncertainty. Essentially, this approach constitutes a probabilistic integration and data-driven prognostic framework with uncertainty management capability. Two sets of industrial lithium-ion battery data are used to show the capability of the proposed approach. It is expected that this approach can be broadly applied to other application areas, where data-driven prognostic approaches are needed.

Remaining Useful Life Prediction of Rolling Bearings Using an Enhanced Particle Filter

Abstract: This paper presents an enhanced particle filter (PF) approach for predicting remaining useful life (RUL) of rolling bearings. In the presented approach, particles in each recursive step are used to determine an alterable importance density function and the backpropagation neutral network is utilized to improve the particle diversity before resampling. Based on the enhanced PF, the framework of online rolling bearing RUL prediction is designed and a multiorder autoregressive model is used to construct the dynamic model for PF. Case studies performed on a simulation example and two test-to-failure experiments indicate that the presented approach can accurately predict the RUL of rolling bearings and it can achieve better performance than the traditional PF-based approach and commonly used support vector regression approach.

Compressed Sensing: A Simple Deterministic Measurement Matrix and a Fast Recovery Algorithm

Abstract: Compressed sensing (CS) is a technique that is suitable for compressing and recovering signals having sparse representations in certain bases. CS has been widely used to optimize the measurement process of bandwidth and power constrained systems like wireless body sensor network. The central issues with CS are the construction of measurement matrix and the development of recovery algorithm. In this paper, we propose a simple deterministic measurement matrix that facilitates the hardware implementation. To control the sparsity level of the signals, we apply a thresholding approach in the discrete cosine transform domain. We propose a fast and simple recovery algorithm that performs the proposed thresholding approach. We validate the proposed method by compressing and recovering electrocardiogram and electromyogram signals. We implement the proposed measurement matrix in a MSP-EXP430G2 LaunchPad development board. The simulation and experimental results show that the proposed measurement matrix has a better performance in terms of reconstruction quality compared with random matrices. Depending on the compression ratio, it improves the signal-to-noise ratio of the reconstructed signals from 6 to 20 dB. The obtained results also confirm that the proposed recovery algorithm is, respectively, 23 and 12 times faster than the orthogonal matching pursuit (OMP) and stagewise OMP algorithms.

Image Reconstruction for Electrical Capacitance Tomography Based on Sparse Representation

Abstract: Image reconstruction for electrical capacitance tomography (ECT) is a nonlinear problem A generalized inverse operator is usually ill-posed (unbounded) and ill-conditioned (with a large norm) Therefore, the solutions for ECT are not unique and highly sensitive to the measurement noise To improve the image quality, a new image reconstruction algorithm for ECT based on sparse representation is proposed An unconventional basis, ie, an extended sensitivity matrix consisting of some normalized capacitance vectors corresponding to the base permittivity elements is designed as an expansion frame The permittivity distributions to be reconstructed can have a natural sparse representation based on the new basis and can be represented as a linear combination of the base elements Another sparsity regularization method-the standard Landweber iteration with a threshold is also conducted for comparison The proposed algorithm has been evaluated by both simulation (with and without noise) and experimental results for different permittivity distributions

Sentinella: Smart Monitoring of Photovoltaic Systems at Panel Level

Abstract: The monitoring of photovoltaic (PV) systems is important for the optimization of their efficiency. In this paper, a low-cost smart multisensor architecture equipped with voltage, current, irradiance, temperature, and inertial sensors, for the monitoring (at the panel level) of a PV system, is presented with the aim of detecting the causes of efficiency losses. The system is based on a Wireless Sensor Networks with sensing nodes installed on each PV panel. The acquired data are then transferred to a service center where dedicated paradigms continuously perform the assessment of electrical efficiency as well the estimation of correlated causes, at the single panel level. In this paper, the detection of critical faults (temporary and permanent shadowing, dirtying, and anomalous aging) is addressed. The methodology adopted to estimate efficiency losses and related causes is based on the comparison between the measured efficiency of each PV panel and the nominal one estimated in the real operating conditions. Moreover, the anomalous aging estimation is based on the five parameter model approach that exploits a dedicated minimization paradigm to analyze the mismatch between the nominal current–voltage model of the PV panel and the measured one. The main advantage of the proposed approach is the continuous monitoring of PV plants and the assessment of possible causes of power inefficiency at the PV panel level, allowing for the implementation of a really efficient distributed fault diagnosis system. The experimental results are presented along with the analysis of the uncertainty affecting the measurement system.

Compressive Sensing of a Taylor-Fourier Multifrequency Model for Synchrophasor Estimation

Abstract: Synchrophasor measurements, performed by phasor measurement units (PMUs), are becoming increasingly important for power system network monitoring. Synchrophasor standards define test signals for verification of PMU compliance, and set acceptance limits in each test condition for two performance classes ( $P$ and $M$ ). Several PMU algorithms have been proposed to deal with steady-state and dynamic operating conditions identified by the standard. Research and discussion arising from design, implementation, testing and characterization of PMUs evidenced that some disturbances, such as interharmonic interfering signals, can seriously degrade synchrophasor measurement accuracy. In this paper, a new compressive sensing (CS) approach is introduced and applied to synchrophasor measurements using a CS Taylor–Fourier (TF) multifrequency (CSTFM) model. The aim is to exploit, in a joint method, the properties of CS and the TF transform to identify the most relevant components of the signal, even under dynamic conditions, and to model them in the estimation procedure, thus limiting the impact of harmonic and interhamonic interferences. The CSTFM approach is verified using composite tests derived from the test conditions of the synchrophasor standard and simulation results are presented to show its potentialities.

Measuring GNSS Multipath Distributions in Urban Canyon Environments

Abstract: In general, standalone global navigation satellite systems (GNSS) receiver architectures cannot provide a position accuracy suitable for use in vehicular applications in urban canyon scenarios. Specifically, GNSS signals are affected by the surrounding objects, such as high buildings, trees, and so on, which introduces multipath errors. Multipath arises from the reception of reflected or diffracted signals, possibly in addition to the line-of-sight signal, and is one of the most detrimental error sources in GNSS positioning applications. Multipath distributions in the urban canyon area are measured and characterized in this paper. In particular, the Doppler and code phase delay under different conditions are assessed as a function of vehicle speed and signal power, which are different from previous calibration metrics. Specifically, multipath directional-dependence phenomenon (i.e., the variation resulting from the direction of travel of the user) is observed during this process, and the multipath maximum Doppler offset and minimum Doppler offset are derived and verified by the real data. The multipath distribution will eventually affect the search strategy (i.e., search space size, coherent integration time) utilized in the high sensitivity receiver.

Adaptive Multiscale Noise Tuning Stochastic Resonance for Health Diagnosis of Rolling Element Bearings

Abstract: The analysis of vibration or acoustic signals is most widely used in the health diagnosis of rolling element bearings. One of the main challenges for vibration or acoustic bearing diagnosis is that the weak signature of incipient defects is generally swamped by severe surrounding noise in the acquired signals. This problem can be solved by the stochastic resonance (SR) approach, which is to enhance the desired signal by the aid of noise. This paper presents an adaptive multiscale noise tuning SR (AMSTSR) for effective and efficient fault identification of rolling element bearings. A new criterion, called weighted power spectrum kurtosis (WPSK), is proposed as the optimization index without prior knowledge of the bearing fault condition. The WPSK concerns both the kurtosis in signal power spectrum and the similarity to a sinusoidal signal in signal waveform, thus it can balance the enhancement of possible characteristic frequency in the frequency domain and the regularity of the signal in the time domain for the SR performance. Two parameters in the AMSTSR, including the cutoff wavelet decomposition level and the tuning parameter, are simultaneously optimized based on the WPSK index through the artificial fish swarm algorithm. The AMSTSR is further applied to the health diagnosis of rolling element bearings and four experimental case studies verify the effectiveness of the proposed method in adaptive identification of the bearing characteristic frequencies.

Noncontact Thickness Measurement of Metal Films Using Eddy-Current Sensors Immune to Distance Variation

Abstract: A simple method for measuring the thickness of metal films based on eddy-current sensors (ECSs) immune to distance variation is proposed. The slope of the lift-off curve (LOC) in the RL impedance plane is a good feature for characterizing target thickness independent of lift-off distance variation. A simple equivalent model was built to deal with the ECS problem, and the essential relationship between the slope of LOC (SLOC) and target properties was obtained. Full finite element analysis was conducted to analyze the relationship between SLOC feature and target thickness, and the results matched the modeling results very well. A sensor coil probe was then manufactured and used to measure the thickness of copper films with high performance, and the capability of this technique for online noncontact thickness measurement was verified. The basic characteristics and performances of this thickness measurement technique were tested and discussed. The SLOC feature for thickness measurement had significant advantages, such as simplicity, reliability, immunity to the lift-off effect (most important), high speed, simple signal processing, and negligible design limitation of the sensor probe. The results of this paper revealed that online thickness measurement systems could be developed for various advanced industrial applications.

Fiber-Optic SPR Sensor for Temperature Measurement

Abstract: A fiber-optic surface plasmon resonance (SPR) sensor for temperature detection has been proposed by utilizing a thermosensitive liquid as the intermediate and combining with the fiber SPR structure. The sensing element of the sensor has been fabricated by packaging the fiber probe coated with a silver layer into a capillary filled with thermosensitive anhydrous ethanol. This packaging can protect the metal layer from oxidation and damage. Moreover, this proposed sensor achieves a sensitivity of 1.5745 nm/°C, which is much higher than that of the traditional fiber SPR sensor according to the comparative experiments.

Empirical Mode Decomposition Analysis for Broken-Bar Detection on Squirrel Cage Induction Motors

Abstract: Induction motor is a ubiquitous machine. In industrial settings, online monitoring of motors’ health status in order to schedule maintenance operations with the goal of damage prevention has become an essential necessity. Broken rotor bar is one of the most common failures in the rotor of a squirrel cage motor. Motor current signature analysis (MCSA) has become a popular method for the detection of this failure because of its high reliability. Recent works have performed the MCSA with a combination of different signal processing techniques to identify the presence of broken bars. In this paper, the MCSA is done with empirical mode decomposition from which a set of intrinsic mode functions (IMFs) is obtained. The extracted features from two of the obtained IMFs form the basis of the proposed classification criterion; these are the samples between zero crossings (SBZCs) and the time between successive zero crossings (TSZCs). The standard deviation from the SBZCs and the TSZCs is used as a classification feature. Experimental results using our method show high accuracy in the detection of a broken and a half-broken rotor bar.

Multiarea Distribution System State Estimation

Abstract: This paper presents a new approach to the distribution system state estimation in wide-area networks. The main goal of this paper is to present a two-step procedure designed to accurately estimate the status of a large-scale distribution network, relying on a distributed measurement system in a multiarea framework. First of all, the network is divided into subareas, according to geographical and/or topological constraints and depending on the available measurement system. Then, in the first step of the estimation process, for each area, a dedicated estimator is used, exploiting all the measurement devices available on the field. In the second step, data provided by local estimators are further processed to refine the knowledge on the operating conditions of the network. To improve the accuracy of the estimation results, correlation arising in the first step estimations has to be suitably evaluated and considered during the second step. Performed analysis shows that existing correlations can be included in the estimation process with very low data exchange among areas, thus involving minimum communication costs. Both first and second steps can be performed in a decentralized way and with parallel processing, thus leading to reduced overall execution times. Test results, obtained on the 123-bus IEEE test network and proving the goodness of the proposed method, are presented and discussed.

Wireless Wearable T-Shirt for Posture Monitoring During Rehabilitation Exercises

Abstract: The monitoring of any human physiological parameters during rehabilitation exercises requires noninvasive sensors for the patient. This paper describes a wireless wearable T-shirt for posture monitoring during rehabilitation or reinforcement exercises. The subject posture is measured through a sensorized T-shirt using an inductive sensor sewn directly on the fabric. The wireless wearable T-shirt design specifications are the following: independence from the remote unit, easy to use, lightweight and comfort of wearing. This paper reports the conceptual framework, the fabricated device description, and the adopted experimental setup. The instrumented T-shirt’s output data are compared with the data obtained via an optical system, as a gold standard, that measures the marker positions over the patient’s back and chest. The trials performed on four subjects obtained on different days demonstrate that the wireless wearable sensor described in this paper is capable of producing reliable data compared with the data obtained with the optical system. The constitutive sensor simplicity that includes only a copper wire and a separable circuit board allows achieving the objectives of simplicity, ease of use, and noninvasiveness. The sensorized T-shirt, integrated with designed conditioning and transmission electronics for remote communication, could be used as a support tool for postural monitoring during rehabilitation exercises.

An Indoor AC Magnetic Positioning System

Abstract: This paper describes the design and realization of a magnetic indoor positioning system. The system is entirely realized using off-the-shelf components and is based on inductive coupling between resonating coils. Both system-level architecture and realization details are described along with experimental results. The realized system exhibits a maximum positioning error of $3 \times 3~\mathrm{m}^{{{2^{^{^{}}}}}}$ area. Extensive experiments in larger areas, in nonline-of-sight conditions, and in unfavorable geometric configurations, show submeter accuracy, thus validating the robustness of the system with respect to other existing solutions.

An Efficient Frequency and Phase Estimation Algorithm With CRB Performance for FMCW Radar Applications

Abstract: In this paper, a chirp $z$ -transform (CZT)-based algorithm for frequency-modulated continuous wave (FMCW) radar applications is presented. The proposed algorithm is optimized for real-time implementation in field-programmable gate arrays. To achieve a very high accuracy, the FMCW radar uses an additional phase evaluation. Therefore, a phase calculation based on the CZT algorithm is derived and compared with a correlation based algorithm. For a better classification of the algorithm, the respective Cramer–Rao bounds are calculated. The performance of the algorithm is shown by the evaluation of different radar measurements with a K-band radar. In the measurements, an accuracy of $5~\mu $ m with a mean standard deviation of 774 nm is achieved, which nearly matches the theoretically predicted mean standard deviation of 160 nm.

Design and Characterization of a Portable Ultrasonic Indoor 3-D Positioning System

Abstract: In this paper, an ultrasonic positioning system is presented and characterized, based on the usage of a portable grid of beacons and of a few fixed anchors. Since the beacon grid can be moved to guarantee line-of-sight transmissions, the proposed strategy is potentially suitable for accurate positioning of a mobile object in an environment with a complex geometry. The system was tested experimentally, exhibiting a subcentimeter positioning accuracy in a range up to 4 m.

An Integrated Framework for 3-D Modeling, Object Detection, and Pose Estimation From Point-Clouds

Abstract: 3-D modeling, object detection, and pose estimation are three of the most challenging tasks in the area of 3-D computer vision. This paper presents a novel algorithm to perform these tasks simultaneously from unordered point-clouds. Given a set of input point-clouds in the presence of clutter and occlusion, an initial model is first constructed by performing pair-wise registration between any two point-clouds. The resulting model is then updated from the remaining point-clouds using a novel model growing technique. Once the final model is reconstructed, the instances of the object are detected and the poses of its instances in the scenes are estimated. This algorithm is automatic, model free, and does not rely on any prior information about the objects in the scene. The algorithm was comprehensively tested on the University of Western Australia data set. Experimental results show that our algorithm achieved accurate modeling, detection, and pose estimation performance.

Dynamic Phasor and Frequency Measurements by an Improved Taylor Weighted Least Squares Algorithm

Abstract: One of the most accurate phasor estimation procedures recently proposed in the literature is the so-called Taylor weighted least squares (TWLS) algorithm, which relies on a dynamic phasor model of an electrical waveform at nominal frequency. In this paper, an extension of the TWLS algorithm [called generalized TWLS (GTWLS) algorithm] to a generic (not only nominal) reference frequency is described and the accuracies of the returned estimates are analyzed through meaningful simulations, performed in different steady-state and dynamic testing conditions according to the IEEE Standard C37.118.1-2011 about synchrophasor measurement for power systems and its Amendment IEEE Standard C37.118.1a-2014. It is shown that the accuracy of the total vector error (TVE), frequency error (FE), and rate of change of frequency error (RFE) normally decreases as the deviation between the reference frequency and the true waveform frequency decreases. Furthermore, a two-step procedure for accurate estimation of the phasor parameters is proposed. In the first step, the waveform frequency is estimated by a classical interpolated discrete Fourier transform (IpDFT) algorithm. The second step then returns an estimate of the phasor parameters by applying the TWLS algorithm based on the frequency estimate returned by the first step. It is shown that the proposed procedure, called the GTWLS–IpDFT algorithm, can comply with the $P$ -class or the $M$ -class of performances in all the considered testing conditions when an appropriate number of waveform cycles is considered and the most significant disturbances are removed from the analyzed waveform. Finally, uncertainties of the proposed estimators and the $\mathrm{IpD}^{2}$ FT algorithm recently presented in the literature are also compared.

Fault Diagnosis of Rotating Electrical Machines in Transient Regime Using a Single Stator Current’s FFT

Abstract: The discrete wavelet transform (DWT) has attracted a rising interest in recent years to monitor the condition of rotating electrical machines in transient regime, because it can reveal the time–frequency behavior of the current’s components associated to fault conditions Nevertheless, the implementation of the wavelet transform (WT), especially on embedded or low-power devices, faces practical problems, such as the election of the mother wavelet, the tuning of its parameters, the coordination between the sampling frequency and the levels of the transform, and the construction of the bank of wavelet filters, with highly different bandwidths that constitute the core of the DWT In this paper, a diagnostic system using the harmonic WT is proposed, which can alleviate these practical problems because it is built using a single fast Fourier transform of one phase’s current The harmonic wavelet was conceived to perform musical analysis, hence its name, and it has spread into many fields, but, to the best of the authors’ knowledge, it has not been applied before to perform fault diagnosis of rotating electrical machines in transient regime using the stator current The simplicity and performance of the proposed approach are assessed by comparison with other types of WTs, and it has been validated with the experimental diagnosis of a 315-MW induction motor with broken bars

A Capacitive Rotary Encoder Based on Quadrature Modulation and Demodulation

Abstract: This paper presents a capacitive rotary encoder for both angular position and angular speed measurements. The encoder is mainly composed of three parts: the transmitting segments; a pair of reflecting electrodes; and a pair of receiving electrodes. The transmitting segments together with four mutual quadrature carrier voltages provide a modulated electric field. The reflecting electrodes, which are patterned sinusoidally can encode the angular position to a phase/frequency modulated signal based on quadrature modulation. The modulated signal is then digitally decoded to the angular position in a field programmable gate array processor based on the quadrature demodulation and the coordinate rotational digital computer algorithm. Through a universal serial bus, the digital angular position is transmitted to a computer for further analysis in National Instruments' LabVIEW software. A prototype of the capacitive encoder shows that its precision is better than 0.006° and the resolution is 0.002°. The dynamic nonlinearity is evaluated at ±0.4° when the rotor is rotating at 1000 r/min.

Wavelet Transform With Histogram-Based Threshold Estimation for Online Partial Discharge Signal Denoising

Abstract: Online condition assessment of the power system devices and apparatus is considered vital for robust operation, where partial discharge (PD) detection is employed as a diagnosis tool. PD measurements, however, are corrupted with different types of noises such as white noise, random noise, and discrete spectral interferences. Hence, the denoising of such corrupted PD signals remains a challenging problem in PD signal detection and classification. The challenge lies in removing these noises from the online PD signal measurements effectively, while retaining its discriminant features and characteristics. In this paper, wavelet-based denoising with a new histogram-based threshold function and selection rule is proposed. The proposed threshold estimation technique obtains two different threshold values for each wavelet sub-band and uses a prodigious thresholding function that conserves the original signal energy. Moreover, two signal-to-noise ratio (SNR) estimation techniques are derived to fit with actual PD signals corrupted with real noise. The proposed technique is applied on different acoustic and current measured PD signals to examine its performance under different noisy environments. The simulation results confirm the merits of the proposed denoising technique compared with other existing wavelet-based techniques by measuring four evaluation metrics: 1) SNR; 2) cross-correlation coefficient; 3) mean square error; and 4) reduction in noise level.

Dynamic Sign Language Recognition for Smart Home Interactive Application Using Stochastic Linear Formal Grammar

Abstract: This paper presents the state-of-the art dynamic sign language recognition (DSLR) system for smart home interactive applications Our novel DSLR system comprises two main subsystems: an image processing (IP) module and a stochastic linear formal grammar (SLFG) module Our IP module enables us to recognize the individual words of the sign language (ie, a single gesture) In this module, we used the bag-of-features (BOFs) and a local part model approach for bare hand dynamic gesture recognition from a video We used dense sampling to extract local 3-D multiscale whole-part features We adopted 3-D histograms of a gradient orientation descriptor to represent features The $k$ -means++ method was applied to cluster the visual words Dynamic hand gesture classification was conducted using the BOFs and nonlinear support vector machine methods We used a multiscale local part model to preserve temporal context The SLFG module analyzes the sentences of the sign language (ie, sequences of gestures) and determines whether or not they are syntactically valid Therefore, the DSLR system is not only able to rule out ungrammatical sentences, but it can also make predictions about missing gestures, which, in turn, increases the accuracy of our recognition task Our IP module alone seals the accuracy of 97% and outperforms any existing bare hand dynamic gesture recognition system However, by exploiting syntactic pattern recognition, the SLFG module raises this accuracy by 165% This makes the aggregate performance of the DSLR system as accurate as 9865%