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In recent years, there has been an increasing interest in hand movements in various application fields. However, the human hand is too dexterous and only has limited space. Developing a proper method to monitor and capture hand motion is meaningful. Conventionally, an optical measurement method is widely used to capture hand gestures. Nevertheless, this method is costly and has the problem of line-of-sight shielding. In this paper, a sensor fusion algorithm is proposed to calculate a more accurate result by calculating acceleration, magnetic field strength, and angular speed from the inertial measurement unit (IMU). The core concept of this algorithm is based on the idea of feedback control. To validate the performance, the active rotary platform is set up to compare the measured results to the references directly. Most of the error results are less than 3°, and the standard deviations are less than 1°. The measurement results ensure the feasibility of the inertial measurement method with the sensor fusion algorithm proposed in this paper. [ABSTRACT FROM AUTHOR]

This paper proposes a mutual calibration strategy for multiple current sensors in an electric vehicle motor drive. The motor drive usually consists of three current sensors, i.e., a dc-bus current sensor and two phase current sensors. Due to the aging effect and harsh operating environment, the accuracy uncertainty issue is inevitable in these crucial sensors, which results in poor driving performance. In this paper, the detection voltage injection (DV-Injection) method is proposed for mutual calibration of the aforementioned current sensors. Two opposite basic vectors are set together to detect and eliminate the offset error of the dc-bus current sensor. Then, both the directly measured phase-current values by the phase-current sensors and the indirectly measured values by the dc-bus current sensor are sampled. These values are utilized for mutual calibration of the phase-current sensor offset errors and scaling error differences among all the current sensors. Meanwhile, the DV-Injection process is only applied in the period of calibration process, whereas in the remaining intervals the space vector pulsewidth modulation technology is utilized. Finally, the effectiveness of the proposed scheme is verified by simulation study in MATLAB/Simulink and experimental results on a 5-kW interior permanent magnet synchronous motor motor prototype. [ABSTRACT FROM AUTHOR]

Methane is the primary constituent of natural gas and is used in many industrial processes. Detection of the presence of methane is important, especially before it reaches explosive concentrations. Earlier sensor types are based on catalytic adsorption (which may limit the sensor lifetime) at elevated temperatures (requiring additional power and possibly compromising safety). Recently, optical sensors based on infrared (IR) absorption by hydrocarbon molecules have become an important research focus. One unsolved problem when using solid-state IR sources is that of optical flux variation due to heating. This paper introduces a novel power compensation approach for IR LEDs employed in gas detection, which accounts for variations in the optical flux of the source. The contribution of this paper is threefold. First, we propose the idea of compensating for emitted IR flux by means of pulsed junction voltage measurement and explain why this is effective. Second, we introduce a compensation algorithm which shows how to take advantage of this concept. Third, experimental results demonstrate that the discrimination algorithm is at least six times better than the uncompensated measurements. The convergence of the algorithm results in a stabilized measurement in less than 1 s. [ABSTRACT FROM PUBLISHER]

Vehicular ad hoc networks (VANETs) help improve traffic safety and lessen traffic congestion. Roadside units (RSUs) play a key role in serving as the event and data broker in the form of vehicle-to-infrastructure communication to supply wireless and mobile vehicle-to-vehicle communication. Recently, hybrid VANET-sensor networks have attracted much attention as events are detected by sensor nodes and are spread to a wider area via VANETs, in which the number of RSUs is restricted due to high cost. This paper investigates the problem of minimizing the total cost of deploying RSUs and sensor nodes along the two sides and the median island of a two-lane road to cover the whole road, represented as a grid, and to form a connected VANET-sensor network. This problem is NP-complete by reduction to the NP-complete placement problem for a single-lane road. Therefore, this paper formulates the problem as an integer linear program and then proposes a center particle swarm optimization approach, in which a center particle is adopted for increasing the convergence speed. Additionally, a theoretical analysis for the approach is provided. Experimental results show that the approach can perform well for moderate-sized problems. [ABSTRACT FROM PUBLISHER]

Modern control systems rely heavily on their sensors for reliable operation. Failure of a sensor could destabilize the system, which could have serious consequences to the system's operations. Therefore, there is a need to detect and accommodate such failures, particularly if the system in question is of a safety critical application. In this paper, a sensor failure detection, identification, and accommodation (SFDIA) scheme is presented. This scheme is based on the fully connected cascade (FCC) neural network (NN) architecture. The NN is trained using the neuron by neuron learning algorithm. This NN architecture is chosen because of its efficiency in terms of the number of neurons and the number of inputs required to solve a problem. The SFDIA scheme considers failures in pitch, roll, and yaw rate gyro sensors of an aircraft. A total of 105 experiments were conducted; out of which, only one went undetected. The SFDIA scheme presented here is efficient, compact, and computationally less expensive, in comparison to schemes using, for example, the popular multilayer perceptron NN. These benefits are inherited from the FCC NN architecture. [ABSTRACT FROM PUBLISHER]

This paper proposes a method to improve the accuracy of an absolute magnetic encoder by using harmonic rejection (HR) and a dual-phase-locked loop (DPLL). The encoder consists of two permanent magnets: an edge-located multipolar magnet (MPM) and a center-located bipolar magnet, in which the signal-processing accuracy of the MPM is crucial for achieving high accuracy of the entire encoder. However, the MPM signals are disturbed by nonidealities such as dc offsets, amplitude mismatch, low- and high-order harmonics, and random noises. In this paper, the HR approach investigates the characteristics of nonidealities of the phase detector and rejects them by using gradient descent. In addition, the DPLL remains robust by maintaining a zero steady-state error during a phase jump, a constant frequency, and a ramp frequency. The proposed method is simulated with MATLAB software and implemented in ARM STM32F407ZG. The obtained results demonstrate efficient performance. This method can be applied to any use of quadrature sinusoidal signals, such as in power grids and in permanent-magnet synchronous motor phase detection. [ABSTRACT FROM AUTHOR]

In this paper, a design method for the yarn tension sensor based on a surface acoustic wave is presented. When the yarn tension is applied to the sensor's doubly clamped beam (i.e., piezoelectric substrate), it is linearly related to the output frequency shift, which achieves the purpose of measuring the yarn tension. This paper deduces the functional relationship between the oscillation frequency shift of the sensor and the yarn tension. We put forward the choice of a piezoelectric substrate material, the choice of the piezoelectric die size, and the placement of interdigital transducers (IDTs) as the three key issues to be determined. By using the ANSYS software, we determined that the optimal piezoelectric substrate is quartz, and determined the optimal size of the die and the maximum strain area of the die. When IDTs are placed in the maximum strain area of the piezoelectric substrate, the sensor has the best sensitivity. We have designed and produced the sensor and also have tested the sensor. Experimental results: the range is 0–2 N, the linearity is 1.0112%, the hysteresis is 0.6153%, the repeatability is 1.1067%, the accuracy is 1.6205%, the maximum overload capacity is 200% (i.e., 4 N), and the fracture strength of the substrate is 5 N. [ABSTRACT FROM AUTHOR]

In this paper, a cascaded H-Bridge (CHB)-multilevel converter (MC)-based photovoltaic (PV) system with no voltage or current sensors at the dc-side is proposed. Eliminating the dc-side sensors simplifies the hardware, leading to lower cost and higher reliability of the PV system. A novel scheme estimating the capacitors’ voltages from the ac output voltage of the inverter is developed. The scheme allows replacing all dc-side voltage sensors by a single-voltage sensor at the ac-side of the converter. Furthermore, the dc current sensors, conventionally required for the maximum power point tracking (MPPT), are also eliminated. Instead, the outputs from the capacitors’ voltage control systems are utilized for the MPPT. The effectiveness of the proposed dc-side sensorless system is experimentally demonstrated on a 2-kW single-phase seven-level CHB converter-based PV system. [ABSTRACT FROM PUBLISHER]

Conventional back-electromotive force (EMF) and flux linkage-based indirect rotor position detection methods have limitations, specifically poor position accuracy and inability to operate at low as well as at high speed. However, practical motor drives require precise operation over a wide speed range, which widens the application areas while increasing reliability compared to direct position sensor-based drives. The method proposed and demonstrated in this paper uses only terminal voltage measurements to estimate the flux linkage increment in six separate sectors of an electrical cycle. This is used to determine the rotor position while avoiding the accumulated position error that is a characteristic of flux linkage-based techniques. This paper also addresses the common problems associated with the practical implementations, such as low-pass filter delay, integrator offset error, and noise sensitivity at low speed. The theoretical explanation of the method has been given in detail and analytical solutions for the flux linkage threshold of both sinusoidal and trapezoidal permanent-magnet (PM) machines is provided, avoiding trial and error tuning commonly utilized in the literature. This paper provides a range of experimental data to verify both the operational robustness and position accuracy of the method. [ABSTRACT FROM PUBLISHER]

Large portions of a winder's control system software are tested during commissioning. This is inefficient because production is stopped and financial pressures limit the time available for testing. However, it avoids the cost of integrating the winder and electrical drive system before installation. This paper describes a winder simulator that allowed control system software to be tested before installation. Shipping roll build up, parent roll build down, mechanical brakes, and a two-drum windup model were developed. These models were used to simulate the winder, web, and drive system dynamics in real time with a dedicated programmable logic controller (PLC). This PLC provided signals for the winder PLC that would normally be supplied by the drive system and various sensors. The simulator was verified with data from a previously installed winder. The use of the simulator before installation of a new winder demonstrated the benefits of pre-installation software testing. [ABSTRACT FROM AUTHOR]

Proper monitoring of quality-related variables in industrial processes is nowadays one of the main worldwide challenges with significant safety and efficiency implications.Variational Bayesian mixture of canonical correlation analysis (VBMCCA)-based process monitoring method was proposed in this paper to predict and diagnose these hard-to-measure quality-related variables simultaneously. Use of Student's t-distribution, rather than Gaussian distribution, in the VBMCCA model makes the proposed process monitoring scheme insensitive to disturbances, measurement noises, and model discrepancies. A sequential perturbation (SP) method together with derived parameter distribution of VBMCCA is employed to approach the uncertainty levels, which is able to provide a confidence interval around the predicted values and give additional control line, rather than just a certain absolute control limit, for process monitoring. The proposed process monitoring framework has been validated in a wastewater treatment plant (WWTP) simulated by benchmark simulation model with abrupt changes imposing on a sensor and a real WWTP with filamentous sludge bulking. The results show that the proposed methodology is capable of detecting sensor faults and process faults with satisfactory accuracy. [ABSTRACT FROM PUBLISHER]

This paper presents an improved saliency-based position sensorless drive of an interior permanent-magnet synchronous machine (IPMSM) with a single current sensor, used for low cost applications. The phase current, reconstructed from a dc-link current, shows a reconstruction error resulted from the difference of the sampling point from that of a full current sensor drive. This reconstruction error results in degradation of the overall sensorless control performance. In this paper, we analyze the impact of the reconstruction error depending on the injection frequency for a high frequency voltage signal injection sensorless control. Based on this analysis, we propose an improved position sensorless drive that employs a new current reconstruction method. In order to minimize the reconstruction error, the proposed current reconstruction method predicts the dq-axis currents with the same sampling point as a full current sensor drive. Several experimental results are provided to verify the analysis of the reconstruction error and the proposed method's improved sensorless control performances. [ABSTRACT FROM PUBLISHER]

Underground localization is considered a key technology for controlling underground equipment, such as directional drilling equipment. However, most sensors for localization such as vision sensors, range sensors, and wireless sensors cannot be utilized in an underground environment. Moreover, vibration by the drill bit and distortion of the geomagnetic field by the surrounding material adversely affect localization performance. To accurately estimate the pose for directional drilling, a resilient underground localization method using concurrent normalized cross correlation (CNCC) and magnetic field vector is proposed in this paper. In the underground environment, a different magnetic field is distributed at each region. Since the drilling system moves only inside the borehole, the magnetic field distribution can be re-measured. Therefore, the constraint between the poses is detected by comparing the magnetic field distribution. A comparison method based on CNCC is proposed to resolve the ambiguity of the magnetic field. Moreover, to reduce the orientation error, magnetic field vector matching is used. The constraint can correct the pose estimation of the system using pose graph optimization. To validate the performance of the proposed method, two types of field tests are carried out in this paper. [ABSTRACT FROM PUBLISHER]

The development of advanced techniques for process monitoring and fault diagnosis using both model-based and data-driven approaches has led to many practical applications. One issue that has not been considered in such applications is the ability to deal with key performance indicators (KPIs) that are only sporadically measured and with significant time delay. Therefore, in this paper, the data-driven design of diagnostic-observer-based process monitoring schemes is extended to include the ability to detect changes given infrequently measured KPIs. The extended diagnostic observer is shown to be stable and hence able to converge to the true value. The proposed method is tested using both Monte Carlo simulations and the Tennessee-Eastman problem. It is shown that although time delay and sampling time increase the detection delay, the overall effect can be mitigated by using a soft sensor. Furthermore, it is shown that the results are not strongly dependent on the sampling time, but do depend on the time delay. Therefore, the proposed soft-sensor-based monitoring scheme can efficiently detect faults even in the absence of direct process information. [ABSTRACT FROM PUBLISHER]

Modular multilevel converter (MMC) has been one of the most popular candidates in high-voltage applications. However, reliability is a critical issue due to a large number of power switching devices and capacitors applied in the MMC. To improve the reliability of the MMC, a fast and reliable diagnosis strategy for the open-circuit fault is proposed in this paper, where submodule voltage sensors are relocated to the upper switching device. Furthermore, a voltage observer based on the submodule voltage sensor is established to monitor the capacitor and realize the power control of the MMC under normal operation. A Boolean logic operation based fault indicator is put forward based on the relationship of the operation state and binaried output of voltage sensors, which could detect and locate the open-circuit faults of the MMC very fast. The ratio of the increment of the observed and measured capacitor voltage during the period of positive arm current is applied to monitor the capacitor. The effectiveness of the proposed fault diagnosis strategy and the capacitor monitoring strategy is verified by the experiment results. [ABSTRACT FROM AUTHOR]

To optimize control and/or state estimation of industrial processes, information about measurement quality provided by sensors is required. In this paper, a probabilistic scheme is proposed in discrete-time nonlinear state space with the purpose of sensor monitoring. A quantitative index representing the measurement quality, as well as satisfied state estimates, is obtained by estimating the probability density functions (PDFs) of the states and the measurement noise covariance considered as a random variable using the variational Bayesian approach. To solve the intractable integrals of nonlinear PDFs in real time, a set of weighted particles is generated to overlap an empirical density of state, while the PDF of the measurement noise is still derived analytically. An example of localization and an experiment with a rotary flexible joint are supplied to demonstrate that the proposed algorithm significantly improves the applicability of existing methods and can monitor correlated sensors satisfactorily. [ABSTRACT FROM AUTHOR]

In this paper, a threshold signal detector is proposed to improve the state estimation accuracy of an extended Kalman filter (EKF) and is validated experimentally with a microelectromechanical system electrostatic microscanner. A first-order derivative of Gaussian filter is used to detect and locate rapid changes in voltage signal caused by crossing of a threshold angle determined by maximum overlap of capacitive electrodes. The event-triggered measurement is used in the update step of the EKF to provide intermittent but more accurate angle measurements than those of the capacitive sensor's continuous output. Experiments on the electrostatic microscanner show that with the threshold signal detector incorporated; the average position estimation accuracy of the EKF is improved by 15.1% with largest improvement (30.3%) seen under low signal-to-noise ratio conditions. A parametric study is conducted to examine sampling frequency and capacitance profile, among other factors that may affect detection error and EKF accuracy. [ABSTRACT FROM AUTHOR]

A self-powered vibration sensor based on electromagnetic induction is presented in this paper. The vibration sensor is implemented with stacked flexible coils and polymeric springs. The stacked flexible coil enables large output signal, while long and flexible springs enable high sensitivity and wide linear range of the sensor. At resonance, the sensor has a sensitivity of 1.83 V/g and a maximum resolution of 0.002 g. In the high-frequency range, where the sensor output is frequency-independent (100–3000 Hz), the sensor has a sensitivity of 66.5 mV/g, a maximum resolution of 0.05 g, and a measurement range up to 8.5 g. [ABSTRACT FROM AUTHOR]

In this paper, a parallel network based on hand detection and body pose estimation is proposed to detect and distinguish human's right and left hands. The network is employed for human–robot interaction (HRI) based on hand gestures. This method fully uses hand feature information and hand information in the human body structure. One channel in the network uses a ResNet-Inception-Single Shot MultiBox Detector to extract hand feature information for human's hand detection. The other channel estimates human body pose first and then estimates the positions of the left and right hands using the forward kinematic tree of the human skeleton structure. Thereafter, the results of the two channels are fused. In the fusion module, the human body structure can be utilized to correct hand detection results and distinguish between the right and left hands. Experimental results verify that the parallel deep neural network can effectively improve the accuracy of hand detection and distinguish between the right and left hands effectively. This method is also used for the hand-gesture-based interaction between astronauts and an astronaut assistant robot. Our method can be suitably used in this HRI system. [ABSTRACT FROM AUTHOR]

Induction motors are widely used in industrial plants for critical operations. Stator faults, bearing faults, or rotor faults can lead to unplanned downtime with associated cost and safety implications. Different sensors may be used to monitor the health state of induction motors with each sensor typically being better suited for diagnosing different faults. Condition monitoring approaches that fuse data from multiple sensors have the potential to diagnose a greater number of faults. In this paper, a sensor fusion approach based on the combination of a two-stage Bayesian method and principal component analysis (PCA) is proposed for diagnosing both electrical and mechanical faults in induction motors. Acoustic, electric, and vibration signals are gathered from motors operating under different loading conditions and health states. The inclusion of the PCA step ensures robustness to varying loading conditions. The obtained results highlight that the proposed method performs better than the equivalent single-stage or feature-based Bayesian methods. [ABSTRACT FROM AUTHOR]

Three-phase pulsewidth modulation inverter fed induction motor drive system is widely applied in high power drive applications. Sensor faults are very common in the drive system, which, once occur, might result in degraded system performance or even system shutdown. In order to rapidly and accurately diagnose the sensor faults, this paper proposes an intelligent time-adaptive data-driven method to identify the fault location and fault type of sensors in the drive system. An emerging machine learning technology named extreme learning machine (ELM) is applied to learn the sensor fault dataset; an ensemble ELM classifier is then designed to improve diagnostic accuracy, based on which a time-adaptive fault diagnosis process is proposed to achieve a high and balanced diagnostic accuracy and speed. As a data-driven method, the proposed method only employs the phase current, dc-link voltage, and speed signals as the inputs to the ensemble ELM classifiers and requires no additional sensors and other hardware. Simulated and experimental tests show that the proposed method can rapidly and accurately detect the fault sensor location and identify offset fault, stuck fault, and noise faults with an average diagnostic accuracy of 98% and the average decision time of 10 ms after the fault occurs. Moreover, such diagnosis method is robust to the fluctuation of catenary voltage and dc-link voltage, fault severity, and variation of model parameters, speed, and load. [ABSTRACT FROM AUTHOR]

This "Special Section on New Trends in Smart Sensors for Industrial Applications" of the IEEE Transactions on Industrial Electronics, will be published in two issues of the journal. In this issue, the first 13 papers are presented. A brief summary of the papers is provided. [ABSTRACT FROM PUBLISHER]

The early alert monitoring system for an effective scheduled maintenance strategy based on a wireless technology requires reliable transfer of the diagnostic information between the sensor and the gateway. This paper presents an industrial wireless sensor network (IWSN)-based machine condition monitoring (MCM) system capable of overcoming a false indication caused by temporary loss of data, signal interference, or invalid data. We use multisensor fusion driven by a quality parameter, which is produced by each sensor node according to the data history outliers and the actual state of the node. The fusion node provides a quality evaluation on its output as well. This novel approach enables the propagation of information about the uncertainty of a measured value from the source node to the sink node. Thus, potential degradation of acquired or transferred diagnostic information is minimized. Instead of raw data, the signal features are transferred, so that bandwidth savings are rapidly improved. The proposed concept was experimentally verified on real wireless sensor network (WSN) hardware. The performance evaluated using the signal-to-noise ratio and false-alarm rate detection demonstrates the effectiveness of the proposed approach. [ABSTRACT FROM PUBLISHER]

One technique which may be employed in certain types of sensing application is the phase-sensitive or lock-in method. Since this method essentially trades off longer measurement time for a greater accuracy, it is sometimes necessary to capture samples over a long time period to ensure sufficient stability in the calculated parameter estimate. This may adversely affect safety-critical systems, and some method of determining the relative stability of the measured parameter is needed to permit this useful approach to be more widely employed. In this paper, we propose and evaluate a novel entropy-based method for ascertaining stability. The contributions of this paper are to first highlight the need for a convergence measure which is reliable, automatic, and easily computed; second, we propose one such measure, and place it on a theoretical footing; finally, we give results both with simulated Gaussian noise having a typical sensor power spectrum, as well as experimental results using the type of optical sensor which would benefit from the proposed method. It is demonstrated that this approach produces a reliable asymptotic figure for convergence, both under simulated noise conditions as well as with real measurements in the field. [ABSTRACT FROM PUBLISHER]

This paper proposes a self-sensing measurement technique to perform the precise estimation of the displacements along two axes in a two-degrees-of-freedom (2-DOF) piezoelectric actuator. For that, a new electrical circuit scheme that permits charge amplification is first proposed to match the different electrodes combination of the 2-DOF actuator. Then, a new bivariable observer that precisely estimates the displacements is calculated and implemented experimentally in a cascade with the electrical circuit to complete the self-sensing. The experimental tests and results verification with external sensors revealed that the measured displacements given by the developed self-sensing measurement technique derive better than a micron of precision which well fits to micromanipulation applications. Discussion about the features and the performances improvement perspectives of the suggested approach are presented at the end of the paper. [ABSTRACT FROM AUTHOR]

This paper focuses on the observer-based methods for induction machine sensorless drive. It is well known that the use of speed observer is limited at a very low stator frequency due to speed unobservability. Some existing methods propose to avoid the zero-stator-frequency working points. These methods are nonetheless limited to high-torque operations. The first contribution of this paper is to analyze finely the speed observability, which leads to the definition of an observability index. The correlation between this index and the observer performance is analyzed and illustrated. The second contribution is to propose a control strategy that maximizes the observability index and improves the observer performance. The proposed strategy is tested first in open loop to evaluate the speed observer performance, and then, in closed loop to illustrate its pertinence for induction machine sensorless drive. All these results are validated by an experiment. The performed experimentation shows that the proposed control strategy gives better results than the existing method that avoid the zero-frequency working points. It offers a new path for induction machine sensorless drives. [ABSTRACT FROM PUBLISHER]

This paper discloses two high-sensitivity probes for Eddy Current Nondestructive Test (NDT) of buried and surface defects. These probes incorporate eight and 32 magnetoresistive sensors, respectively, which are optimized for high sensitivity and spatial resolution. The signal processing and interfacing are carried out by a full-custom application-specific integrated circuit (ASIC). The ASIC signal chain performs with a thermal input-referred noise of 30 nV/ $\sqrt{\text{Hz}}$ at 1 kHz, with 66 mW of power consumption, in a die with 3.7×3.4 mm $^2$. NDT results are presented, showing that there is an increase in spatial resolution of surface defects when contrasted to prior art, enabling the probes to resolve defects with diameters of 0.44 mm, pitches of 0.6 mm, and minimum edge distance as low as 0.16 mm. The results also show that the probe for buried defects is a good all-round tool for detection of defects under cladding and multiple-plate flat junctions. [ABSTRACT FROM AUTHOR]

In this paper, a novel method is developed to detect fault and identify its spatial location for a class of parabolic distributed parameter systems (DPSs) with limited sensors. The normal situation of the DPSs is first modeled under limited sensors. Then, the spatio–temporal dynamics of DPSs is decoupled under time/space separation. After the temporal coefficients are further decomposed using the independent component analysis method, the dominant temporal components are taken and then the spatial residual errors are used to form two monitoring statistics. Using the kernel density function, the confidence bounds of these two statistics (fault free) can be established as the reference signals. Unlike model-based fault detection methods that require explicit mathematical models of the processes, the proposed method is data driven and only utilizes the separable characteristic of parabolic DPSs. Experiments on a typical parabolic process and a snap curing oven are used to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The offset voltage variation of current sensors is mainly due to the thermal drift, and it causes torque ripples in current vector-controlled surface permanent magnet synchronous motor (SPMSM) drives. This paper proposes a current offset error compensator for the current vector-controlled SPMSM drives used in robotic applications, wherein dynamic movements frequently occur. The current offset error compensator for this kind of applications should provide a stable performance in estimating current offset errors during dynamic movements. Especially, the current offset error compensator should be able to suppress interferences from outer-loop controllers. In this paper, analysis on a synchronous frame current regulator is performed to see the relation between current commands and measured currents under the current offset error existing condition. The analysis shows that pure current offset error components can be obtained by subtracting low-pass filtered current commands from measured currents in a synchronous reference frame. It means that the interference from current commands in correcting current offset errors can be easily decoupled by the simple subtraction process, and the dynamic control capability of a current offset error compensator could be significantly improved. The dynamic performance of the proposed current offset error compensator is proved through simulations and experiments. [ABSTRACT FROM AUTHOR]

This paper proposes a promising cost-effective phase current detection scheme for different multiphase switched reluctance motors (SRMs) by employing the multiplexed current sensors. According to the phase current characteristics for the odd-numbered multiphase SRM with m phases and even-numbered multiphase SRM with n phases, the phase windings are specially divided into (m + 1)/2 and n/2 groups, respectively, and each group shares one Hall-effect current sensor. By building the matrix functions to express the relationship between phase currents and sensor currents, the phase currents can be calculated in real time with a simple algorithm and fewer current sensors. Compared to existing phase current detection schemes, the developed method has high universality and can achieve the intact phase current detection without losing any excitation, freewheeling, and demagnetization currents. Moreover, the presented method is easy to implement without any converter change and pulse injection, which shows great potential for massive industrial application to avoid the voltage penalty, current distortion, higher switching loss, and further electromagnetic interference issues. The simulation and experiments are carried out to validate the feasibility and effectiveness of the developed phase current detection scheme. [ABSTRACT FROM AUTHOR]

In practice, sensor precision degradation is ubiquitous and early detection of such a degradation is important for monitoring task. In this paper, a cumulative canonical correlation analysis (CCA) based sensor precision degradation detection method is presented in the Gaussian and non-Gaussian cases. At first, the fault sensitivity of the original CCA method to sensor precision degradation is theoretically analyzed. Then, the cumulative CCA-based method is proposed and delivers better fault detectability than the corresponding standard CCA-based method with respect to fault detection rate. For the non-Gaussian case, an efficient and practical applicable approach, referred as threshold learning approach, is proposed to set appropriate threshold based on available historical measurements. Finally, with the application to a real laboratorial continuous stirred tank heater plant, the feasibility and superiority of the proposed method are demonstrated by a comparison with the standard CCA-based and principal component analysis-based methods. [ABSTRACT FROM AUTHOR]

With the continuously broadening application area of Internet of Things (IoT), robust energy harvesters and power conditioning units for supplying remote sensors and actuators are gaining importance. A single-sided linear induction machine (SLIM) with solid secondary operating in generator mode could meet these requirements as it can harvest electric energy from the kinetic energy of a moving conductive body (secondary). Therefore, the energy harvesting performance of SLIMs is studied in this paper, using analytical models, which are derived with special attention to the case where a conductive coating is applied on the mover—a simple and practical modification that is shown to increase the harvester performance significantly. Measurements are given for three different secondary materials and stator geometries. It is shown that 15 W of electrical power can be harvested with $\text{14}\%$ efficiency over an air gap of 0.5 mm from a copper-coated steel mover with a surface speed of 29 m/s, covering only $\text{17 cm}^{\text{2}}$ of the surface area. [ABSTRACT FROM AUTHOR]

In this paper, a nonlinear active disturbance rejection control (ADRC) scheme is designed to control the pneumatic muscle actuator system. To reflect the impact of the sampling period, a continuous-discrete extended state observer (CDESO) is employed to estimate the so-called total disturbance, which describes the unmodeled complexities. Then, a nonlinear composite controller is used, where the estimates provided by the CDESO act as the feedforward term. The sufficient conditions to guarantee the stability of the CDESO and the overall controlled system are provided, respectively. Finally, a number of practical experiments are conducted on a pneumatic motion platform in order to verify the applicability of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel event-triggered tracking framework for fast and robust visual tracking in the presence of model drift and occlusion. The resulting tracker not only operates in real time, but also is resilient to tracking failures caused by factors such as fast motion and heavy occlusion. Specifically, the tracker consists of an event-triggered decision model as the core module that coordinates other functional modules, including a short-term tracker, occlusion and drift identification, target redetection, short-term tracker updating, and online discriminative learning for a detector. Each functional module is associated with a defined event that is triggered when the proposed conditions are met. The occlusion and drift identification module is intended to perform online evaluation of the short-term tracking. When a model drift event occurs, the target redetection module is activated by the event-triggered decision model to relocate the target and reinitialize the short-term tracker. The short-term tracker updating is carried out at each frame with a variable learning rate depending on the degree of occlusion. A sampling pool is constructed to store discriminative samples that are used to update the detector model. Extensive experiments on large benchmark datasets demonstrate that the proposed tracking algorithm can effectively detect model drift and restore tracking, and more importantly, it outperforms the state-of-the-art approaches in terms of accuracy, efficiency, and robustness. [ABSTRACT FROM AUTHOR]

Safe and reliable operation of systems relies on the use of online condition monitoring and diagnostic systems that aim to take immediate actions upon the occurrence of a fault. Model-based solutions are often not practical in nonstationary environments. Thus, the evolving data stream requires the data-driven model to be adaptive. In this paper, we propose a framework for the fault detection and classification that is accomplished on the data stream with both the gradual and abrupt drifts. The framework is only provided with prior information about the possible faults at the initial step; however, despite this, the framework can still detect the novel faults without receiving any update. Furthermore, an efficient fault classification algorithm is presented to maximize the efficiency of the proposed framework. Finally, the proposed framework is applied for diagnosing bearing defects in the induction motors to demonstrate its feasibility for industrial applications. [ABSTRACT FROM AUTHOR]

This paper proposes a novel solution to the problems that exist in the conventional hill climb searching (HCS) maximum power point tracking (MPPT) algorithm for the wind energy conversion system. The presented solution not only solves the tracking speed versus control efficiency tradeoff problem of HCS but also makes sure that the changing wind conditions do not lead HCS in the wrong direction. It intelligently adapts the variable step size to keep up with the rapid changes in the wind and seizes the perturbation at the maxima to yield 100% control efficiency. For this purpose, a novel peak detection capability has been devised which, in contrast with conventional peak detection, can work robustly under changing wind conditions. The proposed MPPT performs self-tuning to cope with the nonconstant efficiencies of the generator–converter subsystems—a phenomenon quite rarely discussed in research papers so far. In addition, a smart speed-sensorless scheme has been developed to avoid the use of mechanical sensors. The experimental results confirm that the proposed algorithm is remarkably faster and more efficient than the conventional HCS. [ABSTRACT FROM AUTHOR]

RecentIy, many plastic products have been produced and used. These plastic products are mostly manufactured using injection molding machines. The quality of plastic products depends on the injection force. Therefore, it is important to develop a fine force-control system. Generally, in force-control systems, the force information from the environment is detected by a force sensor. However, control systems using force sensors present problems related to signal noise,sensor cost, narrow bandwidth, and other factors. To overcome these problems, this paper presents a proposal for a new sensorless force-control method using a reaction torque observer. The injection molding machine, which uses a ball screw, has a resonance frequency that is affected by the torsion phenomenon. This new reaction torque observer considers the torsion phenomenon and friction phenomena. [ABSTRACT FROM AUTHOR]

Controlling robots in contact with the environment is an important problem in industry applications. Generally, a force sensor is used for sensing the external force. It is well known that information of a force sensor has much noise. Furthermore, unstable state is also caused by the narrow bandwidth of force information by a force sensor. This paper shows that stable contact with the environment is difficult when a force sensor is used because of its soft mechanical structure and narrow bandwidth of force sensing. In order to solve the instability of force control, the disturbance observer is implemented instead of the force sensor. The disturbance observer can observe the external force without force sensors. When the disturbance observer is implemented in a robot, a force control system does not include a soft mechanism between a robot and the environment. Since a robot can detect the environmental information directly, a wide bandwidth of force sensing is attained. In this way, this paper solves the problems of force control by considering the force sensing method without changing the control architecture, and the ability of force control is improved. Experimental results show viability of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a force servoing method to suppress torsional vibration of two-mass resonant system. The resonance ratio control is one of the effective control methods of two-mass resonant system. In this method, the ratio between the resonant frequency of motor and arm is determined arbitrary according to the feedback of estimated reaction torque. The reaction torque is estimated by using a position sensitive detector (PSD). Since the estimation method does not need the parameter identification, the torsion information is obtained with accuracy. To attain the affinity and adaptability to environment, motion systems should control the reaction torque from the environment. In the fore servoing system, the force command is given as a disturbance of the arm portion. The arm disturbance is observed by the arm disturbance observer without force sensors. The proposed force servoing system is based on both the conventional PD control and the resonance ratio control, and the determination method of pole placement is discussed. The proposed force servoing system can realize both the suppression of the inner torsional reaction torque and the adaptation to unknown outer force inputs. The numerical and experimental results show the viability of the proposed method. [ABSTRACT FROM AUTHOR]

A number of HF signal injection techniques have been proposed for the sensorless zero-low-frequency control of induction machines (IMs). This paper reviews these methods and experimentally investigates their performance under true sensorless conditions for a standard cage IM with closed rotor slots. Implementation techniques covering hybrid methods, saliency decoupling, and saliency orientation are discussed. The paper concludes that, while HF techniques can outperform observer-based methods at low frequencies, the robust performance required for industrial application still presents a research challenge. [ABSTRACT FROM AUTHOR]

This paper addresses the design problem of false data injection (FDI) attacks against the output tracking control of networked systems, where the network-induced delays in the feedback and forward channels are considered. The main contributions of this paper are as follows. 1) To actively compensate for the two-channel network-induced delays, a Kalman filter-based networked predictive control scheme is designed for stochastic linear discrete-time systems. 2) From an attacker’s perspective, stealthy FDI attacks are proposed for both the feedback and forward channels so as to disrupt the stability of the resulting closed-loop system while avoiding the detection of a Kalman filter-based attack detector. 3) Both numerical simulations and practical experiments are carried out to show the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

Traditional fault diagnosis and prognosis (FDP) approaches are based on periodic sampling, i.e., samples are taken and algorithms are executed both in a periodic manner. As the volume of sensor data and complexity of algorithms keep increasing, the bottleneck of FDP is mainly the limited computational resources, which is particularly true for distributed applications where FDP functions are deployed on microcontrollers and embedded systems with limited computation resources. This paper introduces the concept of Lebesgue sampling (LS) in FDP and proposes a LS-based FDP (LS-FDP) framework. In the proposed LS-FDP, a novel diagnostic philosophy of “execution only when necessary” is developed in computation cost reduction. For prognosis, different from traditional approaches in which the prognostic horizon is on the time axis, the proposed approach defines the prognostic horizon on the fault state axis. With a reduced prognostic horizon, the LS-FDP naturally benefits the uncertainty management. The goal of this paper is to create the fundamental knowledge for LS-FDP solutions that are cost efficient, capable for the deployment on systems with limited computation sources, and supportive to the trend of distributed FDP schemes in complex systems. The design and implementation of particle-filter-based LS-FDP are presented with experimental results on lithium-ion batteries to verify the effectiveness of the proposed approaches. [ABSTRACT FROM PUBLISHER]

This paper investigates the problem of event-triggered H\infty control for a networked singular system with both state and input subject to quantizations. First, a discrete event-triggered scheme, which activates only at each sampling instance, is presented. Next, two new sector bound conditions of quantizers are proposed to provide a more intuitive stability analysis and controller design. Then, network conditions, quantizations, and the event-triggered scheme are modeled as a time-delay system. With this model, the criteria are derived for H\infty performance analysis, and codesigning methods are developed for the event trigger and the quantized state feedback controller. An inverted pendulum controlled through the network is given to demonstrate the effectiveness and potential of the new design techniques. [ABSTRACT FROM PUBLISHER]

Vehicular networks can be seen as the new key enablers of the future networked society. Vehicles traveling can act as mobile sensors and collect a variety of information that can be used to enable various new services such as environment monitoring, traffic management, urban surveillance, and so on. In this paper, we present “adaptive Anycasting solution for Vehicular Environments” (AVE), which is a message delivery protocol that combines geographical and topological information to dynamically adapt its behavior to network conditions. We focus on vehicle-to-infrastructure connectivity for cloud services, where the vehicles send the sensed information as individual and independent messages to a cloud service in the Internet. This scenario requires access to any available close-by roadside unit, thus making anycasting the ideal delivery mechanism. Simulations results show that the hybrid and adaptive approach of AVE is able to improve network performance. For example, regarding delivery ratio, AVE outperforms DYMO by 10% in sparse scenarios and outperforms delay-tolerant networking techniques by 10% in dense scenarios. [ABSTRACT FROM PUBLISHER]

When setting up an underwater acoustic sensor network (UASN), node deployment is the first and foremost task, upon which many fundamental network services, such as network topology control, routing, and boundary detection, will be built. While node deployment in 2-D terrestrial wireless sensor networks has been extensively studied, little attention has been received by their 3-D counterparts. This paper aims at analyzing the impacts of node deployment strategies on localization performances in a 3-D environment. More specifically, the simulations conducted in this paper reveal that the regular tetrahedron deployment scheme outperforms the random deployment scheme and the cube deployment scheme in terms of reducing localization error and increasing localization ratio while maintaining the average number of neighboring anchor nodes and network connectivity. Given the fact that random deployment is the primary choice for most of practical applications to date, our results are expected to shed some light on the design of UASNs in the near future. [ABSTRACT FROM PUBLISHER]

Current sensing based on the giant magnetoresistance (GMR) effect has been gaining attention due to its outstanding merits. In this paper, hysteretic models of the output characteristics of GMR sensors are presented, and corresponding algorithms are successfully applied to practical GMR sensors to compensate for measurement error due to hysteresis, which is particularly important for high-frequency applications. A 91% decrease in nonlinear error is achieved by the proposed advanced hysteretic model, and the applicable frequency range of the GMR sensor can be extended to around the cutoff frequency of sensor hardware, which is nearly 50 times larger than that based on the conventional linear models. This paper provides optimized solutions for GMR current sensors in different frequency ranges. [ABSTRACT FROM PUBLISHER]

This paper provides a theoretic and systematic analysis on the battery state of charge (SOC) estimation errors caused by sensor noises. The considered noises include the bias and the variance of both current and voltage sensors. Specifically, the bias and the variance of the SOC estimation error are derived as explicit functions of sensor noises, battery parameters, and observer tuning parameters. The derivation is performed for the Kalman filter, which is the most commonly used method for SOC estimation, and the least squares method. It is found that the observer parameter tuning is subject to a tradeoff between suppressing the estimation bias and variance. Either one of these two errors becomes dominant under different observer parameter ranges. The fundamental estimation error that cannot be mitigated through observer tuning has also been identified. The results have been validated by both simulation and experiment. The obtained theoretical findings are of great practical significance as they could be used to guide sensor selection and observer design, as well as enable online uncertainty management. [ABSTRACT FROM PUBLISHER]

This paper presents a sinusoidal current control scheme for the doubly fed induction generator (DFIG)-DC system to reduce the harmonic current losses of the stator and rotor windings. The DFIG-DC system can be controlled as an open-winding system if the DFIG is equivalent in the Г circuit. The stator is connected to the dc link through the diode bridge and the rotor is connected to the same dc link through the rotor-side converter. A phase-locked loop based on stator flux is applied for estimating not only the stator frequency but also the angle of stator flux linkage. The sinusoidal stator current can be achieved by injecting appropriate harmonic voltages to the rotor side. The rotor harmonic voltages are produced by applying the stator voltage directly without employing a series of resonant controllers. Since the voltage of the diode bridge depends on the direction of the flowing current, the stator voltage of DFIG can be expressed by the stator current avoiding the stator voltage sensors. In this way, the harmonic current losses of the stator and rotor windings can be greatly reduced, which is beneficial for system efficiency. Furthermore, the torque ripple and power ripple can also be reduced, which is a good operation condition of the DFIG-DC system. The proposed control method is validated through experiments. [ABSTRACT FROM PUBLISHER]

This paper proposes a simple and cost-effective current measurement technique for four-phase switched reluctance motor (SRM) control, by splitting the dual bus line of the converter, without pulse injection and voltage penalty. Only two Hall-effect sensors are utilized, where one is installed in the upper bus to measure two phase currents, and the other is placed in the lower bus to measure other two phase currents. In order to realize independent current measurement in the whole turn-on region, switching functions are redesigned so that upper switches of two phases act as the choppers, while lower switches of the other two phases are employed as the choppers. Compared to traditional drives, the developed system requires only two Hall-effect sensors in the dual bus line, without a need for individual phase sensors or additional devices, which reduces the cost and volume for SRM drives. Furthermore, compared to the single-sensor based current measurement scheme, the proposed method has no need to implement pulse injection and will not cause any voltage penalty and current distortion, which also improve the current measurement accuracy and system performance. Simulation and experiments carried out on a 150-W four-phase 8/6 SRM confirm the effectiveness of the proposed technique. [ABSTRACT FROM PUBLISHER]

Position and orientation system (POS), which typically integrates strapdown inertial navigation system (SINS) and global navigation satellite system (GNSS), serves as a key sensor in airborne remote sensing, mobile mapping, and vehicle localization. POS can provide reliable, high-frequency and high-precision motion parameters using nonlinear Kalman Filter models based on fusion methods, such as extended Kalman filter, unscented Kalman filter, central difference Kalman filter (CDKF), and square root CDKF (SR-CDKF). Although the nonlinear parametric models are of high efficiency, there are also limitation on their capabilities of prediction and estimation, as it is often impossible to model all aspects of POS. In this paper, Gaussian processes (GP)-based is presented to enhance the capabilities of prediction and estimation for parametric CDKF. On one hand, it can estimate the state vector of POS with the nonlinear parametric CDKF on condition that trained data is limited; on the other hand, GP can take both the noise and the uncertainty in the nonlinear parametric CDKF into consideration. Consequently, the incorporation of GP into CDKF can result in further performance improvement. The proposed approach is verified in the real experiment, and shows that large performance benefits are achieved through applying the enhanced GP-CDKF(EGP-CDKF) into the SINS/GNSS integrated system. [ABSTRACT FROM AUTHOR]