Search

This paper proposes and experimentally evaluates a method, which is able to measure the refrigeration capacity of compressors in extremely short times. Traditionally, the measurement of this parameter is done using specialized and expensive test rigs and can take up to 4.5 h for the measurement of a single compressor. To overcome this long measurement time, it is proposed to use a simpler measuring system, in which, instead of measuring the refrigeration capacity directly, the rate of pressure rise imposed by the compressor to a cylinder with fixed volume is measured. This paper shows that both variables have a high correlation factor when the test and compressor conditions are taken into account. As the correlation among the variables is nonlinear, it is not possible to use simple mathematical correlation functions, so neural networks are used for that purpose. By using the proposed method, the refrigeration capacity of a compressor can be determined in less than 7 s, which is approximately 0.05% of the time required by traditional methods. To guarantee the metrological reliability of the results presented by the proposed neural model, this paper proposes a novel approach based on the Monte Carlo and the bootstrap methods. [ABSTRACT FROM AUTHOR]

In order to improve the pulp quality and to reduce the energy consumption, the fiber length distribution (FLD) is generally employed as one of the important technological indexes in the refining process. Considering that the traditional mean and variance of fiber length are unable to adequately characterize the non-Gaussian distribution properties, this paper proposes a novel geometric analysis based double closed-loop iterative learning control (ILC) method for probability density function (PDF) shaping of output FLD in the refining process. Primarily, a radial basis function (RBF) neural network with Gaussian-type is utilized to approximate the square root PDF in the inner loop, where the RBF basis function parameters (center and width) are tuned between any two adjacent batches by using an ILC law, and the subspace identification method can be applied to establish the state-space model of weight vector. Then, for the sake of accelerating the convergence rate of the closed-loop system, a geometric analysis based ILC method is adopted in the outer loop. Finally, both simulation and experiments demonstrate the effectiveness and practicability of the proposed approach. [ABSTRACT FROM AUTHOR]

The classification of distinct problems of insulators in the distribution networks is a task that requires operator's experience. The applications of techniques to automate the inspection of electrical systems with the objective of detecting faults in insulators have shown to be reasonable alternatives to improve reliability in power grid. In this paper, based on the development of an experimental setup, signals are acquired considering three distinct faults in insulators. In this case, 13.8 kV (rms) is applied in drilled, contaminated, and good insulators considering an ultrasound detector connected to a computer. In the sequence, a multiclass classification method is proposed considering the ensemble of classifiers. The method considers the association of five distinct techniques, Bottom-Up segmentation, wavelet energy coefficient, principal component analysis, and particle swarm optimization associated with ensemble extreme learning machine (EN-ELM). Named optimized ensemble extreme learning machine, the present approach outperforms the original EN-ELM method. Finally, results show significant increase in robustness and faster training procedure when compared to classical approaches. [ABSTRACT FROM AUTHOR]

Remaining useful life (RUL) prediction has been increasingly considered in many industrial fields for the reliability and safety of their systems. As a data analysis tool of deep learning, deep convolutional neural network (CNN) shows great potential for RUL prediction. This paper proposes an intelligent RUL prediction method based on a double-CNN model architecture. Given the powerful feature extraction capability of CNN, the proposed method is fed with original vibration signals with no need to resort to any feature extractor, which can also retain the useful information in maximum. The prediction includes two stages: first, incipient fault point is identified by the first CNN model and a proposed “3/5” principle; then, the second CNN model is constructed for RUL prediction. In practice, RULs of identical components are different from each other, which poses a major challenge in RUL prediction. To overcome this problem, an intermediate reliability variable is first calculated in this paper, instead of directly predicting the RUL value. Then, a mapping algorithm is proposed to map reliability to RUL. To demonstrate the effectiveness of the proposed method, data of four tests of bearing degradation are utilized for RUL prediction. Compared with state-of-the-art methods, the proposed method shows higher prediction accuracy and robustness. The prediction results and evaluation indexes demonstrated the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

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]

Time series classification is an important research topic in machine learning and data mining communities, since time series data exist in many application domains. Recent studies have shown that machine learning algorithms could benefit from good feature representation, explaining why deep learning has achieved breakthrough performance in many tasks. In deep learning, the convolutional neural network (CNN) is one of the most well-known approaches, since it incorporates feature learning and classification task in a unified network architecture. Although CNN has been successfully applied to image and text domains, it is still a challenge to apply CNN to time series data. This paper proposes a tensor scheme along with a novel deep learning architecture called multivariate convolutional neural network (MVCNN) for multivariate time series classification, in which the proposed architecture considers multivariate and lag-feature characteristics. We evaluate our proposed method with the prognostics and health management (PHM) 2015 challenge data, and compare with several algorithms. The experimental results indicate that the proposed method outperforms the other alternatives using the prediction score, which is the evaluation metric used by the PHM Society 2015 data challenge. Besides performance evaluation, we provide detailed analysis about the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a novel intelligent fault diagnosis method to automatically identify different health conditions of wind turbine (WT) gearbox. Unlike traditional approaches, where feature extraction and classification are separately designed and performed, this paper aims to automatically learn effective fault features directly from raw vibration signals while classify the type of faults in a single framework, thus providing an end-to-end learning-based fault diagnosis system for WT gearbox without additional signal processing and diagnostic expertise. Considering the multiscale characteristics inherent in vibration signals of a gearbox, a new multiscale convolutional neural network (MSCNN) architecture is proposed to perform multiscale feature extraction and classification simultaneously. The proposed MSCNN incorporates multiscale learning into the traditional CNN architecture, which has two merits: 1) high-level fault features can be effectively learned by the hierarchical learning structure with multiple pairs of convolutional and pooling layers; and 2) multiscale learning scheme can capture complementary and rich diagnosis information at different scales. This greatly improves the feature learning ability and enables better diagnosis performance. The proposed MSCNN approach is evaluated through experiments on a WT gearbox test rig. Experimental results and comprehensive comparison analysis with respect to the traditional CNN and traditional multiscale feature extractors have demonstrated the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes the energy-based controller (EC) incorporated with radical basis function (RBF) neural-network compensation (ECRBFC), which is used to swing up the Pendubot and raise it to its uppermost unstable equilibrium position. First, for the known dynamics model of the two-link arm, the EC is designed. In the EC, the singularity is successfully avoided by constructing an appropriate energy evaluation function. Second, as for the friction of the Pendubot, because of the time-varying characteristics, an accurate friction dynamics model cannot be known absolutely; thus, the RBF neural network is introduced to offset the bad effect of friction. Finally, in order to evaluate the performance of ECRBFC, the numerical simulations and the experimental results are given, and by comparing the results with that of other algorithms, it is found that ECRBFC proposed in this paper has better performance under the same conditions. [ABSTRACT FROM PUBLISHER]

In this paper, the analysis, design, and implementation of a novel rotor position estimator for the control of variable-speed switched reluctance generators (SRG5) are presented. The rotor position is obtained using the unsaturated instantaneous inductance. This unsaturated inductance is estimated calculating the slope of the phase current and using a reducedsize neural network (NN) whose inputs are the average current and the saturated inductance. The proposed estimator requires less processing time than traditional methods and can be fully implemented using a low-cost DSP with very few additional analog/digital components. The rotor position estimator presented in this paper can be applied to a wind energy conversion system where the SRG is used as a variable-speed generator. This application is currently being studied because the SRG has well-known advantages such as robustness, low manufacturing cost, and good size- to-power ratio. Simulation and experimental results are presented using a 2.5-kW 8/6-SRG prototype. [ABSTRACT FROM AUTHOR]

Human pose recovery in videos is usually conducted by matching 2-D image features and retrieving relevant 3-D human poses. In the retrieving process, the mapping between images and poses is critical. Traditional methods assume this mapping relationship as local joint detection or global joint localization, which limits recovery performance of these methods since this two tasks are actually unified. In this paper, we propose a novel pose recovery framework by simultaneously learning the tasks of joint localization and joint detection. To obtain this framework, multiple manifold learning is used and the shared parameter is calculated. With them, multiple manifold regularizers are integrated and generalized eigendecomposition is utilized to achieve parameter optimization. In this way, pose recovery is boosted by both global mapping and local refinement. Experimental results on two popular datasets demonstrates that the recovery error has been reduced by 10%–20%, which proves the performance improvement of the proposed method. [ABSTRACT FROM PUBLISHER]

This paper presents a practical discrete-time fractional order terminal sliding mode (DFOTSM) control strategy for high-precision tracking tasks based on a linear motor. In particular, the practical parametric uncertainties involving sliding friction, uncertain payload, and disturbance in tracking tasks are considered in this paper. Combining Grünwald–Letnikov fractional order definition and terminal sliding mode technique, the proposed method synthesizes a novel DFOTSM control law to drive the sliding mode dynamics into the stable region in finite steps theoretically, even though the system is suffering from uncertainties and disturbances, and the motion on the surface can guarantee higher tracking precision than the conventional discrete-time terminal sliding surface by selecting suitable controller parameters. The theoretical analyses give out the guideline of parameter selection, and experiments are carried out on the linear-motor-based test platform to demonstrate that the proposed controller is easily implemented and can achieve high-precision tracking, fast response, and considerable robustness to uncertainties. [ABSTRACT FROM AUTHOR]

The general trend toward more intelligent energy-aware ac drives is driving the development of new motor topologies and advanced model-based control techniques. Among the candidates, pure reluctance and anisotropic permanent magnet motors are gaining popularity, despite their complex structure. The availability of accurate mathematical models that describe these motors is essential to the design of any model-based advanced control. This paper focuses on the relations between currents and flux linkages, which are obtained through innovative radial basis function neural networks. These special drive-oriented neural networks take as inputs the motor voltages and currents, returning as output the motor flux linkages, inclusive of any nonlinearity and cross-coupling effect. The theoretical foundations of the radial basis function networks, the design hints, and a commented series of experimental results on a real laboratory prototype are included in this paper. The simple structure of the neural network fits for implementation on standard drives. The online training and tracking will be the next steps in field programmable gate array based control systems. [ABSTRACT FROM PUBLISHER]

The Fuzzy ARTMAP is a supervised learning method, providing high accuracy in many classifications. In this paper, we explore the role of hardware accelerators in remote sensing classification missions. We focus on the designing and implementing a massively parallel hardware architecture on a field-programmable gate array (FPGA) of the performance phase's algorithm. The implementation is mapped on Xilinx Virtex 6 XC6VLX240T FPGA chip for an embedded system using Xilinx ISE 14.5 software. Embedded blocks dedicated to digital signal processing (DSP) and blocks memories are used. DSP48E1 is part of Virtex-6 FPGAs devices. It boasts of increased capability over previous generations, and is highly customizable, a key feature of this primitive that has motivated and enabled the work presented in this paper. We illustrate the application of this methodology to the valuation of various schemes involving embedded elements. This paper also presents a summary of the performance cost with regard to the speed, power, and required computational resources. [ABSTRACT FROM AUTHOR]

Owing to the increasing complexity of electrical systems, diagnostic techniques of cables used for connecting electrical elements are essential for system maintenance in order to prevent a failure that can cause significant impacts on the overall electrical systems. Multicore structures are typically used as control and instrumentation cables in nuclear power plants, and the failure of the control and instrumentation cables can result in a disaster such as a radiation leak. In this paper, a method for the diagnosis of multicore cables is proposed based on the reflectometry. The diagnosis relates to the classification of defective cores in joint, which is one of the weakest parts in cable systems. The reflected signals obtained through reflectometry are converted into images by an advanced image processing algorithm, and the images are classified using artificial neural networks. The proposed method is demonstrated by experimental data using a real-world multicore cable. In the experiment, the faults are emulated similar to real-world defects using a potentiometer. It is expected that the proposed technique will enhance the stability and reliability of multicore cable systems. [ABSTRACT FROM AUTHOR]

In this paper, a distributed fast fault diagnosis approach is proposed for multimachine power systems based on deterministic learning (DL) theory. First, a learning estimator is constructed to accumulate the knowledge of transient fault dynamics in power systems. Through DL, a partial persistent excitation condition of the local neurons is satisfied and convergence of the neural weights is achieved. Thus, a knowledge bank can be established and gradually updated. To deal with the high dimensional data, only the neurons centered in a neighborhood of the system trajectory are activated. In this manner, the computation load is mitigated. Second, by using the learnt knowledge, a distributed fault diagnosis scheme is designed to monitor the power system. The memory of the transient fault dynamics can be quickly recalled and a fast fault diagnosis decision is made. Finally, based on the concepts of mismatch interval and duty ratio, the diagnosis capabilities of the proposed scheme are investigated. The effectiveness of the proposed diagnosis scheme is demonstrated by computer simulation and the hardware-in-loop experimental test based on the RT-LAB realtime simulator. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive trajectory tracking control algorithm for underactuated unmanned surface vessels (USVs) with guaranteed transient performance is proposed. To meet the realistic dynamical model of USVs, we consider that the mass and damping matrices are not diagonal and the input saturation problem. Neural networks (NNs) are employed to approximate the unknown external disturbances and uncertain hydrodynamics of USVs. Moreover, both full-state feedback control and output feedback control are presented, and the unmeasurable velocities of the output feedback controller are estimated via high-gain observer. Unlike the conventional control methods, we employ the error transformation function to guarantee the transient tracking performance. Both simulation and experimental results are carried out to validate the superior performance via comparing with traditional potential integral control approaches. [ABSTRACT FROM AUTHOR]

This paper introduces a new application of deep belief network (DBN) as an emerging artificial neural network for recognizing and classifying different partial discharge (PD) patterns. Phase resolved PD (PRPD) technique with different window intervals is used to manipulate three PD types, including corona, surface, and internal discharges measured in a high-voltage lab. Four approaches are proposed for extracting features from the raw measured data. In the first approach, the DBN is used as both a feature extractor and a PD classifier. The other three approaches extract discriminatory features using statistical and vector-norm-based operators to train a DBN classifier. The impact of the various phase windows through the PRPD method on the performance of the trained classifier is evaluated to obtain the best window interval. It is shown that the deep architectures are capable of learning important distinguishable features from PD data without any data preprocessing. This eliminates time-consuming feature extraction processes that produce the handcrafted features. Based on a comparison analysis, when the input data are corrupted by noise levels or no feature extraction technique is used to preprocess the data, the proposed approach outperforms other techniques, such as artificial neural networks, fuzzy logic classifiers, and support vector machines. [ABSTRACT FROM AUTHOR]

A global and real-time control with embedded hardware artificial neural network (ANN) for imbalance current suppression of parallel connected insulated gate bipolar transistors (IGBTs) is first proposed in this paper. This method focuses on control strategy and control execution. The former one is realized by porting the ANN-based PID (ANN-PID) strategy in the control loop to yield the real-time and adaptive characteristics without IGBT quantity limitation. The latter one is realized by designing the IGBT gate quantity of charge regulator (GQR) to execute the command from ANN-PID controller. The evaluation of ANN-PID algorithm results 0.023% mean error in IGBT current control that reveals the feasibility of the proposed method. A full prototype with FPGA-based hardware accelerator for ANN-PID computing, including the designed GQR circuit, has been built for realization and qualification in a buck converter with parallel connected IGBTs. The experimental results show that the performance of the proposed method in imbalance current suppression is improved about 3.5–5.5 times as the load increase from low to high with the advantage of immunity to load change and the current imbalance can be suppressed within 4%. [ABSTRACT FROM AUTHOR]

In recent years, artificial intelligent techniques have been extensively explored in the field of health monitoring and fault diagnosis due to their powerful capabilities. In this paper, we propose a deep coupled dense convolutional network (CDCN) with complementary data to integrate information fusion, feature extraction, and fault classification together for intelligent diagnosis. In this framework, built-in and external sensor data are first developed to form the input of network in parallel. Then, a one-dimensional CDCN is proposed, which not only could naturally build deeper network with alleviating the loss of features and gradient vanishing, but also develops a double-level information fusion strategy, including self-information fusion and mutual-information fusion, to facilitate the transmission of fault information and capture more comprehensive features. Finally, the extracted joint features are used for fault recognition and classification. The proposed approach is evaluated on a planetary gearbox test-bed. The results demonstrate the validity and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Online recognition of gestures is critical for intuitive human–robot interaction (HRI) and further push collaborative robotics into the market, making robots accessible to more people. The problem is that it is difficult to achieve accurate gesture recognition in real unstructured environments, often using distorted and incomplete multisensory data. This paper introduces an HRI framework to classify large vocabularies of interwoven static gestures (SGs) and dynamic gestures (DGs) captured with wearable sensors. DG features are obtained by applying data dimensionality reduction to raw data from sensors (resampling with cubic interpolation and principal component analysis). Experimental tests were conducted using the UC2017 hand gesture dataset with samples from eight different subjects. The classification models show an accuracy of 95.6% for a library of 24 SGs with a random forest and 99.3% for 10 DGs using artificial neural networks. These results compare equally or favorably with different commonly used classifiers. Long short-term memory deep networks achieved similar performance in online frame-by-frame classification using raw incomplete data, performing better in terms of accuracy than static models with specially crafted features, but worse in training and inference time. The recognized gestures are used to teleoperate a robot in a collaborative process that consists in preparing a breakfast meal. [ABSTRACT FROM AUTHOR]

The demand for electricity for the fused magnesia smelting process is calculated by the moving average of the energy consumption of a given period, which gives the efficiency of the smelting and indicates if the power supply should be cut off. Therefore, demand forecasting is very important for the operation of the smelting process. However, it is difficult to forecast demand since the power changes depend on the smelting process and raw materials. To obtain an accurate model, the mechanism model of the smelting process is combined with the data-driven method using artificial intelligence technology in this paper. The mechanism model is described by a linear model with unknown parameters. The uncertainties and error of the mechanism model are modeled by neural networks with unknown order. The maximal information coefficient method and the rule reasoning are combined to identify the order. To effectively combine the mechanism model and the data-driven model, a new saturated alternating identification strategy is proposed. The results of simulations and industrial applications show that the effectiveness of the proposed intelligent method of demand forecasting has been validated. [ABSTRACT FROM AUTHOR]

This paper focuses on developing an advanced nonlinear controller for hydraulic system to achieve asymptotic tracking with various disturbances. To accomplish this study, a multilayer neural-networks (NNs) estimator is first developed to improve the compensation accuracy of model-based feedforward control terms, which can greatly reduce the uncompensated disturbance, then a continuous robust integral of the sign of the error (RISE) control approach is effectively integrated with the multilayer NNs estimator to deal with the residual mismatched disturbance, in which the RISE feedback gain is adapted online to further decrease the high-gain feedback. At last, by considering the inwardness of matched disturbance in hydraulic systems, it is estimated by another multilayer NNs fully with the residual functional reconstruction inaccuracies handled by a novel adaptive term. As a result, theoretical analysis reveals that the proposed controller guarantees a semiglobal asymptotic stability. Extensively comparative experimental results verify the priority of the proposed control strategy, and a 0.2% dynamic tracking accuracy is achieved. [ABSTRACT FROM AUTHOR]

This paper proposes the use of an artificial neural network (ANN) for solving one of the ongoing research challenges in finite set-model predictive control (FS-MPC) of power electronics converters, i.e., the automated selection of weighting factors in cost function. The first step in this approach is to simulate a detailed converter circuit model or run experiments numerous times using different weighting factor combinations. The key performance metrics [e.g., average switching frequency ($f_{{\rm sw}}$) of the converter, total harmonic distortion, etc.] are extracted from each simulation. This data is then used to train the ANN, which serves as a surrogate model of the converter that can provide fast and accurate estimates of the performance metrics for any weighting factor combination. Consequently, any arbitrary user-defined fitness function that combines the output metrics can be defined and the weighting factor combinations that optimize the given function can be explicitly found. The proposed methodology was verified on a practical weighting factor design problem in FS-MPC regulated voltage source converter for uninterruptible power supply system. Designed weighting factors for two exemplary fitness functions turned out to be robust to load variations and to yield close to expected performance when applied both to detailed simulation model (less than 3% error) and to experimental test bed (less than 10% error). [ABSTRACT FROM AUTHOR]

This paper investigates the optimal consensus control problem for discrete-time multi-agent systems with completely unknown dynamics by utilizing a data-driven reinforcement learning method. It is known that the optimal consensus control for multi-agent systems relies on the solution of the coupled Hamilton–Jacobi–Bellman equation, which is generally impossible to be solved analytically. Even worse, most real-world systems are too complicated to obtain accurate mathematical models. To overcome these deficiencies, a data-based adaptive dynamic programming method is presented using the current and past system data rather than the accurate system models also instead of the traditional identification scheme which would cause the approximation residual errors. First, we establish a discounted performance index and formulate the optimal consensus problem via Bellman optimality principle. Then, we introduce the policy iteration algorithm which motivates this paper. To implement the proposed online action-dependent heuristic dynamic programming method, two neural networks (NNs), 1) critic NN and 2) actor NN, are employed to approximate the iterative performance index functions and control policies, respectively, in real time. Finally, two simulation examples are provided to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

The generalized modulation strategy of the conventional matrix converter (MC), i.e., three-leg MC, was previously developed using the duty-cycle space vector (DCSV) representation of switch duty cycles. This paper uses the DCSV representation of switch duty cycles to develop a new formulation of the generalized modulation strategy for one-, three-, and four-leg MCs. The four-leg MC, unlike the three-leg MC, is capable of providing a neutral connection at the output side of the MC. The neutral connection enables the four-leg MC to operate as a power supply providing a balanced, regulated, and fixed-frequency voltage for balanced and unbalanced loads, including the single-phase load. The generalized modulation strategy of the four-leg MC in this paper is developed in the context of power supply applications. Using the new formulation of the generalized modulation strategy, the direct duty-cycle modulation strategies based on DCSV calculation are developed for one-, three-, and four-leg MCs, which are referred to as the DCSV-based modulation strategy of the MC. Part I of the paper formulates the DCSV-based modulation strategy of the three- and four-leg MCs. [ABSTRACT FROM PUBLISHER]

In this paper, an incremental centralized formation system is developed for controlling the multirobot formation with joining robots, and a nonlinear model predictive control (NMPC) method is implemented as the controller. The incremental updating method is used to update the system's state in real time, when there is a new robot joining during the formation process. Then, an NMPC approach is developed to reformulate the formation system into a convex nonlinear minimization problem, which can be further transformed into a quadratic programming (QP) with constraints. Then, a general projection neural network (GPNN) is implemented for solving this QP problem online to get the optimal inputs. In the end, two examples of incremental multirobot formation are demonstrated to verify the effectiveness of this method. [ABSTRACT FROM AUTHOR]

This paper studies the two-timescales operational optimal control problem for the flotation industrial process with unknown operational model in the presence of setpoint constraints on the device layer. A novel dual-rate data-driven algorithm based on lifting technology and reinforcement learning (RL) is proposed. First, a dual-rate flotation process model including the device layer and the operational models is formulated. Then, a proportional integral (PI) controller for device layer is designed, by using lifting technology, a unified timescale controlled plant model with partially unknown dynamics is established. Based on such a model, an online learning algorithm using neural network is presented so that the operational indices, namely concentrate and tail grades, can be kept in the target range while maintaining the setpoints of the device layer within the specified bounds. At last, emulation experiments in a hardware-in-the-loop system are used to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Tool condition monitoring is critical in ultraprecision manufacturing in order to optimize the performance of the overall process, while maintaining the desired part quality. Recently, deep learning has been successfully applied to numerous classification tasks in manufacturing, often to forecast part quality. In this paper, a novel deep learning data-driven modeling framework is presented, which includes a fusion of multiple stacked sparse autoencoders for tool condition monitoring in ultraprecision machining. The proposed computational framework consists of two main structures. First, a training model that is designed with the ability to process multiple parallel feature spaces to learn the lower-level features. Second, a feature fusion structure that is used to learn the higher-level features and associations to tool wear. To achieve this learning structure, a modified loss function is utilized that enhances the feature extraction and classification tasks. A dataset from a real manufacturing process is used to demonstrate the performance of the proposed framework. Experimental results and simulations show that the proposed method successfully classifies the ultraprecision machining case study with over 96% accuracy, while also outperforming comparable methodologies. [ABSTRACT FROM AUTHOR]

Bearing remaining useful life (RUL) prediction plays a crucial role in guaranteeing safe operation of machinery and reducing maintenance loss. In this paper, we present a new deep feature learning method for RUL estimation approach through time frequency representation (TFR) and multiscale convolutional neural network (MSCNN). TFR can reveal nonstationary property of a bearing degradation signal effectively. After acquiring time-series degradation signals, we get TFRs, which contain plenty of useful information using wavelet transform. Owing to high dimensionality, the size of these TFRs is reduced by bilinear interpolation, which are further regarded as inputs for deep learning models. Here, we introduce an MSCNN model structure, which keeps the global and local information synchronously compared to a traditional convolutional neural network (CNN). The salient features, which contribute for RUL estimation, can be learned automatically by MSCNN. The effectiveness of the presented method is validated by the experiment data. Compared to traditional data-driven and different CNN-based feature extraction methods, the proposed method shows enhanced performance in the prediction accuracy. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach for detection of stator short-circuit faults in three-phase induction motors. The method is based on two stages: feature extraction and classification by intelligent systems. First, mutual information is estimated from delayed stator current signals, which are used as inputs of C4.5 decision trees and multilayer perceptron neural networks in the second step. Several offline and online experimental tests are presented considering voltage unbalance, load torque variations, and 1% to 10% short-circuit levels. The obtained results corroborate the effectiveness of this new diagnostic approach. [ABSTRACT FROM AUTHOR]

In this paper, a data-driven fault diagnosis model dealing with chemical imbalanced data streams is investigated. Different faults occur with varied frequencies by continuous arrival in chemical plants, while this issue has been hardly addressed in developing a diagnosis model. A novel incremental imbalance modified deep neural network (incremental-IMDNN) is proposed to promote the fault diagnosis to the imbalanced data stream. The first step in designing the incremental-IMDNN is the employment of an imbalance modified method combined with active learning for the extraction and generation of the most valuable information keeping in view the model feedback. DNN is utilized as a basic diagnosis model to excavate potential information. Then for the continuous arrival of new fault modes, DNN is promoted in an incremental hierarchical way. Unlike the traditional model that trained on a static snapshot of data, this model inherits the existing knowledge and hierarchically expands the diagnosis model by the similarity of faults. Similar faults that are judged by fuzzy clustering merge into a superclass, and every submodel shares the same architecture that is prevalent in previous research, which can be trained in parallel. We validate the performance of the proposed method in a Tennessee Eastman (TE) dataset, and the simulation results indicate that the proposed incremental-IM-DNN is better than the existing methods and possesses significant robustness and adaptability in chemical fault diagnosis. [ABSTRACT FROM AUTHOR]

Neural networks are widely used in pattern recognition, security applications, and robot control. We propose a hardware architecture system using tiny neural networks (TNNs) specialized in image recognition. The generic TNN architecture allows for expandability by means of mapping several basic units (layers) and dynamic reconfiguration, depending on the application specific demands. One of the most important features of TNNs is their learning ability. Weight modification and architecture reconfiguration can be carried out at run-time. Our system performs objects identification by the interpretation of characteristics elements of their shapes. This is achieved by interconnecting several specialized TNNs. The results of several tests in different conditions are reported in this paper. The system accurately detects a test shape in most of the experiments performed. This paper also contains a detailed description of the system architecture and the processing steps. In order to validate the research, the system has been implemented and configured as a perceptron network with back-propagation learning, choosing as reference application the recognition of shapes. Simulation results show that this architecture has significant performance benefits. [ABSTRACT FROM AUTHOR]

In this paper, a neural-dynamic optimization-based nonlinear model predictive control (NMPC) is developed for controlling leader–follower mobile robots formation. Consider obstacles in the environments, a control strategy is proposed for the formations which includes separation-bearing-orientation scheme (SBOS) for regular leader–follower formation and separation-distance scheme (SDS) for obstacle avoidance. During the formation motion, the leader robot shall track a desired trajectory and the desire leader–follower relationship can be maintained through SBOS method; meanwhile, the followers can avoid the collision by applying the SDS. The formation-error kinematics of both SBOS and SDS are derived and a constrained quadratic programming (QP) can be obtained by transforming the MPC method. Then, over a finite-receding horizon, the QP problem can be solved by utilizing the primal-dual neural network (PDNN) with parallel capability. The computation complexity can be greatly reduced by the implemented neural-dynamic optimization. Compared with other existing formation control approaches, the developed solution in this paper is rooted in NMPC techniques with input constraints and the novel QP problem formulation. Finally, experimental studies of the proposed formation control approach have been performed on several mobile robots to verify the effectiveness. [ABSTRACT FROM PUBLISHER]

In this paper, a discrete-time neural network for solving convex quadratic programming (QP) problems in constrained model predictive control (MPC) technology is investigated and implemented on a digital signal processor (DSP) device. This makes it possible to apply MPC technology to local control for high-dimensional multiple-input–multiple-output systems. The convergence issue of the discrete-time neural network is first studied. By choosing a proper error function, a sufficient condition is obtained under which the neural network converges to the exact optimal solution globally. This is the theoretical basis of this paper. An integrated hardware and software design method to implement the neural network on a DSP chip as a universal QP solver is then developed. With the QP solver handling the computational tasks in MPC problems, a general DSP-based MPC controller is achieved. A prototype system is built on a TMDSEVM6678L DSP development board. It is then applied to an air-separation-unit system and achieves satisfactory control performance. This verifies the effectiveness of the whole design. [ABSTRACT FROM PUBLISHER]

In this paper, a real-time implementation of selective harmonic elimination pulsewidth modulation (SHEPWM) using generalized Hopfield neural network (GHNN) as applied to the single-phase inverter is designed and discussed. Finding the location of the switching instants in the case of SHEPWM involves the solution of a set of nonlinear algebraic transcendental equations. The problem is redrafted as an optimization problem, and it is solved by using GHNN. Though, in principle, the $n$ number of harmonics can be eliminated using the presented methodology, this paper tries for the case of the elimination of the 5th-, 7th-, 11th-, and 13th-order harmonics while retaining the desired fundamental. An energy function is formulated for this problem and the set of ordinary differential equations (ODEs) describing the behavior of GHNN is obtained. A program in KEIL C was developed to solve these systems of ODEs by the Runge–Kutta fourth-order method to give the switching instants for continuously varying modulation indexes $(M)$. A MATLAB simulation was carried out, and an experimental setup was also constructed in order to validate the simulated results. The fast Fourier transform analysis of the simulated output voltage waveform and the experimental output voltage waveform confirms the effectiveness of the proposed method. Hence, the proposed method proves that it is much applicable in the industrial applications by virtue of its suitability in real-time applications. [ABSTRACT FROM PUBLISHER]

This paper concentrates on the problem of control of a hybrid energy storage system (HESS) for an improved and optimized operation of load-frequency control applications. The HESS consists of a supercapacitor serving as the main power source and a fuel cell serving as the auxiliary power source. First, a Hammerstein-type neural network is proposed to identify the HESS, which formulates the Hammerstein model with a nonlinear static gain in cascade with a linear dynamic block. It provides the model information for the controller to achieve the adaptive performance. Second, a feedforward neural network based on a back-propagation training algorithm is designed to formulate the proportional-integral-derivative (PID)-type neural network, which is used for the adaptive control of the HESS. Meanwhile, a dynamic antiwindup signal is designed to solve the operational constraint of the HESS. Then, an appropriate power reference signal for the HESS can be generated. Third, the stability and the convergence of the whole system are proved based on the Lyapunov stability theory. Finally, simulation experiments are followed through on a four-area interconnected power system to demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM PUBLISHER]

The key challenge of intelligent fault diagnosis is to develop features that can distinguish different categories. Because of the unique properties of mechanical data, predetermined features based on prior knowledge are usually used as inputs for fault classification. However, proper selection of features often requires expertise knowledge and becomes more difficult and time consuming when volume of data increases. In this paper, a novel deep learning network (LiftingNet) is proposed to learn features adaptively from raw mechanical data without prior knowledge. Inspired by convolutional neural network and second generation wavelet transform, the LiftingNet is constructed to classify mechanical data even though inputs contain considerable noise and randomness. The LiftingNet consists of split layer, predict layer, update layer, pooling layer, and full-connection layer. Different kernel sizes are allowed in convolutional layers to improve learning ability. As a multilayer neural network, deep features are learned from shallow ones to represent complex structures in raw data. Feasibility and effectiveness of the LiftingNet is validated by two motor bearing datasets. Results show that the proposed method could achieve layerwise feature learning and successfully classify mechanical data even with different rotating speed and under the influence of random noise. [ABSTRACT FROM PUBLISHER]

Recently, it has been verified by simulation results that the ability of recognizing and classifying patterns by artificial neural network can be successfully used for anti-islanding protection of distributed synchronous generators. However, since this type of protection differs from the available commercial relays, it is important to test the algorithm performance implemented in hardware. So, this paper presents a performance evaluation of the hardware implementation of an ANN-based algorithm for anti-islanding protection of distributed generators. For the evaluation process, a complete digital relay architecture is developed in LabVIEW, thus allowing its implementation in the commercial real-time platform cRIO-9025/cRIO-9018. The proposed scheme is tested under real conditions by means of the CMC-256-plus test set, as performed for conventional intelligent electronic devices. The proposed algorithm implemented in hardware shows high effectiveness, detecting all islanding conditions within 1 s, sharing the same good performance verified by computational simulations. The algorithm implementation shows robustness, as it remains blocked for other power grid disturbances, such as frequency variation in the grid utility, massive connection or load relief. [ABSTRACT FROM PUBLISHER]

The variable-flux flux-intensifying interior permanent magnet machine exhibits high efficiency with variable permanent magnet flux properties. Control algorithms for a single magnetization state and during transition between different magnetization states are developed. Maximum-torque-per-ampere control is implemented for loss reduction. However, the proposed prototype machine exhibits sophisticated nonlinear inductance properties, which have a significant impact on precise maximum-torque-per-ampere control. This paper proposes an artificial-neural-network-based maximum-torque-per-ampere control scheme. The well-constructed neural network exhibits an excellent capability in nonlinearity simulations and is suitable for maximum-torque-per-ampere current command generation. In addition, the magnetization state should be manipulated properly according to the machine's working condition. A novel magnetization-state control system that generates magnetization-state switch signals by calculating the dc-link voltage margin ratio is proposed. The combination of the neural-network-based maximum-torque-per-ampere control and the magnetization-state control constitutes the proposed control system. The entire control system is validated experimentally. [ABSTRACT FROM PUBLISHER]

In this paper, the robust trajectory tracking design of uncertain nonlinear systems is investigated by virtue of a self-learning optimal control formulation. The primary novelty lies in that an effective learning based robust tracking control strategy is developed for nonlinear systems under a general uncertain environment. The augmented system construction is performed by combining the tracking error with the reference trajectory. Then, an improved adaptive critic technique, which does not depend on the initial stabilizing controller, is employed to solve the Hamilton–Jacobi–Bellman (HJB) equation with respect to the nominal augmented system. Using the obtained control law, the closed-loop form of the augmented system is built with stability proof. Moreover, the robust trajectory tracking performance is guaranteed via Lyapunov approach in theory and then through simulation demonstration, where an application to a practical spring–mass–damper system is included. [ABSTRACT FROM PUBLISHER]

Based on neural network model, a method for quantitative sound quality (SQ) evaluation of the interior noise of a pure electric vehicle (PEV) is presented in this paper. The method can be divided into four steps. First, the interior noises under different speeds of a PEV are collected through the interior noise test of the PEV. Subsequently, one physical acoustic parameter (A-weighted sound pressure level) and six psychoacoustic parameters (loudness, fluctuation strength, tonality, roughness, articulation index, and sharpness) are applied to describe the noise samples for objective evaluation of SQ. In the third step, five semantic evaluation indexes, namely, “annoying or pleasing,” “harsh or sweet,” “weak or powerful,” “promiscuous and pure,” and “unobservable or perceptible,” are proposed based on semantic differential method, which are used for subjective evaluation of SQ by jury tests. Finally, the neural network model for SQ evaluation of the interior noise of the PEV is established, the SQ characteristics of the interior noise of the PEV are evaluated, as well as revealing the coefficient weight of influencing factors. This model can be used for SQ prediction and evaluation of the interior noise of the PEV considering that the average error is 9%. [ABSTRACT FROM AUTHOR]

In this paper, we present locomotion learning for an Anguilliform robotic fish using a central pattern generator (CPG) approach. First, we give the overall structure of the CPG. Different from a traditional CPG that contains only coupled oscillators, our CPG consists of coupled Andronov–Hopf oscillators, an artificial neural network (ANN), and an outer amplitude modulator. Coupled oscillators, which possess a limit-cycle character, are used to generate inputs to excite the ANN. The ANN serves as a learning mechanism, from which we can obtain desired waveforms. By inputting different signals to the ANN, different desired locomotion patterns can be obtained. Outer amplitude modulator resizes the amplitudes of the ANN outputs according to task specifications. The CPG possess temporal scalability, spatial scalability, and phase-shift property; thus, we can obtain desired amplitudes, oscillation frequencies, and phase differences by tuning corresponding parameters. By extracting the swimming pattern from a real fish and using the CPG approach, we successfully generate a new swimming pattern and apply it to the robotic fish. The new pattern reserves the swimming characters of the real fish, and it is more suitable to be applied to the robotic fish. By using the new pattern, the robotic fish can perform both forward locomotion and backward locomotion, which are validated by experiments. [ABSTRACT FROM PUBLISHER]

This paper presents a phase-locking control scheme based on artificial neural networks (ANNs) for active power filters (APFs). The proposed phase locking is achieved by estimating the fundamental supply frequency and by generating a phase-locking signal. The nonlinear-least-squares-based approach is modified to estimate the supply frequency. To improve the accuracy of frequency estimation, when the supply voltage contains harmonics that are not known, a prefiltering stage is introduced. In shunt APF applications, not only the information of frequency is sufficient but also the phase information of the supply voltage is required to generate a unit template that is phase-locked to the supply voltage. Therefore, in this paper, an adaptive-linear-neuron-based scheme is proposed to extract the phase information of the supply voltage. The estimated system frequency and phase information are then utilized to generate a phase-locking signal that assures a perfect synchronization with the fundamental supply voltage. To demonstrate the effectiveness of the proposed approach, the synchronous reference frame ( d-q theory) shunt APF control method with the proposed ANN-based phase-locking scheme is adopted. The performance of the proposed ANN-based approach is verified experimentally with different supply systems and load conditions. [ABSTRACT FROM PUBLISHER]

The detection and classification of power quality (PQ) disturbances have become a pressing concern due to the increasing number of disturbing loads connected to the power line and the susceptibility of certain loads to the presence of these disturbances; moreover, they can appear simultaneously since, in any real power system, there are multiple sources of different disturbances. In this paper, a new dual neural-network-based methodology to detect and classify single and combined PQ disturbances is proposed, consisting, on the one hand, of an adaptive linear network for harmonic and interharmonic estimation that allows computing the root-mean-square voltage and total harmonic distortion indices. With these indices, it is possible to detect and classify sags, swells, outages, and harmonics–interharmonics. On the other hand, a feedforward neural network for pattern recognition using the horizontal and vertical histograms of a specific voltage waveform can classify spikes, notching, flicker, and oscillatory transients. The combination of the aforementioned neural networks allows the detection and classification of all the aforementioned disturbances even when they appear simultaneously. An experiment under real operating conditions is carried out in order to test the proposed methodology. [ABSTRACT FROM PUBLISHER]

This paper investigates the data-based optimal control for a class of networked industrial processes with a double-layer architecture. Without knowing the dynamics of subsystems at the device layer, the index prediction function is constructed via the input/output signals, and radial basis function neural networks. The tuning laws for the index prediction function are obtained through the optimal control strategy. Then, by treating the network-induced phenomenon as random round-trip time delay and introducing the predictive algorithm, the compensation scheme is designed at the operation layer to dynamically decompose the setpoints. Finally, two simulation examples are given to further illustrate the effectiveness of the proposed compensation strategy. [ABSTRACT FROM AUTHOR]

In this paper, a new identification and control scheme using multitime scale recurrent high-order neural networks is proposed to control the singularly perturbed nonlinear systems with uncertainties. First, a novel identification scheme using modified optimal bounded ellipsoid based weight's updating laws is developed to identify the unknown nonlinear systems. By adding two additional terms to the original optimal bounded ellipsoid based weight's updating laws, the new modified identification scheme can achieve high convergence speed due to the adaptively adjusted learning gain at the beginning of the identification process and remain effective during the whole identification process. Based on the identified model, a new indirect adaptive control scheme for trajectory tracking problem using singular perturbation theory is developed, which is different from the control scheme proposed previously that can only be applied to a regulation problem. The closed-loop stability is analyzed and the convergence of system states is guaranteed. Experimental results are presented to demonstrate the effectiveness of the identification and control scheme. [ABSTRACT FROM AUTHOR]

This paper studies the fault-tolerant tracking control problem for a class of strict-feedback nonlinear systems subjected to actuator faults and external disturbances. The prior knowledge for actuator fault, nonlinearity, and external disturbance is totally unknown, besides the control directions. Based on a backstepping approach, an adaptive fault-tolerant control scheme is developed, without utilizing neural networks. In the control design, a group of new feedback mechanisms are proposed to compensate for the unknown system dynamics and actuator faults. Furthermore, to relax a requirement of the initial system states, a modification technique is designed to adjust the reference signal and virtual control laws for a short time. It is shown that the global closed-loop stability is guaranteed and the tracking performance is achieved. The above result is illustrated via simulations on a one-link manipulator and a ship autopilot. [ABSTRACT FROM PUBLISHER]

Early detection of the motor faults is essential and artificial neural networks are widely used for this purpose. The typical systems usually encapsulate two distinct blocks: feature extraction and classification. Such fixed and hand-crafted features may be a suboptimal choice and require a significant computational cost that will prevent their usage for real-time applications. In this paper, we propose a fast and accurate motor condition monitoring and early fault-detection system using 1-D convolutional neural networks that has an inherent adaptive design to fuse the feature extraction and classification phases of the motor fault detection into a single learning body. The proposed approach is directly applicable to the raw data (signal), and, thus, eliminates the need for a separate feature extraction algorithm resulting in more efficient systems in terms of both speed and hardware. Experimental results obtained using real motor data demonstrate the effectiveness of the proposed method for real-time motor condition monitoring. [ABSTRACT FROM AUTHOR]

A new approach for modulation of an 11-level cascade multilevel inverter using selective harmonic elimination is presented in this paper. The dc sources feeding the multilevel inverter are considered to be varying in time, and the switching angles are adapted to the dc source variation. This method uses genetic algorithms to obtain switching angles offline for different dc source values. Then, artificial neural networks are used to determine the switching angles that correspond to the real-time values of the dc sources for each phase. This implies that each one of the dc sources of this topology can have different values at any time, but the output fundamental voltage will stay constant and the harmonic content will still meet the specifications. The modulating switching angles are updated at each cycle of the output fundamental voltage. This paper gives details on the method in addition to simulation and experimental results. [ABSTRACT FROM PUBLISHER]

In this paper, a generalized formulation for intelligent energy management of a microgrid is proposed using artificial intelligence techniques jointly with linear-programming-based multiobjective optimization. The proposed multiobjective intelligent energy management aims to minimize the operation cost and the environmental impact of a microgrid, taking into account its preoperational variables as future availability of renewable energies and load demand (LD). An artificial neural network ensemble is developed to predict 24-h-ahead photovoltaic generation and 1-h-ahead wind power generation and LD. The proposed machine learning is characterized by enhanced learning model and generalization capability. The efficiency of the microgrid operation strongly depends on the battery scheduling process, which cannot be achieved through conventional optimization formulation. In this paper, a fuzzy logic expert system is used for battery scheduling. The proposed approach can handle uncertainties regarding to the fuzzy environment of the overall microgrid operation and the uncertainty related to the forecasted parameters. The results show considerable minimization on operation cost and emission level compared to literature microgrid energy management approaches based on opportunity charging and Heuristic Flowchart (HF) battery management. [ABSTRACT FROM PUBLISHER]