This paper presents a comprehensive survey of fault diagnosis and fault tolerant approaches for permanent magnet synchronous machines (PMSM). PMSMs are prominent in the pervading usage of electric motors, for their high efficiency, great robustness, reliability and low torque inertia. In spite of their extensive appliance, they can be quite non-resilient and inadequate in operation when faults appear in motor drive apparatus such as inverters, stator windings, sensors, etc. These may lead to insulation failure, torque fluctuations, overcurrent or even system collapse. On that account, fault diagnosis and fault tolerant methods are equipped to enhance the stability and robustness in PMSMs. Progressive methodologies of PMSM fault diagnosis and tolerance are classified, discussed, reviewed and compared in this paper, beginning with mathematical modeling of PMSM and then scrutinizing various fault conditions in PMSMs. Finally, the scope of research on the topic is highlighted. The contribution of this review is to emphasize optimistic schemes and to assist researchers with the latest trends in this field for future directions.
Outliers accompany control engineers in their real life activity. Industrial reality is much richer than elementary linear, quadratic, Gaussian assumptions. Outliers appear due to various and varying, often unknown, reasons. They meet research interest in statistical and regression analysis and in data mining. There are a lot of interesting algorithms and approaches to outlier detection, labelling, filtering and finally interpretation. Unfortunately, their impact on control systems has not been found sufficient attention in research. Their influence is frequently unnoticed, ignored or not mentioned. This work focuses on the subject of outlier detection and labelling in the context of control system performance analysis. Selected statistical data-driven approaches are analyzed, as they can be easily implemented with limited a priori knowledge. The study consists of a simulation study followed by the analysis of real control data. Different generation mechanisms are simulated, like overlapping Gaussian processes, symmetric and asymmetric, artificially shifted points and fat-tailed distributions. Simulation observations are confronted with industrial control loops datasets. The work concludes with a practical procedure, which should help practitioners in dealing with outliers in control engineering temporal data.
Large-scale mobile social networks (MSNs) facilitate communications through mobile devices. The users of these networks can use mobile devices to access, share and distribute information. With the increasing number of users on social networks, the large volume of shared information and its propagation has created challenges for users. One of these challenges is whether users can trust one another. Trust can play an important role in users′ decision making in social networks, so that, most people share their information based on their trust on others, or make decisions by relying on information provided by other users. However, considering the subjective and perceptive nature of the concept of trust, the mapping of trust in a computational model is one of the important issues in computing systems of social networks. Moreover, in social networks, various communities may exist regarding the relationships between users. These connections and communities can affect trust among users and its complexity. In this paper, using user characteristics on social networks, a fuzzy clustering method is proposed and the trust between users in a cluster is computed using a computational model. Moreover, through the processes of combination, transition and aggregation of trust, the trust value is calculated between users who are not directly connected. Results show the high performance of the proposed trust inference method.
3-RRR planar parallel robots are utilized for solving precise material-handling problems in industrial automation applications. Thus, robust and stable control is required to deliver high accuracy in comparison to the state of the art. The operation of the mechanism is achieved based on three revolute (3-RRR) joints which are geometrically designed using an open-loop spatial robotic platform. The inverse kinematic model of the system is derived and analyzed by using the geometric structure with three revolute joints. The main variables in our design are the platform base positions, the geometry of the joint angles, and links of the 3-RRR planar parallel robot. These variables are calculated based on Cayley-Menger determinants and bilateration to determine the final position of the platform when moving and placing objects. Additionally, a proposed fractional order proportional integral derivative (FOPID) is optimized using the bat optimization algorithm to control the path tracking of the center of the 3-RRR planar parallel robot. The design is compared with the state of the art and simulated using the Matlab environment to validate the effectiveness of the proposed controller. Furthermore, real-time implementation has been tested to prove that the design performance is practical.
Autism spectrum disorder (ASD) is a neurodevelopmental disorder affecting social, communicative, and repetitive behavior. The phenotypic heterogeneity of ASD makes timely and accurate diagnosis challenging, requiring highly trained clinical practitioners. The development of automated approaches to ASD classification, based on integrated psychophysiological measures, may one day help expedite the diagnostic process. This paper provides a novel contribution for classifing ASD using both thermographic and EEG data. The methodology used in this study extracts a variety of feature sets and evaluates the possibility of using several learning models. Mean, standard deviation, and entropy values of the EEG signals and mean temperature values of regions of interest (ROIs) in facial thermographic images were extracted as features. Feature selection is performed to filter less informative features based on correlation. The classification process utilizes Naïve Bayes, random forest, logistic regression, and multi-layer perceptron algorithms. The integration of EEG and thermographic features have achieved an accuracy of 94% with both logistic regression and multi-layer perceptron classifiers. The results have shown that the classification accuracies of most of the learning models have increased after integrating facial thermographic data with EEG.
In this paper, a novel compression framework based on 3D point cloud data is proposed for telepresence, which consists of two parts. One is implemented to remove the spatial redundancy, i.e., a robust Bayesian framework is designed to track the human motion and the 3D point cloud data of the human body is acquired by using the tracking 2D box. The other part is applied to remove the temporal redundancy of the 3D point cloud data. The temporal redundancy between point clouds is removed by using the motion vector, i.e., the most similar cluster in the previous frame is found for the cluster in the current frame by comparing the cluster feature and the cluster in the current frame is replaced by the motion vector for compressing the current frame. The first, the B-SHOT (binary signatures of histograms orientation) descriptor is applied to represent the point feature for matching the corresponding point between two frames. The second, the K-mean algorithm is used to generate the cluster because there are a lot of unsuccessfully matched points in the current frame. The matching operation is exploited to find the corresponding clusters between the point cloud data of two frames. Finally, the cluster information in the current frame is replaced by the motion vector for compressing the current frame and the unsuccessfully matched clusters in the current and the motion vectors are transmitted into the remote end. In order to reduce calculation time of the B-SHOT descriptor, we introduce an octree structure into the B-SHOT descriptor. In particular, in order to improve the robustness of the matching operation, we design the cluster feature to estimate the similarity between two clusters. Experimental results have shown the better performance of the proposed method due to the lower calculation time and the higher compression ratio. The proposed method achieves the compression ratio of 8.42 and the delay time of 1228 ms compared with the compression ratio of 5.99 and the delay time of 2163 ms in the octree-based compression method under conditions of similar distortion rate.
For complex systems with high nonlinearity and strong coupling, the decoupling control technology based on proportion integration differentiation (PID) neural network (PIDNN) is used to eliminate the coupling between loops.The connection weights of the PIDNN are easy to fall into local optimum due to the use of the gradient descent learning method.In order to solve this problem, a hybrid particle swarm optimization (PSO) and differential evolution (DE) algorithm (PSO-DE) is proposed for optimizing the connection weights of the PIDNN.The DE algorithm is employed as an acceleration operation to help the swarm to get out of local optima traps in case that the optimal result has not been improved after several iterations.Two multivariable controlled plants with strong coupling between input and output pairs are employed to demonstrate the effectiveness of the proposed method.Simulation results show that the proposed method has better decoupling capabilities and control quality than the previous approaches.
In practice, the model structure, parameters and time-delay of the actual process may vary simultaneously.However, the general identification methods of the 3 items are performed with separate procedures which is very inconvenient in practical application.In view of the fact that variable selection procedure can ensure a compact model with robust input-output relation and in order to explore the feasibility of variable selection algorithm for the simultaneous identification of process structure, parameters and time-delay, non-negative garrote (NNG) algorithm is introduced and applied to system identification and the corresponding procedures are presented.The application of NNG variable selection algorithm to the identification of single input single output (SISO) system, multiple input multiple output (MIMO) system and Wood-Berry tower industry are investigated.The identification accuracy and the time-series variable selection results are analyzed and compared between NNG and ordinary least square (OLS) algorithms.The derived excellent results show that the proposed NNG-based modeling algorithm can be utilized for simultaneous identification of the model structure, parameters and time-delay with high precision.
Design of an Ethernet network compatible data acquisition system for the measurement of yaw rate and longitudinal velocity in automobiles is presented.The data acquisition system includes a base node and a remote node.The remote node consists of a micro electro mechanical system (MEMS) accelerometer, an MEMS gyroscope, an advanced RISC machines (ARM) CORTEX M3 microcontroller and an Ethernet PHY device.The remote node measures the yaw rate and the longitudinal velocity of an automobile and sends the measured values to the base node using Ethernet communication.The base node consists of an ARM CORTEX M3 microcontroller and an Ethernet PHY device.The base node receives the measured values and saves in a microSD card for further analysis.The characteristics of the network and the measurement system are studied and reported.