Search results for " machine"
showing 10 items of 1317 documents
Maximum Torque Per Ampere control algorithm for low saliency ratio interior permanent magnet synchronous motors
2017
This paper presents an investigation on the comparison between the Maximum Torque Per Ampere (MTPA) and the Field Orientation Control (FOC) algorithms for interior permanent magnet synchronous machines (IPMSMs). In particular, this study was carried out on a small-power IPMSM with low salience ratio. Both control algorithms have been implemented in the Matlab/Simulink environment, obtaining promising results.
Early detection and classification of bearing faults using support vector machine algorithm
2017
Bearings are one of the most critical elements in rotating machinery systems. Bearing faults are the main reason for failures in electrical motors and generators. Therefore, early bearing fault detection is very important to prevent critical system failures in the industry. In this paper, the support vector machine algorithm is used for early detection and classification of bearing faults. Both time and frequency domain features are used for training the support vector machine learning algorithm. The trained classier can be employed for real-time bearing fault detection and classification. By using the proposed method, the bearing faults can be detected at early stages, and the machine oper…
The helicoidal magnetic generator
2016
Recently helicoidal generator for the exploitation of sea wave energy has been proposed. This device can convert both the vertical and rotational movement of seawaves. The electrical energy generated by such a device must be converted and conditioned in order to match the instantaneous utility requirements and a power link from the sea to an interconnection is needed. In this paper, the authors propose a mathematical model of this device and preliminarily present a prototype of the machine.
A Planar Generator for a Wave Energy Converter
2019
This article presents a permanent magnet planar translational generator which is able to exploit multiple modes of sea wave energy extraction. Linear electrical generators have recently been studied for the exploitation of sea wave energy, but, to the best of our knowledge, no synchronous planar translational generator has been proposed. In this article, to maximize the energy extraction, we have considered all the potential modes of motion due to wave excitation and included them within the mathematical model of the proposed system. The principle of operation of the generator can be summarized as follows: the moving part (translator) of the generator is driven from the sea waves and induce…
Experimental comparison of two control algorithms for low-saliency ratio interior permanent magnet synchronous motors
2018
In this paper, an experimental investigation on the comparison between the Maximum Torque Per Ampere (MTPA) and the Field Orientation Control (FOC) algorithms for interior permanent magnet synchronous machines (IPMSMs) is described, analyzed and discussed. This investigation was carried out on a small-power IPMSM with low saliency ratio. More in detail, after a previous simulation study, the control techniques have been experimentally implemented and validated through means of a dSPACE® rapid prototyping system. The performances of the two algorithms have been evaluated and compared, obtaining interesting results.
Enhanced loss model algorithm for interior permanent magnet synchronous machines
2017
This paper presents an experimental study on the impact of the parameter variations over the performances of a LMA (Loss Model Algorithm) designed for an IPMSM (Interior Permanent Magnet Synchronous Machine). In a previous work, the characterization was carried out by assessing, for several working conditions, the motor parameters that influence the motor efficiency. The proposed enhanced loss model algorithm is implemented in a rapid prototyping system and its performances, in term of efficiency, are compared with other control systems, obtaining promising results.
Data-driven Fault Diagnosis of Induction Motors Using a Stacked Autoencoder Network
2019
Current signatures from an induction motor are normally used to detect anomalies in the condition of the motor based on signal processing techniques. However, false alarms might occur if using signal processing analysis alone since missing frequencies associated with faults in spectral analyses does not guarantee that a motor is fully healthy. To enhance fault diagnosis performance, this paper proposes a machinelearning based method using in-built motor currents to detect common faults in induction motors, namely inter-turn stator winding-, bearing- and broken rotor bar faults. This approach utilizes single-phase current data, being pre-processed using Welch’s method for spectral density es…
Computer-aided analysis and design procedure for rotating induction machine magnetic circuits and windings
2018
The aim of this study is to present a new, accurate, and user-friendly software procedure for the analysis and rapid design of rotating induction machine windings, considering both the electric and the magnetic specifications of the machine itself. This procedure is a valid aid for quick first stage design without the necessity of using finite element method (FEM)-based design procedures. FEM can be used in a second design phase in order to refine the first stage results. The design procedure is hereafter outlined and some examples show its capability.
Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3
2012
Abstract ESA's upcoming satellites Sentinel-2 (S2) and Sentinel-3 (S3) aim to ensure continuity for Landsat 5/7, SPOT-5, SPOT-Vegetation and Envisat MERIS observations by providing superspectral images of high spatial and temporal resolution. S2 and S3 will deliver near real-time operational products with a high accuracy for land monitoring. This unprecedented data availability leads to an urgent need for developing robust and accurate retrieval methods. Machine learning regression algorithms may be powerful candidates for the estimation of biophysical parameters from satellite reflectance measurements because of their ability to perform adaptive, nonlinear data fitting. By using data from …
Multi-phase classification by a least-squares support vector machine approach in tomography images of geological samples
2016
Abstract. Image processing of X-ray-computed polychromatic cone-beam micro-tomography (μXCT) data of geological samples mainly involves artefact reduction and phase segmentation. For the former, the main beam-hardening (BH) artefact is removed by applying a best-fit quadratic surface algorithm to a given image data set (reconstructed slice), which minimizes the BH offsets of the attenuation data points from that surface. A Matlab code for this approach is provided in the Appendix. The final BH-corrected image is extracted from the residual data or from the difference between the surface elevation values and the original grey-scale values. For the segmentation, we propose a novel least-squar…