Benchmarking Methods for Audio-Visual Recognition Using Tiny Training Sets

International audienceThe problem of choosing a classifier for audio-visual command recognition is addressed. Because such commands are culture- and user-dependant, methods need to learn new commands from a few examples. We benchmark three state-of-the-art discriminative classifiers based on bag of words and SVM. The comparison is made on monocular and monaural recordings of a publicly available dataset. We seek for the best trade off between speed, robustness and size of the training set. In the light of over 150,000 experiments, we conclude that this is a promising direction of work towards a flexible methodology that must be easily adaptable to a large variety of users

Scene Flow Estimation by Growing Correspondence Seeds

International audienceA simple seed growing algorithm for estimating scene flow in a stereo setup is presented. Two calibrated and synchronized cameras observe a scene and output a sequence of image pairs. The algorithm simultaneously computes a disparity map between the image pairs and optical flow maps between consecutive images. This, together with calibration data, is an equivalent representation of the 3D scene flow, i.e. a 3D velocity vector is associated with each reconstructed point. The proposed method starts from correspondence seeds and propagates these correspondences to their neighborhood. It is accurate for complex scenes with large motions and produces temporallycoherent stereo disparity and optical flow results. The algorithm is fast due to inherent search space reduction. An explicit comparison with recent methods of spatiotemporal stereo and variational optical and scene flow is provided

Audio-Visual Robot Command Recognition

International audienceThis paper addresses the problem of audio-visual command recognition in the framework of the D-META Grand Challenge. Temporal and non-temporal learning models are trained on visual and auditory descriptors. In order to set a proper baseline, the methods are tested on the ''Robot Gestures'' scenario of the publicly available RAVEL data set, following the leave-one-out cross-validation strategy. The classification-level audio-visual fusion strategy allows for compensating the errors of the unimodal (audio or vision) classifiers. The obtained results (an average audio-visual recognition rate of almost 80%) encourage us to investigate on how to further develop and improve the methodology described in this paper

The Harmonic Order Tracking Analysis Method for the Fault Diagnosis in Induction Motors Under Time-Varying Conditions

[EN] This paper introduces a new approach for improving the fault diagnosis in induction motors under time-varying conditions. A significant amount of published approaches in this field rely on representing the stator current in the time-frequency domain, and identifying the characteristic signatures that each type of fault generates in this domain. However, time-frequency transforms produce three-dimensional (3-D) representations, very costly in terms of storage and processing resources. Moreover, the identification and evaluation of the fault components in the time-frequency plane requires a skilled staff or advanced pattern detection algorithms. The proposed methodology solves these problem by transforming the complex 3-D spectrograms supplied by time-frequency tools into simple x-y graphs, similar to conventional Fourier spectra. These graphs display a unique pattern for each type of fault, even under supply or load time-varying conditions, making easy and reliable the diagnostic decision even for nonskilled staff. Moreover, the resulting patterns can be condensed in a very small dataset, reducing greatly the storage or transmission requirements regarding to conventional spectrograms. The proposed method is an extension to nonstationary conditions of the harmonic order tracking approach. It is introduced theoretically and validated experimentally by using the commercial induction motors feed through electronic converters.This work was supported by the Spanish "Ministerio de Economia y Competitividad" in the framework of the "Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a los Retos de la Sociedad" (Project reference DPI2014-60881-R). Paper no. TEC-00176-2016.Sapena-Bano, A.; Burriel-Valencia, J.; Pineda-Sanchez, M.; Puche-Panadero, R.; Riera-Guasp, M. (2017). The Harmonic Order Tracking Analysis Method for the Fault Diagnosis in Induction Motors Under Time-Varying Conditions. IEEE Transactions on Energy Conversion. 32(1):244-256. doi:10.1109/TEC.2016.2626008S24425632

Multi-Band Frequency Window for Time-Frequency Fault Diagnosis of Induction Machines

[EN] Induction machines drive many industrial processes and their unexpected failure can cause heavy producti on losses. The analysis of the current spectrum can identify online the characteristic fault signatures at an early stage, avoiding unexpected breakdowns. Nevertheless, frequency domain analysis requires stable working conditions, which is not the case for wind generators, motors driving varying loads, and so forth. In these cases, an analysis in the time-frequency domain¿such as a spectrogram¿is required for detecting faults signatures. The spectrogram is built using the short time Fourier transform, but its resolution depends critically on the time window used to generate it¿short windows provide good time resolution but poor frequency resolution, just the opposite than long windows. Therefore, the window must be adapted at each time to the shape of the expected fault harmonics, by highly skilled maintenance personnel. In this paper this problem is solved with the design of a new multi-band window, which generates simultaneously many different narrow-band current spectrograms and combines them into as single, high resolution one, without the need of manual adjustments. The proposed method is validated with the diagnosis of bar breakages during the start-up of a commercial induction motor.This research was funded by the Spanish "Ministerio de Ciencia, Innovacion y Universidades (MCIU)", the "Agencia Estatal de Investigacion (AEI)" and the "Fondo Europeo de Desarrollo Regional (FEDER)" in the framework of the "Proyectos I+D+i - Retos Investigacion 2018", project reference RTI2018-102175-B-I00 (MCIU/AEI/FEDER, UE).Burriel-Valencia, J.; Puche-Panadero, R.; Martinez-Roman, J.; Riera-Guasp, M.; Sapena-Bano, A.; Pineda-Sanchez, M. (2019). Multi-Band Frequency Window for Time-Frequency Fault Diagnosis of Induction Machines. Energies. 12(17):1-18. https://doi.org/10.3390/en12173361S118121

Simultaneous Pose, Correspondence and Non-Rigid Shape

Recent works have shown that 3D shape of non-rigid surfaces can be accurately retrieved from a single im- age given a set of 3D-to-2D correspondences between that image and another one for which the shape is known. However, existing approaches assume that such correspon- dences can be readily established, which is not necessarily true when large deformations produce significant appear- ance changes between the input and the reference images. Furthermore, it is either assumed that the pose of the cam- era is known, or the estimated solution is pose-ambiguous. In this paper we relax all these assumptions and, given a set of 3D and 2D unmatched points, we present an approach to simultaneously solve their correspondences, compute the camera pose and retrieve the shape of the surface in the input image. This is achieved by introducing weak priors on the pose and shape that we model as Gaussian Mixtures. By combining them into a Kalman filter we can progressively reduce the number of 2D candidates that can be potentially matched to each 3D point, while pose and shape are refined. This lets us to perform a complete and efficient exploration of the solution space and retain the best solution

New Method for Spectral Leakage Reduction in the FFT of Stator Currents: Application to the Diagnosis of Bar Breakages in Cage Motors Working at Very Low Slip

[EN] Motor current signature analysis has become a widespread fault diagnosis technique for induction machines (IMs), because it is noninvasive and requires low resources of hardware (a current sensor) and software (a fast Fourier transform). Nevertheless, its industrial application faces practical problems. One of its most challenging scenarios is the detection of broken bars in IMs working at very low slip, like large machines with a very small rated slip, or unloaded induction motors in off-line tests. In these cases, the leakage of the main supply component can hide the fault harmonics, even with a severe fault. Diverse solutions to this problem have been proposed, such as the use of smoothing windows, advanced spectral estimators, or the removal of the supply component. Nevertheless, these methods modify the spectral content of the current signal or add a high computational burden. In this work, a new approach is proposed, based on the analysis of the current with a very fine spectrum, obtained via simple zero padding, followed by the extraction of a practically leakage-free conventional, coarse spectrum. The method is experimentally validated by the diagnosis of a broken bar fault in a 3.15-MW induction motor.This work was supported in part by the Spanish "Ministerio de Ciencia, Innovacion y Universidades (MCIU)," in part by the "Agencia Estatal de Investigacion (AEI)," and in part by the "Fondo Europeo de Desarrollo Regional (FEDER)" in the framework of the "Proyectos I+D+i-Retos Investigacion 2018," Project under Grant RTI2018-102175-BI00 (MCIU/AEI/FEDER, UE). The Associate Editor coordinating the review process was Hongrui Wang. (Corresponding author: Manuel Pineda-Sanchez.)Puche-Panadero, R.; Martinez-Roman, J.; Sapena-Bano, A.; Burriel-Valencia, J.; Pineda-Sanchez, M.; Pérez-Cruz, J.; Riera-Guasp, M. (2021). New Method for Spectral Leakage Reduction in the FFT of Stator Currents: Application to the Diagnosis of Bar Breakages in Cage Motors Working at Very Low Slip. IEEE Transactions on Instrumentation and Measurement. 70:1-11. https://doi.org/10.1109/TIM.2021.30567411117

Fast Numerical Model of Power Busbar Conductors Through the FFT and the Convolution Theorem

[EN] Skin and proximity effects can cause a non-uniform current distribution in the electrical conductors used in alternating current (ac) busbar systems, which increases resistance, decreases internal inductance, and causes asymmetries in the electromagnetic fields and forces. As no explicit solution for the ac resistance or the ac internal inductance of a rectangular conductor has been found, numerical methods are needed to obtain the distribution of the currents inside the busbars. In this paper, a novel numerical approach, based on the fast Fourier transform (FFT) and the convolution theorem, is proposed to model the rectangular conductors of the busbar system, based on the subdivision of the conductor in filamentary subconductors. This technique is know to lead to a dense, huge inductance matrix, that must be multiplied by the current vector, which limits its practical application. The proposed method replaces this matrix-vector multiplication with a simple element-wise vector product in the spatial frequency domain. The FFT speed makes the proposed method very fast and easy to apply. This approach is theoretically explained and applied to an industrial busbar system.This work was supported in part by the Spanish Ministerio de Ciencia, Innovación y Universidades (MCIU), in part by the Agencia Estatal de Investigación (AEI), and in part by the Fondo Europeo de Desarrollo Regional (FEDER) in the framework of the Proyectos I+D+i - Retos Investigación 2018, under Project Reference RTI2018-102175-B-I00 (MCIU/AEI/FEDER, UE).Martinez-Roman, J.; Puche-Panadero, R.; Sapena-Bano, A.; Burriel-Valencia, J.; Terrón-Santiago, C.; Pineda-Sanchez, M.; Riera-Guasp, M. (2022). Fast Numerical Model of Power Busbar Conductors Through the FFT and the Convolution Theorem. IEEE Transactions on Power Delivery. 37(4):1-11. https://doi.org/10.1109/TPWRD.2021.312626511137

Automatic Fault Diagnostic System for Induction Motors under Transient Regime Optimized with Expert Systems

[EN] Induction machines (IMs) power most modern industrial processes (induction motors) and generate an increasing portion of our electricity (doubly fed induction generators). A continuous monitoring of the machine's condition can identify faults at an early stage, and it can avoid costly, unexpected shutdowns of production processes, with economic losses well beyond the cost of the machine itself. Machine current signature analysis (MCSA), has become a prominent technique for condition-based maintenance, because, in its basic approach, it is non-invasive, requires just a current sensor, and can process the current signal using a standard fast Fourier transform (FFT). Nevertheless, the industrial application of MCSA requires well-trained maintenance personnel, able to interpret the current spectra and to avoid false diagnostics that can appear due to electrical noise in harsh industrial environments. This task faces increasing difficulties, especially when dealing with machines that work under non-stationary conditions, such as wind generators under variable wind regime, or motors fed from variable speed drives. In these cases, the resulting spectra are no longer simple one-dimensional plots in the time domain; instead, they become two-dimensional images in the joint time-frequency domain, requiring highly specialized personnel to evaluate the machine condition. To alleviate these problems, supporting the maintenance staff in their decision process, and simplifying the correct use of fault diagnosis systems, expert systems based on neural networks have been proposed for automatic fault diagnosis. However, all these systems, up to the best knowledge of the authors, operate under steady-state conditions, and are not applicable in a transient regime. To solve this problem, this paper presents an automatic system for generating optimized expert diagnostic systems for fault detection when the machine works under transient conditions. The proposed method is first theoretically introduced, and then it is applied to the experimental diagnosis of broken bars in a commercial cage induction motor.Burriel-Valencia, J.; Puche-Panadero, R.; Martinez-Roman, J.; Sapena-Bano, A.; Pineda-Sanchez, M.; Pérez-Cruz, J.; Riera-Guasp, M. (2019). Automatic Fault Diagnostic System for Induction Motors under Transient Regime Optimized with Expert Systems. Electronics. 8(1):1-16. https://doi.org/10.3390/electronics8010006S11681Puche-Panadero, R., Pineda-Sanchez, M., Riera-Guasp, M., Roger-Folch, J., Hurtado-Perez, E., & Perez-Cruz, J. (2009). Improved Resolution of the MCSA Method Via Hilbert Transform, Enabling the Diagnosis of Rotor Asymmetries at Very Low Slip. IEEE Transactions on Energy Conversion, 24(1), 52-59. doi:10.1109/tec.2008.2003207Abd-el -Malek, M., Abdelsalam, A. K., & Hassan, O. E. (2017). Induction motor broken rotor bar fault location detection through envelope analysis of start-up current using Hilbert transform. Mechanical Systems and Signal Processing, 93, 332-350. doi:10.1016/j.ymssp.2017.02.014Martinez, J., Belahcen, A., & Muetze, A. (2017). Analysis of the Vibration Magnitude of an Induction Motor With Different Numbers of Broken Bars. IEEE Transactions on Industry Applications, 53(3), 2711-2720. doi:10.1109/tia.2017.2657478Sapena-Bano, A., Pineda-Sanchez, M., Puche-Panadero, R., Perez-Cruz, J., Roger-Folch, J., Riera-Guasp, M., & Martinez-Roman, J. (2015). Harmonic Order Tracking Analysis: A Novel Method for Fault Diagnosis in Induction Machines. IEEE Transactions on Energy Conversion, 30(3), 833-841. doi:10.1109/tec.2015.2416973Sapena-Bano, A., Burriel-Valencia, J., Pineda-Sanchez, M., Puche-Panadero, R., & Riera-Guasp, M. (2017). The Harmonic Order Tracking Analysis Method for the Fault Diagnosis in Induction Motors Under Time-Varying Conditions. IEEE Transactions on Energy Conversion, 32(1), 244-256. doi:10.1109/tec.2016.2626008Burriel-Valencia, J., Puche-Panadero, R., Martinez-Roman, J., Sapena-Bano, A., & Pineda-Sanchez, M. (2017). Short-Frequency Fourier Transform for Fault Diagnosis of Induction Machines Working in Transient Regime. IEEE Transactions on Instrumentation and Measurement, 66(3), 432-440. doi:10.1109/tim.2016.2647458Yin, Z., & Hou, J. (2016). Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes. Neurocomputing, 174, 643-650. doi:10.1016/j.neucom.2015.09.081Bazan, G. H., Scalassara, P. R., Endo, W., Goedtel, A., Godoy, W. F., & Palácios, R. H. C. (2017). Stator fault analysis of three-phase induction motors using information measures and artificial neural networks. Electric Power Systems Research, 143, 347-356. doi:10.1016/j.epsr.2016.09.031Mustafidah, H., Hartati, S., Wardoyo, R., & Harjoko, A. (2014). Selection of Most Appropriate Backpropagation Training Algorithm in Data Pattern Recognition. International Journal of Computer Trends and Technology, 14(2), 92-95. doi:10.14445/22312803/ijctt-v14p120Godoy, W. F., da Silva, I. N., Lopes, T. D., Goedtel, A., & Palácios, R. H. C. (2016). Application of intelligent tools to detect and classify broken rotor bars in three-phase induction motors fed by an inverter. IET Electric Power Applications, 10(5), 430-439. doi:10.1049/iet-epa.2015.0469Ghorbanian, V., & Faiz, J. (2015). A survey on time and frequency characteristics of induction motors with broken rotor bars in line-start and inverter-fed modes. Mechanical Systems and Signal Processing, 54-55, 427-456. doi:10.1016/j.ymssp.2014.08.022Valles-Novo, R., de Jesus Rangel-Magdaleno, J., Ramirez-Cortes, J. M., Peregrina-Barreto, H., & Morales-Caporal, R. (2015). Empirical Mode Decomposition Analysis for Broken-Bar Detection on Squirrel Cage Induction Motors. IEEE Transactions on Instrumentation and Measurement, 64(5), 1118-1128. doi:10.1109/tim.2014.2373513De Santiago-Perez, J. J., Rivera-Guillen, J. R., Amezquita-Sanchez, J. P., Valtierra-Rodriguez, M., Romero-Troncoso, R. J., & Dominguez-Gonzalez, A. (2018). Fourier transform and image processing for automatic detection of broken rotor bars in induction motors. Measurement Science and Technology, 29(9), 095008. doi:10.1088/1361-6501/aad3aaMerabet, H., Bahi, T., Drici, D., Halam, N., & Bedoud, K. (2017). Diagnosis of rotor fault using neuro-fuzzy inference system. Journal of Fundamental and Applied Sciences, 9(1), 170. doi:10.4314/jfas.v9i1.12Riera-Guasp, M., Pineda-Sanchez, M., Perez-Cruz, J., Puche-Panadero, R., Roger-Folch, J., & Antonino-Daviu, J. A. (2012). Diagnosis of Induction Motor Faults via Gabor Analysis of the Current in Transient Regime. IEEE Transactions on Instrumentation and Measurement, 61(6), 1583-1596. doi:10.1109/tim.2012.2186650Gyftakis, K. N., Marques Cardoso, A. J., & Antonino-Daviu, J. A. (2017). Introducing the Filtered Park’s and Filtered Extended Park’s Vector Approach to detect broken rotor bars in induction motors independently from the rotor slots number. Mechanical Systems and Signal Processing, 93, 30-50. doi:10.1016/j.ymssp.2017.01.04

Targeted Next-Generation Sequencing in a Large Cohort of Genetically Undiagnosed Patients with Neuromuscular Disorders in Spain

This article belongs to the Special Issue Genetic Advances in Neuromuscular Disorders: From Gene Identification to Gene Therapy.The term neuromuscular disorder (NMD) includes many genetic and acquired diseases and differential diagnosis can be challenging. Next-generation sequencing (NGS) is especially useful in this setting given the large number of possible candidate genes, the clinical, pathological, and genetic heterogeneity, the absence of an established genotype-phenotype correlation, and the exceptionally large size of some causative genes such as TTN, NEB and RYR1. We evaluated the diagnostic value of a custom targeted next-generation sequencing gene panel to study the mutational spectrum of a subset of NMD patients in Spain. In an NMD cohort of 207 patients with congenital myopathies, distal myopathies, congenital and adult-onset muscular dystrophies, and congenital myasthenic syndromes, we detected causative mutations in 102 patients (49.3%), involving 42 NMD-related genes. The most common causative genes, TTN and RYR1, accounted for almost 30% of cases. Thirty-two of the 207 patients (15.4%) carried variants of uncertain significance or had an unidentified second mutation to explain the genetic cause of the disease. In the remaining 73 patients (35.3%), no candidate variant was identified. In combination with patients’ clinical and myopathological data, the custom gene panel designed in our lab proved to be a powerful tool to diagnose patients with myopathies, muscular dystrophies and congenital myasthenic syndromes. Targeted NGS approaches enable a rapid and cost-effective analysis of NMD- related genes, offering reliable results in a short time and relegating invasive techniques to a second tier.This study was granted by FIS PI15/01898, funded by ISCIII and FEDER, ‘Una manera de hacer Europa’ and by Fundación Mutua Madrileña in the “Convocatoria de ayudas a la Investigación en Salud 2015”. It was also funded by an ACCI grant from CIBERER. Daniel Natera-de Benito is the recipient of a grant from the Instituto de Salud Carlos III (Contrato Rio Hortega, CM17/00044)