Development of algorithms of statistical signal processing for the detection and pattern recognitionin time series. Application to the diagnosis of electrical machines and to the features extraction in Actigraphy signals

Tesis por compendio[ES] En la actualidad, el desarrollo y aplicación de algoritmos para el reconocimiento de patrones que mejoren los niveles de rendimiento, detección y procesamiento de datos en diferentes áreas del conocimiento resulta un tema de gran interés. En este contexto, y específicamente en relación con la aplicación de estos algoritmos en el monitoreo y diagnóstico de máquinas eléctricas, el uso de señales de flujo es una alternativa muy interesante para detectar las diferentes fallas. Asimismo, y en relación con el uso de señales biomédicas, es de gran interés extraer características relevantes en las señales de actigrafía para la identificación de patrones que pueden estar asociados con una patología específica. En esta tesis, se han desarrollado y aplicado algoritmos basados en el procesamiento estadístico y espectral de señales, para la detección y diagnóstico de fallas en máquinas eléctricas, así como su aplicación al tratamiento de señales de actigrafía. Con el desarrollo de los algoritmos propuestos, se pretende tener un sistema dinámico de indicación e identificación para detectar la falla o la patología asociada que no depende de parámetros o información externa que pueda condicionar los resultados, sólo de la información primaria que inicialmente presenta la señal a tratar (como la periodicidad, amplitud, frecuencia y fase de la muestra). A partir del uso de los algoritmos desarrollados para la detección y diagnóstico de fallas en máquinas eléctricas, basados en el procesamiento estadístico y espectral de señales, se pretende avanzar, en relación con los modelos actualmente existentes, en la identificación de fallas mediante el uso de señales de flujo. Además, y por otro lado, mediante el uso de estadísticas de orden superior, para la extracción de anomalías en las señales de actigrafía, se han encontrado parámetros alternativos para la identificación de procesos que pueden estar relacionados con patologías específicas.[CA] En l'actualitat, el desenvolupament i aplicació d'algoritmes per al reconeixement de patrons que milloren els nivells de rendiment, detecció i processament de dades en diferents àrees del coneixement és un tema de gran interés. En aquest context, i específicament en relació amb l'aplicació d'aquests algoritmes a la monitorització i diagnòstic de màquines elèctriques, l'ús de senyals de flux és una alternativa molt interessant per tal de detectar les diferents avaries. Així mateix, i en relació amb l'ús de senyals biomèdics, és de gran interés extraure característiques rellevants en els senyals d'actigrafia per a la identificació de patrons que poden estar associats amb una patologia específica. En aquesta tesi, s'han desenvolupat i aplicat algoritmes basats en el processament estadístic i espectral de senyals per a la detecció i diagnòstic d'avaries en màquines elèctriques, així com la seua aplicació al tractament de senyals d'actigrafia. Amb el desenvolupament dels algoritmes proposats, es pretén obtindre un sistema dinàmic d'indicació i identificació per a detectar l'avaria o la patologia associada, el qual no depenga de paràmetres o informació externa que puga condicionar els resultats, només de la informació primària que inicialment presenta el senyal a tractar (com la periodicitat, amplitud, freqüència i fase de la mostra). A partir de l'ús dels algoritmes desenvolupats per a la detecció i diagnòstic d'avaries en màquines elèctriques, basats en el processament estadístic i espectral de senyals, es pretén avançar, en relació amb els models actualment existents, en la identificació de avaries mitjançant l'ús de senyals de flux. A més, i d'altra banda, mitjançant l'ús d'estadístics d'ordre superior, per a l'extracció d'anomalies en els senyals d'actigrafía, s'han trobat paràmetres alternatius per a la identificació de processos que poden estar relacionats amb patologies específiques.[EN] Nowadays, the development and application of algorithms for pattern recognition that improve the levels of performance, detection and data processing in different areas of knowledge is a topic of great interest. In this context, and specifically in relation to the application of these algorithms to the monitoring and diagnosis of electrical machines, the use of stray flux signals is a very interesting alternative to detect the different faults. Likewise, and in relation to the use of biomedical signals, it is of great interest to extract relevant features in actigraphy signals for the identification of patterns that may be associated with a specific pathology. In this thesis, algorithms based on statistical and spectral signal processing have been developed and applied to the detection and diagnosis of failures in electrical machines, as well as to the treatment of actigraphy signals. With the development of the proposed algorithms, it is intended to have a dynamic indication and identification system for detecting the failure or associated pathology that does not depend on parameters or external information that may condition the results, but only rely on the primary information that initially presents the signal to be treated (such as the periodicity, amplitude, frequency and phase of the sample). From the use of the algorithms developed for the detection and diagnosis of failures in electrical machines, based on the statistical and spectral signal processing, it is intended to advance, in relation to the models currently existing, in the identification of failures through the use of stray flux signals. In addition, and on the other hand, through the use of higher order statistics for the extraction of anomalies in actigraphy signals, alternative parameters have been found for the identification of processes that may be related to specific pathologies.Iglesias Martínez, ME. (2020). Development of algorithms of statistical signal processing for the detection and pattern recognitionin time series. Application to the diagnosis of electrical machines and to the features extraction in Actigraphy signals [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/145603TESISCompendi

Detection of nonadjacent rotor faults in induction motors via spectral subtraction and autocorrelation of stray flux signals

(c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] In this paper, statistical signal processing techniques are applied to electromotive force signals captured in external coil sensors for adjacent and nonadjacent broken bars detection in induction motors. An algorithm based on spectral subtraction analysis is applied for broken bar identification, independent of the relative position of the bar breakages. Moreover, power spectrum analyses enable the discrimination between healthy and faulty conditions. The results obtained with experimental data prove that the proposed approach provides good results for fault detectability. Moreover, the identification of the faults, and the signal correlation indicator to prove the results are also presented for different positions of the flux sensor.This work was supported in part by MEC under Project MTM 2016-7963-P and in part by the Spanish 'Ministerio de Ciencia Innovacion y Universidades' and FEDER program in the framework of the 'Proyectos de I+D de Generacion de Conocimiento del Programa Estatal de Generacion de Conocimiento y Fortalecimiento Cientifico y Tecnologico del Sistema de I+D+i, Subprograma Estatal de Generacion de Conocimiento' (ref: PGC2018-095747-B-I00).Iglesias-Martínez, ME.; Fernández De Córdoba, P.; Antonino Daviu, JA.; Conejero, JA. (2019). Detection of nonadjacent rotor faults in induction motors via spectral subtraction and autocorrelation of stray flux signals. IEEE Transactions on Industry Applications. 55(5):4585-4594. https://doi.org/10.1109/TIA.2019.2917861S4585459455

Automatic Classification of Winding Asymmetries in Wound Rotor Induction Motors based on Bicoherence and Fuzzy C-Means Algorithms of Stray Flux Signals

(c) 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] Wound rotor induction motors are used in a certain number of industrial applications due to their interesting advantages, such as the possibility of inserting external rheostats in series with the rotor winding to enhance the torque characteristics under starting and to decrease the high inrush currents. However, the more complex structure of the rotor winding, compared to cage induction motors, is a source for potential maintenance problems. In this regard, several anomalies can lead to the occurrence of asymmetries in the rotor winding that may yield terrible repercussions for the machine¿s integrity. Therefore, monitoring the levels of asymmetry in the rotor winding is of paramount importance to ensure the correct operation of the motor. This work proposes the use of Bicoherence of the stray flux signal, as an indicator to obtain an automatic classification of the rotor winding condition. For this, the Fuzzy C-Means machine learning algorithm is used, which starts with the Bicoherence calculation and generates the different clusters for grouping and classification, according to the level of winding asymmetry. In addition, an analysis regarding the influence of the flux sensor position on the automatic classification and the failure detection is carried out. The results are highly satisfactory and prove the potential of the method for its future incorporation in autonomous condition monitoring systems that can be satisfactorily applied to determine the health of these machines.This work was supported in part by Generalitat Valenciana, Conselleria de Innovacion, Universidades, Ciencia y Sociedad Digital, (project AICO/019/224) and in part by MEC under Project MTM2016-75963-P.Iglesias Martínez, ME.; Antonino-Daviu, JA.; Fernández De Córdoba, P.; Conejero, JA.; Dunai, L. (2021). Automatic Classification of Winding Asymmetries in Wound Rotor Induction Motors based on Bicoherence and Fuzzy C-Means Algorithms of Stray Flux Signals. IEEE Transactions on Industry Applications. 57(6):5876-5886. https://doi.org/10.1109/TIA.2021.3108413S5876588657

Machinery Failure Approach and Spectral Analysis to Study the Reaction Time Dynamics over Consecutive Visual Stimuli: An Entropy-Based Model.

The reaction times of individuals over consecutive visual stimuli have been studied using an entropy-based model and a failure machinery approach. The used tools include the fast Fourier transform and a spectral entropy analysis. The results indicate that the reaction times produced by the independently responding individuals to visual stimuli appear to be correlated. The spectral analysis and the entropy of the spectrum yield that there are features of similarity in the response times of each participant and among them. Furthermore, the analysis of the mistakes made by the participants during the reaction time experiments concluded that they follow a behavior which is consistent with the MTBF (Mean Time Between Failures) model, widely used in industry for the predictive diagnosis of electrical machines and equipment

Indoor air quality analysis using recurrent neural networks: a case study of environmental variables

In the pursuit of energy efficiency and reduced environmental impact, adequate ventilation in enclosed spaces is essential. This study presents a hybrid neural network model designed for monitoring and prediction of environmental variables. The system comprises two phases: An IoT hardware–software platform for data acquisition and decision-making and a hybrid model combining short-term memory and convolutional recurrent structures. The results are promising and hold potential for integration into parallel processing AI architectures

The ballast pick-up problem. A theoretical approach and two experimental campaigns

The aim of this contribution is to present a theoretical approach and two experimental campaigns (on wind tunnel and on the track) concerning the research work about the ballast train-induced-wind erosion (BTIWE) phenomenon. When a high speed train overpasses the critical speed, it produces a wind speed close to the track large enough to start the motion of the ballast elements, eventually leading to the rolling of the stones (Kwon and Park, 2006) and, if these stones get enough energy, they can jump and then initiate a saltation-like chain reaction, as found in the saltation processes of soil eolian erosion (Bagnold, 1941). The expelled stones can reach a height which is larger than the lowest parts of the train, striking them (and the track surroundings) producing considerable damage that should be avoided. There is not much published work about this phenomenon, in spite of the great interest that exists due to its relevant applications in increasing the maximum operative train speed. Particularly, the initiation of flight of ballast due to the pass of a high speed train has been studied by Kwon and Park (2006) by performing field and wind tunnel experiments

Spectral analysis of anomalous capacitance measurements in interleaving structures: study of frequency distribution in photomultipliers

This study presents experimental results on capacitance fluctuations in several devices located within an interleaving structure. Specifically, it examines the behavior of the capacitance between the anode and cathode of a photomultiplier, comparing it with the characteristics of the ultra-stable capacitor analyzed in via measurements inside and outside a modified Faraday cage. The results cover spectral and correlation analyses both inside and outside the box, confirming differences in the spectrum using the periodograms. In particular, the confidence intervals for the mean capacitance values show significant changes between the two scenarios, from the inside to the outside of the enclosure. In the case of the ultra-stable capacitor, there is an increase from 0.004 to 0.008 nF. On the other hand, a symmetry analysis is conducted for all measurements taken both outside and inside the modified Faraday cage. It is observed that in all cases, there is clear non-symmetric behavior in the data.Agencia Estatal de Investigación | Ref. PID2021-128676OB-I00Agencia Estatal de Investigación | Ref. PID2022-142407NB-I00Generalitat Valenciana | Ref. CIPROM/2022/3

Phase I study of plitidepsin in combination with bortezomib and dexamethasone in patients with relapsed/refractory multiple myeloma

Previous studies showed antitumor activity for plitidepsin plus dexamethasone (DXM) in relapsed/refractory multiple myeloma (r/r MM), and in vitro synergism with bortezomib (BTZ) or DXM against MM cells. This phase I trial evaluated plitidepsin (3-h intravenous infusion Day 1 and 15), BTZ (subcutaneous bolus Day 1, 4, 8, and 11), and DXM (orally Day 1, 8, 15, and 22), every 4 weeks in 36 r/r MM patients. Twenty-two patients were treated using a standard dose escalation design (10 at the recommended dose [RD] cohort), and 14 additional patients were treated to expand the RD cohort. No dose-limiting toxicities (DLTs) occurred during dose escalation. The highest dose level evaluated (plitidepsin 5.0 mg/m2 , BTZ 1.3 mg/m2 , DXM 40.0 mg) was the RD for phase II studies. Results shown herein are focused on this RD. Two patients had DLTs (grade 3 diarrhea, and grade 3 nausea/vomiting refractory to antiemetic therapy). Grade ? 3 hematological toxicity (thrombocytopenia 46%, anemia 33%, and neutropenia 17%) was manageable and did not result in treatment discontinuation. Transient and manageable grade 3 ALT increase (26%) was the most common biochemical abnormality. At the RD cohort, overall response rate was 22.2% (95%CI, 6.4%-47.6%), including one stringent complete response, one very good partial response, and two partial responses in r/r patients to BTZ and/or lenalidomide. The clinical benefit rate was 77.8% (95%CI, 52.4-93.6%). No major pharmacokinetic drug-drug interaction was found. In conclusion, the triple combination of plitidepsin, BTZ, and DXM showed an acceptable safety profile and had moderate activity in adult patients with r/r MM.FUNDING: The study was funded by Pharma Mar, S.A. ACKNOWLEDGMENTS: The authors thank the patients, their families, and investigators teams for their participation in this phase I clinical trial

Exposición de aparatos de medida de los laboratorios de Física

Memoria ID-0227. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2014-2015