No-estacionariedad, multifractalidad y limpieza de ruido en señales reales

Las señales biomédicas, como el electrocardiograma, el electroencefalograma, o la señal de voz, tienen en común características de no estacionariedad y no linealidad. Aunque enmuchas aplicaciones se considera que se trata de señales estacionarias procedentes de sistemas lineales, ésta simplificación constituye una hipótesis de trabajo válida sólo como una aproximación que permite la aplicación de técnicas clásicas deanálisis de señales. Muchos trastornos que afectan a uno o varios órganos pueden ser detectados a través de un correcto análisis de las señales en cuya producción están involucrados. Sin embargo, debe atenderse al hecho de que una señal procedente de un sistema patológico se aleja aún más de las condiciones hipotéticas de estacionariedad y linealidad. Se desprende de esta circunstancia la necesidad de abordar el análisis de las señales biomédicas mediante técnicas no convencionales que permitan su tratamiento en un marco que tenga en cuenta sus características de no estacionariedad y no linealidad. Sobre la base de la experiencia del grupo de trabajo en las áreas del análisis tiempo-frecuencia/escala, análisis y modelado estadístico, análisis multifractal, complejidad y métodos guiados por los datos (adaptativos), a partir de problemas reales se han propuesto y estudiado nuevas técnicas que posibiliten su solución

Zur Reduzierung des mehrwegebedingten GNSS-Trägerphasenmessfehlers durch Anwendung der Hilbert-Huang-Transformation auf Signalqualitätsparameter

Die Positionsbestimmung von Verkehrsteilnehmern basiert häufig auf der Auswertung von Signalen globaler Navigationssatellitensysteme (GNSS). Dabei werden die Laufzeiten der Signale und darauf beruhend die Entfernungen zwischen den Satelliten und dem Verkehrsteilnehmer ermittelt. Die Positionsbestimmung erfolgt dann nach dem Prinzip der Trilateration. Bei hohen Anforderungen an die Genauigkeit der Position wird hierfür insbesondere die Phase des Trägersignals ausgewertet. Eine besondere Herausforderung stellt dabei die Mehrwegeausbreitung der Signale dar. Hervorgerufen wird diese durch Objekte, wie Bäume, Gebäude oder Fahrzeuge in der Umgebung des Verkehrsteilnehmers. Durch die Mehrwegeausbreitung werden die Laufzeiten der Signale und somit die Position fehlerhaft bestimmt. Es ist daher von großer Bedeutung, die mehrwegebedingten Fehleranteile zu detektieren und sie zu reduzieren. In dieser Arbeit wird dafür der Zusammenhang zwischen dem Trägerphasenmessfehler und der Signalqualität genutzt. Durch Anwendung einer im Rahmen dieser Arbeit entwickelten adaptierten Hilbert-Huang-Transformation auf die aus dem Signalqualitätsparameter des Signal-zu-Rauschleistungsdichte-Verhältnis abgeleiteten Signalamplituden können Mehrwegesignale detektiert und der durch sie verursachte Trägerphasenmessfehler berechnet werden. Anhand der Auswertung eines Experimentaldatensatzes sowie Daten von GNSS-Referenzstationen des SAPOS-Netzes kann der Erfolg des Einsatzes der adaptierten Hilbert-Huang-Transformation nachgewiesen werden

Condition Monitoring and Fault Diagnosis of a Multi-Stage Gear Transmission Using Vibro-acoustic Signals

Gearbox condition monitoring(CM) plays a vital role in ensuring the reliability and operational efficiency of a wide range of industrial facilities such as wind turbines and helicopters. Many technologies have been investigated intensively for more accurate CM of rotating machines with using vibro-acoustic signature analysis. However, a comparison of CM performances between surface vibrations and airborne acoustics has not been carried out with the use of emerging signal processing techniques. This research has focused on a symmetric evaluation of CM performances using vibrations obtained from the surface of a multi stage gearbox housing and the airborne sound obtained remotely but close to the gearbox, in conjunction with state of the art signal processing techniques, in order to provide efficient and effective CM for gear transmissions subject to gradual and progressive deteriorations. By completing the comparative studies, this research has resulted in a number of new findings that show significant contributions to knowledge which are detailed as follows. In general, through a comprehensive review of the advancement in the subject, the research has been carried out by integrating an improved dynamic modelling, more realistic experiment verification and more advanced signal processing approaches. The improved modelling has led to an in-depth understanding of the nonlinear modulation in vibro-acoustic signals due to wear effects. Thereafter, Time Synchronous Average (TSA) and Modulation Signal Bispectrum (MSB) are identified to be the most promising signal processing methods to fulfil the evaluation because of their unique properties of simultaneous noise reduction and modulation enhancement. The more realistic tests have demonstrated that arun-to-failure test is necessary to develop effective diagnostic tools as it produces datasets from gear transmissions where deterioration naturally progresses over a long operation, rather than faults created artificially to gear systems, as is common in the majority of studies and the results unreliable. Particularly, the evaluation studies have clarified a number of key issues in the realisation of gearbox diagnostics based on TSA and MSB analysis of the vibrations from two accelerometers and acoustics from two microphones in monitoring the run-to-failure process, which showed slight gear wear of two back-to-back multiple stage helical gearboxes under variable load and speed operations. TSA analysis of vibration signals and acoustic signals allows for accurate monitoring and diagnosis results of the gradual deterioration in the lower speed transmission of both the tested gearboxes. However, it cannot give the correct indication of the higher speed stages in the second gearbox as the reference angle signal is too erroneous due to the distortion of long transmission trains. In addition, acoustic signals can indicate that there is a small determination in the higher speed transmission of the first gearbox. The MSB analysis of vibration signals and sound signals allows for the gathering of more corrective monitoring and diagnostic results of the deterioration in the four stages of transmissions of the two tested gearboxes. MSB magnitudes of both the two lower speed transmissions show monotonic increases with operational time and the increments over a longer period are in excess of three times higher than the baselines, the deteriorations are therefore regarded as severe. For the two higher speed transmissions, the MSB of vibrations and acoustics illustrates small deteriorations in the latter operating hours. Comparatively, acoustic signal based diagnostics can out-perform vibration as it can provide an early indication of deteriorations and correct diagnosis of the faults as microphones perceive a large area of dynamic responses from gearbox housing whereas accelerometers collect a very localised response which can be distorted by transmission paths. In addition, MSB analysis can out-perform conventional TSA as it maintains all diagnostic information regarding the rotating systems and can be implemented without any additional reference channels