Characteristic extraction of rolling bearing compound faults of aero-engine

Rolling bearing’s fault mode usually shows compound faults in aero-engine. The compound faults characteristics are more complex than single one, and many signal analysis methods have rather great limitation for compound fault characteristic extraction which leads to the difficulty to monitor the running state of rolling bearing in aero-engine. Based on above analysis, a method of combining wavelet transform with cyclostationary theory, autocorrelation function and Hilbert transform is proposed and applied to extract characteristic frequency of rolling bearing from compound faults mode only according to single-channel vibration acceleration signal of aero-engine. Meanwhile, a consideration is given to the influence of sensor installation position, compound fault types in the extraction of compound faults characteristics. The result indicates that the proposed new method can effectively monitor rolling bearing running state in four different compound fault modes just according to single-channel vibration acceleration signal no matter sensors are installed in horizontal or vertical direction

Casing vibration signal characteristic extractions and applications in rolling bearing

A method of combining autocorrelation function with cyclostationary theory and Hilbert envelope analysis is proposed and applied to extract characteristic frequency of rolling bearing. Meanwhile, mean power ratio is calculated and used to identify the fault types of aero-engine rolling bearing based on single-channel casing vibration signal. To verify the effectiveness of proposed method, a comparing analysis is carried out between traditional studies and proposed new method. Furthermore, the influences on the extraction of characteristics and calculation of mean power ratio are taken into account, including the ones of sensor installation position, fault types, type of experiment rigs, failure mode and rotational speed of rolling bearing. The result shows that the proposed method can diagnose running conditions and identify fault types of rolling bearing accurately and effectively just by single-channel casing vibration signal

Blind separation of convolved cyclostationary processes

International audienc

Surveillance des centres d'usinage à grande vitesse par approche cyclostationnaire et vitesse instantanée

La surveillance des centres d'usinage à grande vitesse, est un facteur clé pour accroître la disponibilité des machines. Cette mesure permet d'atteindre un niveau de robustesse des processus d'usinage plus élevé. Les défaillances dans le processus d'usinage et les composants des machines-outils peuvent générer des effets négatifs sur la finition du produit et l'instabilité du processus d'usinage. C'est le contexte dans lequel s'inscrit ce travail de recherche. Notre premier objectif est d'étudier les apports de la cyclostationnarité au diagnostic vibratoire des centres d'usinage à grande vitesse. Le deuxième objectif est d'explorer la possibilité de détection du broutage en se basant sur l'analyse des signaux de vitesse angulaire instantanée. Ce type de signal est calculé à partir du signal délivré par le codeur interne monté sur la broche. Développer une nouvelle procédure de détection du broutage dans les machines à grande vitesse, représente le troisième objectif de cette thèse. Cette procédure combine les techniques de traitement du signal et les techniques d'intelligence artificielle

Evaluation of audio source separation in the context of 3D audio

The emergence and broader availability of 3D audio systems allows for new possibilities in mixing, post-production and playback of audio content. Used in movie post-production for cinemas, as special effect by disk jockeys for example and even for live concerts, 3D rendering immerses the listener more than ever before. When existing audio material is to be employed, Audio Source Separation (ASS) techniques enable the extraction of single sources from a mixture. Modern mixing approaches for 3D audio do not assign individual gains and delays for each source in every channel. A sound scene is rather designed, with individual sources treated as objects to be placed within a scene. The hardware layer is mostly irrelevant for mixing in such a setting. ASS is therefore a valuable tool to ¿disassemble¿ amore traditional monophonic, stereophonic, or multichannel mix. However, due to the complexity of the ASS problem, extracted sources are subject to degradations. While state-of-the-art objective measures for ASS quality build on monaural auditory models, they don¿t take into account binaural listening and the psychoacoustic phenomena that are involved, such as binaural unmasking. In this thesis, an extension to Perceptive Evaluation Methods for Audio Source Separation (PEASS) [41] is proposed with spatial rendering in mind. Additionally a new binaural model for ASS evaluation in the context of 3D audio is presented. The performance of the basic and extended versions of PEASS, as well as the proposed binaural model is evaluated in two subjective studies. The first study is conducted with binaural spatialisation presented over headphones, while the second experiment uses a 3DWave Field Synthesis (WFS) system. A set of artificial ASS degradation algorithms is proposed and used for the stimuli of the subjective studies. Results of the studies indicate monotonic decrease of the perceived quality as a function of the amounts of degradations introduced. The most important degradation is found to be target distortion, followed by onset misallocation and musical noise-type artifacts. Additionally, spatialising the extracted target source away from the residue or having it louder than the residue negatively affects the results, indicating a perceived quality degradation. In 3D WFS conditions, results show evidence for monaural and binaural unmasking. The performance of the proposed binauralmodel is consistently superior to that of the basic or extended PEASS versions. In the binaural spatialisation experiment, a correlation coefficient of 0.60 between subjective and objective results is achieved, versus 0.57 and 0.53 with the extended and basic PEASS version respectively. For the 3D WFS study, the binaural model achieves 0.67 prediction accuracy whereas both PEASS versions get 0.57. The perceptual validity of the WFS formulation is also verified in a localisation experiment. Vertical localisation is found to be nearly as good as physical source localisation for an extended listening area with localisation precision of 6± - 9±. The response time is also used as an indicator of localisation performance