An ensemble of learning machines for quantitative analysis of bronze alloys

We deal with the determination of the composition of bronze alloys measured through Laser-Induced Breakdown Spectroscopy (LIBS) analysis. The relation between LIBS spectra and bronze alloy composition, represented by means of the concentrations of constituting elements, is modeled by adopting an ensemble of learning machines, fed with different inputs. Then, the combiner computes the final response. The results obtained on the test set show that the ensemble model manages to determine the composition of alloy samples with mean squared error of about 6.53 10^-2

online condition monitoring of bearings for improved reliability in packaging materials industry

The production processes in the packaging materials industry has to be very efficient and cost-effective. These processes usually take place under extreme conditions and high speeds that requires a high level of reliability and efficiency. Rollers including their supporting bearings and motors are the most common components of production machines in the packaging materials industry. Bearing faults, which often occur gradually, represent one of the foremost causes of failures in the industry. Therefore it is very important to take care of bearings during maintenance and detect their faults in an early stage in order to assure safe and efficient operation. We present a new automated technique for early fault detection and diagnosis in rolling-element bearings based on vibration signal analysis. After normalization and the wavelet transform of vibration signals, the standard deviation as a measure of average energy level and the logarithmic energy entropy as a measure of the degree of order/disorder are extracted in a few sub-bands of interest as representative features. Then the feature space dimension is optimally reduced to two using scatter matrices. In the reduced two-dimensional feature space the fault detection is performed by a quadratic classifier and the fault diagnosis by another two quadratic classifiers. Accuracy of the new technique was tested on the ball bearing data recorded at the Case Western Reserve University Bearing Data Center. In total four classes of the vibrations signals were studied, i.e. normal, with the fault of inner race, outer race and balls operation. An overall accuracy of 100% was achieved. The new technique can be used to increase reliability and efficiency by preventing unexpected faulty operation of machinery bearings

Comportement vibratoire de rotors opérant à hautes vitesses et détection des défauts de roulements

L’objectif principal de cette thèse est l’étude du comportement dynamique des broches de machines-outils tournant à hautes vitesses et le diagnostic des roulements qui leurs servent de support. L’analyse dynamique des broches est faite en utilisant un modèle numérique composé d’un rotor et deux paliers de roulements. Elle offre la possibilité de voir l’influence de l’effet gyroscopique survenant à haute vitesse sur les fréquences naturelles du système. Les développements réalisés permettent une meilleure compréhension de la dynamique des broches d’usinage tournant à haute vitesse et permettent de corriger les limites des zones de stabilité des paramètres de coupe, lorsque la machine opère à haute vitesse. Cette modélisation a permis la mise au point d’un simulateur numérique du comportement vibratoire des broches considérant d’un côté les phénomènes non linéaires reliés à la haute vitesse et d’un autre côté, les défauts de roulements. Il est aussi démontré au travers de l’analyse expérimentale que les centres d’usinage à hautes vitesses ne sont pas des machines tournantes conventionnelles et qu’un diagnostic fiable devra intégrer dans le processus de décision non seulement les indicateurs de vibration, mais aussi les conditions de coupe. Dans ce sens, cette thèse présente un outil expérimental original de surveillance des machines-outils combinant la mesure des vibrations couplée avec les paramètres de fonctionnement (positions XYZ, vitesse instantanée, vitesse d’avance, outil en cours, puissance instantanée…) recueillis en temps réel grâce à un protocole de communication avec le contrôleur de la machine. Cette thèse est rédigée dans un format par articles, dans lequel 3 articles y seront présentés. Le premier article, publié dans la revue International Journal of COMADEM présente un nouvel outil de diagnostic automatisé des défauts de roulements, appelé «Envelop Shock Detector (ESD) », destiné à détecter, filtrer et extraire les chocs provoqués par les roulements dans le domaine temporel, puis à les isoler des autres composantes harmoniques et aléatoires. L’ESD est appliqué comme outil de débruitage-filtrage temporel pour éliminer les composantes aléatoires et harmonique du signal vibratoire, et pour distinguer les défauts de roulements de ceux provoqués par les engrenages par le calcul d’un paramètre de glissement. Le même outil pourrait être utilisé comme paramètre d’entrée d’un réseau de neurones pour améliorer ses performances de détection et séparation en cas de défauts multiples provenant d’un même roulement. Le deuxième article publié dans la revue sur l’ingénierie des risques industriels (JI-IRI), présente un simulateur numérique basé sur un modèle tridimensionnel à 20 degrés de liberté incluant l’effet gyroscopique et permettant de simuler le fonctionnement en régime transitoire -montées et descentes en vitesse- des broches de centre d’usinage, en présence de défaut de roulements. Le simulateur permet de déceler l’apparition de nouvelles fréquences critiques, hautement nuisibles pour la qualité d’usinage et surtout pour la durée de vie de l’équipement. Ces nuisances sont dues à la coïncidence entre les fréquences d’excitation et celles dues aux défauts de roulements avec les fréquences naturelles du rotor, en modes de précession avant et arrière, ainsi qu’aux fréquences du variateur de vitesse de la machine-outil. Le troisième article, soumis pour publication dans la revue internationale Machines traite de l’influence de la vitesse de rotation sur les paramètres dynamiques des broches et de la transmissibilité des vibrations provenant d’un roulement défectueux au reste de la broche. De nouvelles fréquences naturelles apparaissent et varient avec la vitesse. Les diagrammes de stabilité, utilisant les fréquences naturelles à l’arrêt, deviennent périmés et ne sont plus représentatifs du comportement vibratoire réel du système mécanique. L’originalité dans cet article provient de l’ajout de l’effet gyroscopique pour tracer de nouveaux lobes de stabilité d’usinage et pour étudier la dérive des fréquences naturelles à haute vitesse. Les théories avancées sont validées grâce à des essais de mesures vibratoires sur la machine-outil Huron opérant à 24,000 tours par minute

Classifier ensembles to improve the robustness to noise of bearing fault diagnosis

In this paper, we perform a noise analysis to assess the degree of robustness to noise of a neural classifier aimed at performing multi-class diagnosis of rolling element bearings. We work on vibration signals collected by means of two accelerometers and we consider ten levels of noise, each of which characterized by a different signal-to-noise ratio ranging from 40. 55 to -11. 35 db. We classify the noisy signals by means of a neural classifier initially trained on signals without noise and then we repeat the training process with signals affected by increasing levels of noise. We show that adding noisy signals to the training set we can significantly increase the classification accuracy of a single classifier. Finally, we apply the two most used strategies to combine classifiers: classifier fusion and classifier selection, and show that, in both cases, we can significantly increase the performance of the single best classifier. In particular, classifier selection achieves the best results for low and medium levels of noise, while classifier fusion is the most accurate for high levels of noise. The analysis presented in the paper can be profitably used to identify both the type of classifier (e. g., single classifier or classifier ensemble) and how many and which noise levels should be used in the training phase in order to achieve the desired classification accuracy in the application domain of interest

Advanced vibration analysis for the diagnosis and prognosis of rotating machinery components within condition-based maintenance programs

Machines used in the industrial field may deteriorate with usage and age. Thus it is important to maintain them so as to avoid failure during actual operation which may be dangerous or even disastrous.The literature has focused its attention on the development of optimal maintenance strategies, such as condition-based maintenance (CBM), in order to improve system reliability, to avoid system failures, and to decrease maintenance costs. CBM aims to detect the early occurrence and seriousness of a fault, to estimate the time interval during which the equipment can still operate before failure, and to identify the components which are deteriorating. CBM has been widely and effectively applied to rotating machines, which usually operate by means of bearings. The reliable and continuous work of bearings is important as the break of one of them can compromise the work of the system. Thus the monitoring, prognosis and diagnosis of bearings represent crucial and important tasks to support real-time maintenance programs. This research has carried out a complete analysis of advanced soft computing techniques ranging from the multi-class classification to one-class classification, and of combination strategies based on classifier fusion and selection. The purpose of this analysis was to design and develop high accurate and high robust methodologies to perform the detection, diagnosis and prognosis of defects on rolling elements bearings. We used vibration signals recorded by four accelerometers on a mechanical device including rolling element bearings: the signals were collected both with all faultless bearings and after substituting one faultless bearing with an artificially damaged one. Four defects and three severity levels were considered. This research has brought to the design and development of new classifiers which have proved to be very accurate and thus to represent a valuable alternative to the traditional classifiers. Besides, the high accuracy and the high robustness to noise, shown by the obtained results, prove the effectiveness of the proposed methodologies, which can be thus profitably used to perform automatic prognosis and diagnosis of rotating machinery components within real-time condition-based maintenance programs

Development of the maintenance improvement model in small and medium enterprises based on principles of total productive maintenance

Small and medium enterprises (SME) represent most efficient part of national economies. Also, this companies are generating high contribution in increase of employment, gross domestic product and trade level and due to that are considered as economy foundation of every country. It is of our interest to understand how small and medium enterprises can be further improved and how to systematically and sustainably generate high quality level of the products by providing low cost production. This thesis has researched different models of SME improvements based on equipment maintenance systems improvements maximising utilization of available resources in SME, having in consideration all financial and labour resource limitations. Research has come to conclusion that is necessary to develop new, simplified, continuous improvement program based on Total productive maintenance (TPM) concept, that is based on equipment maintenance as main pillar for SME success. New model is utilising resources that SME currently has, production employees, by positioning them as leaders in continuous improvement program. In order to promote new development model with owners and managers of SME theses has performed analysess in to predict potential improvements in SME using break-even point analyses. This analysess is based on data received from six Tetra Pak factories in Europe. This analysis is used to predict movement of break-even point based on the length of continuous improvement program application and prediction of potential increase of company profit