A fuzzy diagnosis of multi-fault state based on information fusion from multiple sensors

This paper presents a fuzzy diagnosis for detecting and distinguishing multi-fault state, the method is constructed on the basis of possibility theory and support vector machines (SVMs) with information fusion from multiple sensors. Non-dimensional symptom parameters (NSPs) are defined to reflect the characteristics of vibration information, and principal component analysis (PCA) is used to evaluate and select sensitive NSPs of each sensor. SVMs are employed to fuse vibration information from different sensors into an effective synthetic symptom parameter (SSP) for increasing diagnostic sensitivity, then the possibility function of the SSP is used to construct a fuzzy diagnosis for fault detection and fault-type identification by possibility theory. Practical examples of diagnosis for a roller bearing used in a test bench are given to show that multi-fault states of bearing can be identified precisely by the proposed method

A sensitivity comparison of Neuro-fuzzy feature extraction methods from bearing failure signals

This thesis presents an account of investigations made into building bearing fault classifiers for outer race faults (ORF), inner race faults (IRF), ball faults (BF) and no fault (NF) cases using wavelet transforms, statistical parameter features and Artificial Neuro-Fuzzy Inference Systems (ANFIS). The test results showed that the ball fault (BF) classifier successfully achieved 100% accuracy without mis-classification, while the outer race fault (ORF), inner race fault (IRF) and no fault (NF) classifiers achieved mixed results

Metodologie innovative per il monitoraggio dell'interazione fluido-struttura mediante misure senza contatto

This thesis deals with a particular line of research in the context of a larger research project which aims to develop innovative measurement methodologies for the analysis of fluid-structure interaction, with special reference to monitoring and diagnostic components of the power equipment (turbines, wind turbine blades, etc..). In particular, the objective was focused on the measurement by Thermoelastic Stress Analysis (TSA) of stress on wind turbines fully assembled and in real operating conditions, even moving at high speeds. In a first experimental step, we designed case studies and reproduced in laboratory (wind tunnel) the loading conditions considered to be significant and representative of the real operating conditions of the components. Instrumental acquisitions were performed simultaneously with traditional contact measures and thermographic non-contact measures. Then we developed two innovative solutions for post-processing. First, we developed an algorithm for motion compensation of the thermographic image, by pattern recognition for tracking thermal markers appropriately positioned on the surface of the mechanical component in question, which allows the passage of the acquired thermographic measure from an absolute reference system fixed with respect to the camera to a relative reference system integral with the mechanical component. In this way we got a new sequence of thermographic images "rotated" to be subjected to a second algorithm for the validation and the scaling of the thermographic tensional map of the entire body, through the comparison with the conventional measures simultaneously acquired, which finally allows to get a tensional map quantitatively significant over the entire surface of the body under examination. In this way, using the obtained results, it is possible to apply the thermoelastic analysis (TSA) for the measurement of continuous stress maps on the entire surface of the components in question. So we can overcome the limitations of traditional contact measurements, which are difficult to apply in some operating conditions (components in random motion) and in some geometric areas of components, and that always provide only point measurements