OF THE THESIS Damage Detection in Dynamic Systems with Nonlinearities by William Bruce Dunbar Master of Science in Engineering Sciences (Applied Mechanics) University of California, San Diego, 1999 Professor John B. Kosmatka, Chair In general, damage detection can be described by the process of monitoring a physical dynamic (moving) system accompanied by con rmation and assessment of any degrad...

Quality control of textile products is an important stage in textile industries. To this end, the conventional method in fault detection is human inspection. In the present work, Wavelet transform was applied on images of simple circular knitted fabrics to diagnose five regular defects. The results showed that the method applied was accurate and fast in addition to being capable of determining ...

This paper deals with the early detection and intelligent diagnosis of mechanical faults in industrial turning machines. We develop a new strategy hybridizing conventional signal analysis based techniques and artificial neural networks issued methods. Experimental results, obtained from a real experimental industrial plant, validating the proposed strategy and issued intelligent fault detection...

Current spectrum analysis is a proven technique for fault diagnosis in electrical machines. Current spectral estimation is usually performed using classical techniques such as periodogram (FFT) or its extensions. However, these techniques have several drawbacks since their frequency resolution is limited and additional post-processing algorithms are required to extract a relevant fault detectio...

The railway assets studied in this project, are those widely distributed pieces of equipment that are critical to the dependable operation of the railway system. A failed asset is likely to cause significant delay to rail services, and may even place the system into an unsafe state. A generic fault detection and diagnosis (FDD) solution for a number of railway assets of different types is there...

in this study, detection and identification of common faults in industrial gas turbines is investigated. we propose a model-based robust fault detection(fd) method based on multiple models. for residual generation a bank of local linear neuro-fuzzy (llnf) models is used. moreover, in fault detection step, a passive approach based on adaptive threshold is employed. to achieve this purpose, the a...