Machinery fault diagnosis by wavelet analysis

Many machines generate nonstationary dynamic signals. The featured components of such signals, such as spikes and transients, are usually localized both in time and in frequency. Since these features often carry rich information about the condition of the machines, enhancing and extracting them are of particular importance in machinery fault diagnosis.Doctor of Philosophy (MPE