Despite their reliability, induction motors tend to fail. Around 41% of faults in motors are bearing related and that is the most common fault in motor field. Due to the lack of research on generalized roughness bearing fault diagnostics by use of a stator current spectrum, the presented study analyses both single-point and generalized roughness bearing faults and their classification possibilities. In this paper, a new method for generalized roughness ball bearing fault identification by use of a stator current signal analysis is presented. The algorithm relies on Discrete Wavelet Transform and Welch's spectral density analysis. The composition of both methods is used for building a feature vector for the classifier. In order to achieve classification, support vector machine classifier with linear kernel function has been applied. The validation experiment and results are presented.
A prototype of ionic wind fan is presented. Modelling using numerical methods was performed. The physical model was created. The calculated theoretical results were compared with measurement results in physical model. Overall efficiency of the system was calculated. Digital simulation model was created using "COMSOL Multiphysics". The air flow measurement uncertainties due to instrumentation were discussed. The air flow model in tube was investigated and airflow measurement incarnates were removed.
The article presents the capabilities of a new fault identification method used for different fault levels. The method allows identifying unbalanced induction motor by only using stator current signals. The signal analysis was done by using wavelet packet decomposition and reconstruction (WPDR) and information entropy methods. The validation of proposed method was carried out by comparing unbalanced fault progressive simulation and experimentally obtained results. The experimental results were also analysed to identify the most band of frequencies (node) for the proposed method. Signals were divided into five overlapped time intervals in order to investigate which interval is the most informative for fault diagnosis.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.