Thesis (MScIng)--University of Stellenbosch, 2007. / Bibliography. Also available via the Internet.

Vibration signals generated from spalled elements in rolling element bearings (REBs) are investigated in this thesis. A novel signal-processing algorithm to diagnose localized faults in rolling element bearings has been developed and tested on a variety of signals. The algorithm is based on Spectral Kurtosis (SK), which has special qualities for detecting REBs faults. The algorithm includes three steps. It starts by pre-whitening the signal's power spectral density using an autoregressive (AR) model. The impulses, which are contained in the residual of the AR model, are then enhanced using the minimum entropy deconvolution (MED) technique, which effectively deconvolves the effect of the transmission path and clarifies the impulses. Finally the output of the MED filter is decomposed using complex Morlet wavelets and the SK is calculated to select the best filter for the envelope analysis. Results show the superiority of the developed algorithm and its effectiveness in extracting fault features from the raw vibration signal. The problem of modelling the vibration signals from a spalled bearing in a gearbox environment is discussed. This problem has been addressed through the incorporation of a time varying, non-linear stiffness bearing model into a previously developed gear model. It has the new capacity of modeling localized faults and extended faults in the different components of the bearing. The simulated signals were found to have the same basic characteristics as measured signals, and moreover were found to have a characteristic seen in the measured signals, and also referred to in the literature, of double pulses corresponding to entry into and exit from a localized fault, which could be made more evident by the MED technique. The simulation model is useful for producing typical fault signals from gearboxes to test new diagnostic algorithms, and also prognostic algorithms. The thesis provides two main tools (SK algorithm and the gear bearing simulation model), which could be effectively employed to develop a successful prognostic model.

Vibration signals generated from spalled elements in rolling element bearings (REBs) are investigated in this thesis. A novel signal-processing algorithm to diagnose localized faults in rolling element bearings has been developed and tested on a variety of signals. The algorithm is based on Spectral Kurtosis (SK), which has special qualities for detecting REBs faults. The algorithm includes three steps. It starts by pre-whitening the signal's power spectral density using an autoregressive (AR) model. The impulses, which are contained in the residual of the AR model, are then enhanced using the minimum entropy deconvolution (MED) technique, which effectively deconvolves the effect of the transmission path and clarifies the impulses. Finally the output of the MED filter is decomposed using complex Morlet wavelets and the SK is calculated to select the best filter for the envelope analysis. Results show the superiority of the developed algorithm and its effectiveness in extracting fault features from the raw vibration signal. The problem of modelling the vibration signals from a spalled bearing in a gearbox environment is discussed. This problem has been addressed through the incorporation of a time varying, non-linear stiffness bearing model into a previously developed gear model. It has the new capacity of modeling localized faults and extended faults in the different components of the bearing. The simulated signals were found to have the same basic characteristics as measured signals, and moreover were found to have a characteristic seen in the measured signals, and also referred to in the literature, of double pulses corresponding to entry into and exit from a localized fault, which could be made more evident by the MED technique. The simulation model is useful for producing typical fault signals from gearboxes to test new diagnostic algorithms, and also prognostic algorithms. The thesis provides two main tools (SK algorithm and the gear bearing simulation model), which could be effectively employed to develop a successful prognostic model.

Thesis. (M. Tech. (Mechanical Engineering))--Vaal University of Technology / Bearing condition monitoring and fault diagnosis have been studied for many years. Popular techniques are applied through advanced signal processing and pattern recognition technologies. The subject of the research was vibration condition monitoring of incipient damage in rolling element bearings. The research was confined to deep-groove ball bearings because of their common applications in industry. The aim of the research was to apply neural networks to vibration condition monitoring of rolling element bearings. Kohonen's Self-Organising Feature Map is the neural network that was used to enable an automatic condition monitoring system. Bearing vibration is induced during bearing operation and the main cause is bearing friction, which ultimately causes wear and incipient spalling in a rolling element bearing. To obtain rolling element bearing vibrations a condition monitoring test rig for rolling element bearings had to be designed and built. A digital vibration measurement acquisition environment was created in Labview and Matlab. Data from the bearing test rig was recorded with a piezoelectric accelerometer, and an S-type load cell connected to dynamic signal analysis cards. The vibration measurement instrumentation was cost-effective and yielded accurate and repeatable measurements. Defects on rolling element bearings were artificially inflicted so that a pattern of bearing defects could be established. An input data format of vibration statistical parameters was created using the time and frequency domain signals. Kohonen's Self-Organising Feature Maps were trained in the input data, utilising an unsupervised, competitive learning algorithm and vector quantisation to cluster the bearing defects on a two-dimensional topographical map. A new practical dimension to condition monitoring of rolling element bearings was developed. The use of time domain and frequency domain analysis of bearing vibration has been combined with a visual and classification analysis of distinct bearing defects through the application of the Self-Organising Feature Map. This is a suitable technique for rolling element bearing defect detection, remaining bearing life estimation and to assist in planning maintenance schedules. / National Research Foundation; Council for Scientific and Industrial Research