On the use of the stepped isostress method in the prediction of creep behavior of polyamide 6

The stepped isostress method (SSM) is an advanced technique which allows the prediction of the long-term behavior and enables the construction of creep master curves of materials with short-term experimental tests. However, the performance of this method is highly dependent on the numerical model and the time spent in data processing. In this paper, the effect of the extrapolation techniques on the creep curves trend is investigated using the SSM data of Polyamide test. Three extrapolation functions are used to offset the delay of the stress history: polynomial, power and exponential functions. Furthermore, a numerical routine is developed during the last step of the SSM, where the shift factors are computed taking into account the rescaling and the dwell times of each level of stresses. The processing of the SSM raw data has revealed that the rescaling parameters are the most determining factors to reach an accurate long-term creep curves. The rescaling process has shown an appropriate time, whether achieved by the exponential or power functions. Larger shift factors for exponential functions are assessed and therefore a long period of creep master curve was obtained

Detection and diagnosis of fault bearing using wavelet packet transform and neural network

Bearings, considered crucial components in rotating machinery, are widely used in the industry. Bearing status monitoring has become an essential step in the deployment of preventive maintenance policy. This work is part of the diagnosis and classification of bearing defects by vibration analysis of signals from defective bearings using time domain and frequency analysis and wavelet packet transformations (Wavelet Packet Transform WPT) with Artificial Neural Networks (ANN). WPT is used for extracting defect indicators to train the neural classifier. The main goal is the determination of the wavelet generating the most representative indicators of the state of the bearings for better detection and classification of defects. Using the WPT-based neural classifier, the obtained simulation results showed that the db6 wavelet with level 3 decomposition is best suited for diagnosing and classifying bearing defect