In this study some new concepts regarding certain aspects related to shape memory polymers are presented. A blend of polylactic acid (PLA) (80%) and polybutylene succinate (PBS) (20%) was prepared first by extrusion, then by injection molding to obtain the samples. Tensile, stress-relaxation and recovery tests were performed on these samples at 70 °C. The results indicated that the blend can only regain 24% of its initial shape. It was shown that, this partial shape memory effect could be improved by successive cycles of shape memory tests. After a fourth cycle, the blend is able to regain 82% of its shape. These original results indicated that a polymer without (or with partial) shape memory effect may be transformed into a shape memory polymer without any chemical modification. In this work, we have also shown the relationship between shape memory and property memory effect. Mono and multi-frequency DMA (dynamic mechanical analyzer) tests on virgin and 100% recovered samples of polyurethane (PU) revealed that the polymer at the end of the shape memory tests regains 100% of its initial form without regaining some of its physical properties like glass transition temperature, tensile modulus, heat expansion coefficient and free volume fraction. Shape memory (with and without stress-relaxation) tests were performed on the samples in order to show the role of residual stresses during recovery tests. On the basis of the results we have tried to show the origin of the driving force responsible for shape memory effect.

The development and validation of a novel current-based induction motor (IM) condition monitoring (CM) system is described. The system utilizes only current and voltage signals and conducts fault detection using a combination of model-based and model-free (motor current signature analysis) fault detection methods. The residuals (or fault indicator values) generated by these methods are analyzed by a fuzzy logic diagnosis algorithm that provides a diagnosis with regard to the health of the induction motor. Specifically, this includes an indication of the health of the major induction motor subsystems, namely the stator windings, the rotor cage, the rolling element bearings, and the air-gap (eccentricity). The paper presents the overall system concept, the induction motor models, development of parameter estimation techniques, fault detection methods, and the fuzzy logic diagnosis algorithm and includes results from 110 different test cases involving four 7.5 kW four pole squirrel cage motors. The results show good performance for the four chosen faults and demonstrate the potential of the system to be used as an industrial condition monitoring tool.

A novel sensorless speed estimation algorithm for use with direct online three-phase induction motors is proposed. Speed information is extracted from the motor current spectrum by tracking the frequency of key components, which vary as a function of motor rotational speed. An important advantage of this technique is that the speed estimation algorithm is independent of motor mechanical and electrical parameters. The algorithm operates via estimating rotor bar number, which is in turn used to determine rotational speed via rotor bar pass frequency detection including a sanity check on estimated speed via comparison with a linear speed estimate based on rated nameplate data. Experimental results are included for a range of three-phase induction motors including motors carrying faults (bearing, rotor, stator and air-gap eccentricity). The results demonstrate the robustness of the algorithm to motors operating with a variety of faults and thus the potential for use of the algorithm in induction motor fault detection and diagnosis applications.