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This work presents a new multilayered laminated composite structure model to predict the mechanical behaviour of multilayered laminated composite structures. This new multilayered structure model describes the shear stress distribution model through the thickness respecting free boundary conditions on the top and bottom surfaces by an exponential function. This model has the same order of complexity as Touratier's model 'Sine', so a shear correction factor is not required like in the first-order shear deformation theory. This model is more precise than all other existing refined theories. This theory is based on the kinematic approach in which the shearing is represented by an exponential function. The virtual power principal is used to deduce the boundary value problem. To verify the precision of the present model, several significant problems on bending, vibration, and buckling of laminated and sandwich structures have been studied. The results by the present model are compared with the exact three-dimensional elasticity theory and with several other well-known theories. The proposed model is found to be more precise for analysing multilayered structures.

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Accurate and early detection of machine faults is an important step in the preventive maintenance of industrial enterprises. It is essential to avoid unexpected downtime as well as to ensure the reliability of equipment and safety of humans. In the case of rotating machines, significant information about machine’s health and condition is present in the spectrum of its vibration signal. This work proposes a fault detection system of rotating machines using vibration signal analysis. First, a dataset of 3-dimensional vibration signals is acquired from large induction motors representing healthy and faulty states. The signal conditioning is performed using empirical mode decomposition technique. Next, multi-domain feature extraction is done to obtain various combinations of most discriminant temporal and spectral features from the denoised signals. Finally, the classification step is performed with various kernel settings of multiple classifiers including support vector machines, K-nearest neighbors, decision tree and linear discriminant analysis. The classification results demonstrate that a hybrid combination of time and spectral features, classified using support vector machines with Gaussian kernel achieves the best performance with 98.2% accuracy, 96.6% sensitivity, 100% specificity and 1.8% error rate.