Quantifying axial deformations of core shear walls using modal parameters

Differential axial deformation between column elements and shear wall elements of cores increase with building height and geometric complexity. Adverse effects due to the differential axial deformation reduce building performance and life time serviceability. Quantifying axial deformations using ambient measurements from vibrating wire, external mechanical and electronic strain gauges in order to acquire adequate provisions to mitigate the adverse effects is well established method. However, these gauges require installing in or on elements to acquire continuous measurements and hence use of these gauges is uneconomical and inconvenient. This motivates to develop a method to quantify the axial deformations. This paper proposes an innovative method based on modal parameters to quantify axial deformations of shear wall elements in cores of buildings. Capabilities of the method are presented though an illustrative example.

Application of machine learning in fault diagnostics of mechanical systems

A diagnostic method based on Bayesian Networks (probabilistic graphical models) is presented. Unlike conventional diagnostic approaches, in this method instead of focusing on system residuals at one or a few operating points, diagnosis is done by analyzing system behavior patterns over a window of operation. It is shown how this approach can loosen the dependency of diagnostic methods on precise system modeling while maintaining the desired characteristics of fault detection and diagnosis (FDD) tools (fault isolation, robustness, adaptability, and scalability) at a satisfactory level. As an example, the method is applied to fault diagnosis in HVAC systems, an area with considerable modeling and sensor network constraints.

Mechanical behaviours of pumpkin peel under compression test

Mechanical damages such as bruising, collision and impact during food processing stages diminish quality and quantity of productions as well as efficiency of operations. Studying mechanical characteristics of food materials will help to enhance current industrial practices. Mechanical properties of fruits and vegetables describe how these materials behave under loading in real industrial operations. Optimizing and designing more efficient equipments require accurate and precise information of tissue behaviours. FE modelling of food industrial processes is an effective method of studying interrelation of variables during mechanical operation. In this study, empirical investigation has been done on mechanical properties of pumpkin peel. The test was a part of FE modelling and simulation of mechanical peeling stage of tough skinned vegetables. The compression test has been conducted on Jap variety of pumpkin. Additionally, stress strain curve, bio-yield and toughness of pumpkin skin have been calculated. The required energy for reaching bio-yield point was 493.75, 507.71 and 451.71 N.mm for 1.25, 10 and 20 mm/min loading speed respectively. Average value of force in bio-yield point for pumpkin peel was 310 N.

A study on acoustic emission signal propagation of a small size multi-cylinder diesel engine

Acoustic emission has been found effective in offering earlier fault detection and improving identification capabilities of faults. However, the sensors are inherently uncalibrated. This paper presents a source to sensor paths calibration technique which can lead to diagnosis of faults in a small size multi-cylinder diesel engine. Preliminary analysis of the acoustic emission (AE) signals is outlined, including time domain, time-frequency domain, and the root mean square (RMS) energy. The results reveal how the RMS energy of a source propagates to the adjacent sensors. The findings lead to allocate the source and estimate its inferences to the adjacent sensor, and finally help to diagnose the small size diesel engines by minimising the crosstalk from multiple cylinders.