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The large wooden resonator of the Sarasvati Veena amplifies and radiates the sound in almost all directions. The directional and spatial dependence of this radiation is studied in conjunction with the mode shapes of the top plate of the resonator. Sound radiation patterns are simulated theoretically using the nodal displacement data obtained from the numerical modal analysis of the resonator. The experimental analysis involves the manual plucking of the Veena string. The radiated sound is recorded by placing microphones around the resonator in circular arrays of different radii in the different planes. These combinations of arrays at different distances and planes provide a thorough knowledge of sound radiating out of the resonator. The intensities of different frequencies in the recorded spectral data as functions of direction and distance from the approximate center of the top plate of the resonator are studied. Experimentally measured patterns show the importance of the top plate over the body of the resonator. Theoretical and experimental radiation patterns for different harmonics of the plucked string are compared and a good match is observed. The behavior of the radiating sound in the different planes at different radial distances from the assumed center is discussed.

Bearings are integral components of rotating machinery and their failure tends to be a catastrophic failure of the machine. Poincare Maps are used to detect bearing failures using the concept of non-linear dynamics. Each time-domain vibration signature array has its own Poincare Map over a period of time. Fast Fourier Transform (FFT) is a method of analysing the frequency plots of a bearing signature. Convolutional Neural Networks (CNN) process the bearing Continuous Wavelet Transform images and provide the Remaining Useful Life (RUL) of the bearing. The Poincare Maps and FFT plots are used to diagnose the type and location of the fault in the bearing, whereas the CNN helps to provide the fraction of Remaining Useful Life. The study concludes that a combination of Poincare Maps, FFT analysis and Convolutional Neural Networks constitutes a robust and precise method of monitoring bearing conditions.

This study outlines the experimental investigation methods of condition monitoring to predict bearing failures using the experimental vibration signatures. The purpose of condition monitoring is to maximize the machine availability and utility of the machine components. Bearings being one of the most common component in any rotating machinery, it is vital to study the health of the bearing and can predict bearing failure, its location and severity. This prevents machine downtime, monetary loss and unfortunate accidents. A test rig was fabricated to get the vibration signatures of bearings. Prediction of bearing failure relies on the presence of the bearing characteristic frequencies – inner race frequency, outer race frequency, ball pass frequency and fundamental train frequency – and its harmonics in the vibration signal acquired. These frequencies are present in the vibration signature due to the interaction of surfaces of different bearing components that have defects in them. Both time and frequency domain numerical signature analysis were performed on the vibration signatures acquired. Simple frequency domain method like Fast Fourier Transform (FFT), chaotic vibration method like modified Poincare mapping and time-frequency domain Wigner-Ville distribution (WVD) were used in detecting bearing failure. Using the FFT analysis method, it is hard to predict the failures, hence better signal processing methods like modified Poincare mapping and WVD are used. Also, it is observed that the chaotic vibration signatures found in the lower-order mechanical systems like bearings. With the chaotic analysis methods like, Poincare Mapping and Wigner-Ville Distribution, the location and the severity of the bearing failure can be predicted.

This paper is based on the experimental study for design and control of vibrations in automotive vehicles. The objective of this paper is to develop a model for the highly nonlinear Magneto-Rheological (MR) damper to maximize passenger comfort in an automotive vehicle. The behavior of the MR damper is studied under different loading conditions and current values in the system. The input and output parameters of the system are used as a training data to develop a suitable model using Genetic Algorithm. To generate the training data, a test rig similar to a quarter car model was fabricated to load the MR damper with a mechanical shaker to excite it externally. With the help of the test rig the input and output parameter data points are acquired by measuring the acceleration and force of the system at different points with the help of an impedance head and accelerometers. The model is validated by measuring the error for the testing and validation data points. The output of the model is the optimum current that is supplied to the MR Damper, using a controller, to increase the passenger comfort by minimizing the amplitude of vibrations transmitted to the passenger. Besides using this model for cars, bikes and other automotive vehicles it can also be modified by re-training the algorithm and used for civil structures to make them earthquake resistant.

This study aims to characterize and standardize the Sarasvati Veena of South India. The instrument is very unique in the way in which it is manufactured and played. This instrument is often tuned relative to the accompanying instruments. The aim of the study was to find out the optimum tuning frequency for a particular Veena. The natural frequency of the body of the Veena was determined by conducting an impact test. The strings of the Veena were tuned to the corresponding body frequency to obtain resonance and FFT plots were generated to see the frequency variation. The optimum tuning frequency for a given Veena was obtained such that the body undergoes conductive resonance when tuned to that particular frequency. Acoustic readings were taken using a sound level meter to confirm on the results obtained by impact test.

Present day requirements for enhanced reliability of rotating machinery have become critical for the manufacturing sector. Every rotating machine exhibits a unique characteristic vibration and acoustic signature. This can be used to identify the defective parts and estimate the present severity of the problem; most importantly, without opening the machine for inspection. Moreover, it aids in the reduction of unscheduled down time, turnaround time and existing noise levels. The paper deals with the vibro-acoustic condition monitoring of metal lathe and development of predictive models for the detection of probable faults using Machine Learning. Experiments were conducted to obtain vibration signatures using accelerometers and the data was further processed while the acoustic signatures were obtained using noise level meters. Results were obtained for idle running, turning and facing operations using a single point cutting tool for constant spindle speeds, feed and depth of cut. The vibro-acoustic signatures of six metal lathe machines were collected over a period of 5 months and the trends obtained were analyzed. The filtered acceleration (g-peak) signatures were compared with the General Machinery Vibration Severity Chart and based on the velocity classification results, the best machine was chosen for the development of predictive models. Vibration as well as acoustic signatures were isolated using filters, empirical relations and manufacturing data. Predictive models were made using machine learning algorithms to predict the failure of the lathe based on its historical data. These models can be used by industries to detect unhealthy trends and identify troublesome parts in the machine which can be then scheduled for maintenance thereby decreasing production downtimes.

Automotive suspension system is an important part of car comfort and safety. In this article active suspension for 2DOF nonlinear coupled Passenger-Car model with force actuator is designed using PID control and H-infinity control. This paper is focused on comparison of those two controllers. Simulations on an exact nonlinear model of the suspension are performed for control validation.