Spectral Characterization of Content Level Based on Acoustic Resonance: Neural Network and Feedforward Fuzzy Net Approaches

Free vibration occurs when a mechanical system is disturbed from equilibrium by an external force and then it is allowed to vibrate freely. In free vibrations, the system oscillates under the influence of inherent forces on the system itself. Free vibrations are associated with natural frequencies that are properties of the oscillating system, quantified in parameters such as mass, shape, and stiffness distribution. A number of these mechanical characteristics can be inferred from vibration patterns or from the generated sound using the adequate sensors. It is well known that liquid level inside a container modifies its natural frequencies. Unfortunately, other container characteristics such as shape, composition, temperature, and pressure modifies the natural frequencies of vibration making the task of level measurement nontrivial. Preliminary experiments aiming to do measurement of liquid content level and container characterization are presented in this work. Spectral analysis in Fourier domain is used to perform feature extraction, with the feature vectors containing information about the frequencies having the greatest amplitude in the respective spectral analysis. Classification has been carried out using two computational intelligence techniques for comparison purposes: neural network classification and a fuzzy logic inference system built using singleton fuzzifier, product inference rule, Gaussian membership functions and center average defuzzifier. Preliminary results showed a better performance when using the neural network-based approach in comparison to the fuzzy logic-based approach, obtaining in average a MSE of 0.02 and 0.09, respectively.