A novel method to encrypt audio(sound) stream of data by applying chaos is discussed. A pair of one-dimensional logistic maps is used for generating a chaotic sequence. The routine tests of encryption are performed and the results are observed. The proposed scheme is then implemented in real time on a mobile phone and the robustness of the idea is established.

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This study identifies a method for detection of irregularities such as open cracks or grooves on a rotating stepped shaft with multiple discs, based on the wavelet transforms. Cracks are represented as reduction in diameter of shaft (groove) with small width. Single as well as multiple grooves are considered on stepped shaft at locations of stress concentration. Translational or rotational response curves/mode shapes are extracted from finite element analysis of rotors with and without grooves. Discrete and continuous 1D wavelet transforms are applied on resultant response curve or mode shapes. The results show that rotational response curves or mode shapes are more sensitive to shaft cracks and key contributors to identify the location of cracks than translation response curves or mode shapes. Discrete wavelet transforms are accurate enough to locate the groove of smaller size. Effectiveness of detection by wavelets transforms is analysed for single as well as multiple grooves with increase in groove depth. Increase in groove depth can be quantified by increase in wavelet coefficient, and it can be an indicator. White Gaussian noise with low signal-to-noise ratio is added to response curves and analysed for crack location identification. Intelligent techniques such as artificial neural networks are used to quantify the location and depth of crack. Discrete wavelet transforms coefficients are provided as input to the neural network. Feed forward artificial neural networks are trained with Levenberg–Marquardt back propagation algorithm. Trained networks are able to quantify the crack location and depth accurately.

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This study identifies a method for detection of irregularities like open cracks or grooves on a rotating stepped shaft with multiple discs, based on the Wavelet Transforms. Cracks are represented as reduction in diameter of shaft (groove) with small width. Single as well as multiple grooves are considered on stepped shaft at locations of stress concentration. Translational or rotational response curves/mode shapes are extracted from finite element analysis of rotor with and without grooves. Discrete and continuous 1D wavelet transforms applied on resultant response curve or mode shapes. The results show that rotational response curves or mode shapes are more sensitive to shaft cracks and key contributors to identify the location of cracks than translation response curves or mode shapes. Discrete Wavelet Transforms are accurate enough to locate the groove of smaller size. Effectiveness of detection by Wavelets Transforms is analyzed for Single as well as Multiple grooves with increase in grove depth. Increase in groove depth can be quantified by increase in wavelet coefficient and it can be an indicator.

Condition monitoring of rotor dynamic systems is emerging research in recent years. The proposed research is a condition monitoring methodology based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to detect the open cracks in the single disk rotor-bearing system. Condition monitoring systems generally requires a large amount of processed data for a specific output. The proposed methodology uses the same input data to train two different ANFIS without compromising on the accuracy of the results. The response of rotor-bearing system is generated by using finite element model analysis and harmonic balance method. All simulations are programed in MATLAB programing software. The effects of open groove or wedge cracks (notch crack) on natural frequency and resultant operational response (nodal deflections) of rotor-bearing systems are analyzed. Response orbit at 3× resonance of first natural frequency is analyzed to diagnose the crack in rotor shaft. Resultant operational response (Absolute response due to crack) is recorded for various crack locations and crack depths. Continuous Wavelet Transforms (CWT) are used, to extract the features from operational deflection shape (from operational response) to detect the crack location and their severity (depth). Location of maximum CWT coefficients provides the close vicinity of crack and their magnitude provides the severity of crack. Crack as small as 1% of crack depth to diameter ratio can be identified by CWT. ANFIS are used as a machine condition monitoring methodology to diagnose the crack and predict the crack parameters (crack location and depth). Two Parallel ANFIS are trained to predict the crack parameters. ANFIS-1 is trained for crack location and ANFIS-2 is trained for crack depth. CWT coefficients, maximum response amplitude at the vicinity of crack, and first three natural frequencies are provided as input to both ANFIS-1 and ANFIS-2 for training. The trained condition monitoring methodology accurately detects (predict) the crack location (ANFIS-1) and crack depth (ANFIS-2) with root mean squared error of 0.0833 and 0.137916 respectively.