Vibration Effect on the SMS Fiber Structure

We present a preliminary result on the singlemode-multimode-singlemode (SMS) fiber structure for a vibration sensor. The SMS fiber structure was placed in a macrobender within the mechanical transducer to detect the frequency of a vibration source. The time series of optical output power of the SMS fiber structure was measured and it was transformed into the frequency domain using the fast Fourier transform. It was demonstrated that the frequency of vibration source can be determined by using the mechanical transducer with the SMS fiber structure. It was also analyzed the distance effect between the source and the SMS fiber structure. It was shown that the frequency measurement of 20 Hz vibration source can be carried out in a range of 0 to 30 cm with an error frequency 0.1 Hz. This scheme is potential for the vibration measurement which offers inexpensive and simple configuration

Fault Diagnosis of Rotating Machinery based on Acoustic Emission using PARAFAC-Source Separation

A common technique of vibration spectrum analysis is used for fault diagnosis of rotating machine in industry. The technique, however, requires a significant man power and has the risk of the direct measurement of vibration signal. This paper presents a remote maintenance technique based on acoustic emission of rotating machinery. The mixing matrix and the source signals are estimated using PARAFAC source separation by performing PARAFAC decomposition algorithm, permutation, and capon beamforming. This proposed technique prove the suitability and effectiveness of acoustic emission technique to diagnose Ball Pass Frequency of The Outer Race (BPFO) defect and misalignment coupling motor to pump

TensorLy: Tensor Learning in Python

Tensors are higher-order extensions of matrices. While matrix methods form the cornerstone of traditional machine learning and data analysis, tensor methods have been gaining increasing traction. However, software support for tensor operations is not on the same footing. In order to bridge this gap, we have developed TensorLy, a Python library that provides a high-level API for tensor methods and deep tensorized neural networks. TensorLy aims to follow the same standards adopted by the main projects of the Python scientific community, and to seamlessly integrate with them. Its BSD license makes it suitable for both academic and commercial applications. TensorLy's backend system allows users to perform computations with several libraries such as NumPy or PyTorch to name but a few. They can be scaled on multiple CPU or GPU machines. In addition, using the deep-learning frameworks as backend allows to easily design and train deep tensorized neural networks. TensorLy is available at https://github.com/tensorly/tensorl

Multi-way Array Decomposition on Acoustic Source Separation for Fault Diagnosis of a Motor-Pump System

In this study, we propose a multi-way array decomposition approach to solve the complexity of approximate joint diagonalization process for fault diagnosis of a motor-pump system. Sources used in this study came from  drive end-motor, nondrive end-motor , drive end pump , and nondrive end pump. An approximate joint diagonalization is a common approach to resolving an underdetermined cases in blind source separation. However, it has quite heavy computation and requires more complexity. In this study, we use an acoustic emission to detect faults based on multi-way array decomposition approach. Based on the obtained results, the difference types of machinery fault such as misalignment and outer bearing fault can be detected by vibration spectrum and estimated acoustic spectrum. The performance of proposed method is evaluated using MSE and LSD. Based on the results of the separation, the estimated signal of the nondrive end pump is the closest to the baseline signal compared to other signals with  LSD is 1.914 and MSE is 0.0707. The instantaneous frequency of the estimated source signal will also be compared with the vibration signal in frequency spectrum to test the effectiveness of the proposed method

Estimating number of speakers via density-based clustering and classification decision

It is crucial to robustly estimate the number of speakers (NoS) from the recorded audio mixtures in a reverberant environment. Some popular time-frequency (TF) methods approach this NoS estimation problem by assuming that only one of the speech components is active at each TF slot. However, this condition is violated in many scenarios where the speeches are convolved with long length of room impulse response coefficients, which causes degenerated performance of NoS estimation. To tackle this problem, a density-based clustering strategy is proposed to estimate NoS based on a local dominance assumption of speeches. Our method consists of several steps from clustering to classification of speakers with the consideration of robustness. First, the leading eigenvectors are extracted from the local covariance matrices of mixture TF components and ranked by the combination of local density and minimum distance to other leading eigenvectors with higher density. Second, a gap-based method is employed to determine the cluster centers from the ranked leading eigenvectors at each frequency bin. Third, a criterion based on averaged volume of cluster centers is proposed to select reliable clustering results at some frequency bins for the classification decision of NoS. The experiment results demonstrate that the proposed algorithm is superior to the existing methods in various reverberation cases with noise-free condition or noise condition

Parallel Algorithms for Constrained Tensor Factorization via the Alternating Direction Method of Multipliers

Tensor factorization has proven useful in a wide range of applications, from sensor array processing to communications, speech and audio signal processing, and machine learning. With few recent exceptions, all tensor factorization algorithms were originally developed for centralized, in-memory computation on a single machine; and the few that break away from this mold do not easily incorporate practically important constraints, such as nonnegativity. A new constrained tensor factorization framework is proposed in this paper, building upon the Alternating Direction method of Multipliers (ADMoM). It is shown that this simplifies computations, bypassing the need to solve constrained optimization problems in each iteration; and it naturally leads to distributed algorithms suitable for parallel implementation on regular high-performance computing (e.g., mesh) architectures. This opens the door for many emerging big data-enabled applications. The methodology is exemplified using nonnegativity as a baseline constraint, but the proposed framework can more-or-less readily incorporate many other types of constraints. Numerical experiments are very encouraging, indicating that the ADMoM-based nonnegative tensor factorization (NTF) has high potential as an alternative to state-of-the-art approaches.Comment: Submitted to the IEEE Transactions on Signal Processin