Fault Feature Extraction for Reciprocating Compressors Based on Underdetermined Blind Source Separation

In practical engineering applications, the vibration signals collected by sensors often contain outliers, resulting in the separation accuracy of source signals from the observed signals being seriously affected. The mixing matrix estimation is crucial to the underdetermined blind source separation (UBSS), determining the accuracy level of the source signals recovery. Therefore, a two-stage clustering method is proposed by combining hierarchical clustering and K-means to improve the reliability of the estimated mixing matrix in this paper. The proposed method is used to solve the two major problems in the K-means algorithm: the random selection of initial cluster centers and the sensitivity of the algorithm to outliers. Firstly, the observed signals are clustered by hierarchical clustering to get the cluster centers. Secondly, the cosine distance is used to eliminate the outliers deviating from cluster centers. Then, the initial cluster centers are obtained by calculating the mean value of each remaining cluster. Finally, the mixing matrix is estimated with the improved K-means, and the sources are recovered using the least square method. Simulation and the reciprocating compressor fault experiments demonstrate the effectiveness of the proposed method.

A Novel Underdetermined Blind Source Separation Method Based on OPTICS and Subspace Projection

In recent years, the problem of underdetermined blind source separation (UBSS) has become a research hotspot due to its practical potential. This paper presents a novel method to solve the problem of UBSS, which mainly includes the following three steps: Single source points (SSPs) are first screened out using the principal component analysis (PCA) approach, which is based on the statistical features of signal time-frequency (TF) points. Second, a mixing matrix estimation method is proposed that combines Ordering Points To Identify the Clustering Structure (OPTICS) with an improved potential function to directly detect the number of source signals, remove noise points, and accurately calculate the mixing matrix vector; it is independent of the input parameters and offers great accuracy and robustness. Finally, an improved subspace projection method is used for source signal recovery, and the upper limit for the number of active sources at each mixed signal is increased from m−1 to m. The unmixing process of the proposed algorithm is symmetrical to the actual signal mixing process, allowing it to accurately estimate the mixing matrix and perform well in noisy environments. When compared to previous methods, the source signal recovery accuracy is improved. The method’s effectiveness is demonstrated by both theoretical and experimental results.

Augmented Hardware Design of BSS in Real Time by Real Time Feasible Source Signal Separator

Blind source separation is a blooming sector in the digital signal processing for severing exact signal from the dense recorded. Exclusively, among the “Blind Source Separation” the “Under Determined Blind Source Separation” is considered than an over determined Blind Source Separation due to its wide range of usage. Nevertheless, it is seen that the real implementation is very rarely done in existing researches, because the real time implementation of UBSS (Underdetermined Blind Source Separation)is existed to be a challenging one due to its lacking hardware characteristics of increased latency, reduced speed and consumption of more memory space. Consequently, there is an increase need to implement an Underdetermined source signal separation real time with improved hardware utility that in this Unswerving framework a Real time feasible Source Signal separator is formulated in which initially the source signals are decomposed by Boosted band-limited VMD (Variational Mode Decomposition)into the “Multi component Signal” and then to an amount of "Band-Limited” IMF subjected to the Encompassed Hammer sley–Clifford source separation algorithm that uses expectation-maximization and Gibbs sampling an alternative to deterministic algorithms to determine the exact estimated parameter from E-M method. Subsequently, the source separation algorithm infers the best separation of sources signals by exact estimation and determination from the decomposed signals, whereas the iterations in E-M estimation are reduced by Gauss-Seidel method. Thus our novel source signal separator internally with a signal decomposer and a source separation algorithm with lesser number of iterations which reduces memory consumption and yields better hardware realization with reduced latency and increased speed. The proposed Implementation is done in Xilinx Platform.