Towards damage detection using blind source separation integrated with time-varying auto-regressive modeling

We propose an adaptive improved natural gradient algorithm for blind separation of independent sources. First, inspired by the well-known backpropagation algorithm, we incorporate a momentum term into the natural gradient learning process to accelerate the convergence rate and improve the stability. Then an estimation function for the adaptation of the separation model is obtained to adaptively control a step-size parameter and a momentum factor. The proposed natural gradient algorithm with variable step-size parameter and variable momentum factor is therefore particularly well suited to blind source separation in a time-varying environment, such as an abruptly changing mixing matrix or signal power. The expected improvement in the convergence speed, stability, and tracking ability of the proposed algorithm is demonstrated by extensive simulation results in both time-invariant and time-varying environments. The ability of the proposed algorithm to separate extremely weak or badly scaled sources is also verified. In addition, simulation results show that the proposed algorithm is suitable for separating mixtures of many sources (e.g., the number of sources is 10) in the complete case.

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A Novel Damage Detection Algorithm using Time-Series Analysis-Based Blind Source Separation

Shock and Vibration ◽

10.1155/2013/237805 ◽

2013 ◽

Vol 20 (3) ◽

pp. 423-438 ◽

Cited By ~ 24

Author(s):

A. Sadhu ◽

B. Hazra

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Damage Detection ◽

Blind Source Separation ◽

Source Separation ◽

Damage Index ◽

Detection Algorithm ◽

Mode Shapes ◽

Series Analysis ◽

Modal Response

In this paper, a novel damage detection algorithm is developed based on blind source separation in conjunction with time-series analysis. Blind source separation (BSS), is a powerful signal processing tool that is used to identify the modal responses and mode shapes of a vibrating structure using only the knowledge of responses. In the proposed method, BSS is first employed to estimate the modal response using the vibration measurements. Time-series analysis is then performed to characterize the mono-component modal responses and successively the resulting time-series models are utilized for one-step ahead prediction of the modal response. With the occurrence of newer measurements containing the signature of damaged system, a variance-based damage index is used to identify the damage instant. Once the damage instant is identified, the damaged and undamaged modal parameters of the system are estimated in an adaptive fashion. The proposed method solves classical damage detection issues including the identification of damage instant, location as well as the severity of damage. The proposed damage detection algorithm is verified using extensive numerical simulations followed by the full scale study of UCLA Factor building using the measured responses under Parkfield earthquake.

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A Multimodal Solution to Blind Source Separation of Moving Sources

Machine Audition ◽

10.4018/978-1-61520-919-4.ch005 ◽

2010 ◽

pp. 107-125

Author(s):

Syed Mohsen Naqvi ◽

Yonggang Zhang ◽

Miao Yu ◽

Jonathon A. Chambers

Keyword(s):

Real Time ◽

Blind Source Separation ◽

Particle Filtering ◽

Source Separation ◽

Visual Modality ◽

Time Varying ◽

Video Cameras ◽

Moving Sources ◽

Tracking Process ◽

Reverberant Environment

A novel multimodal solution is proposed to solve the problem of blind source separation (BSS) of moving sources. Since for moving sources the mixing filters are time varying, therefore, the unmixing filters should also be time varying and can be difficult to track in real time. In this solution the visual modality is utilized to facilitate the separation of moving sources. The movement of the sources is detected by a relatively simplistic 3-D tracker based on video cameras. The tracking process is based on particle filtering which provides robust tracking performance. Positions and velocities of the sources are obtained from the 3-D tracker and if the sources are moving, a beamforming algorithm is used to perform real time speech enhancement and provide separation of the sources. Experimental results show that by utilizing the visual modality, a good BSS performance for moving sources in a low reverberant environment can be achieved.

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