A procedure for weighted summation of the derivatives of reflection coefficients in adaptive Schur filter with application to fault detection in rolling element bearings

Makowski, Ryszard; Zimroz, Radosław

doi:10.1016/j.ymssp.2012.05.005

Cited by 40 publications

(26 citation statements)

References 32 publications

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“…It proposes a segmentation algorithm whose starting point is estimation of the reflection coefficients with the use of an adaptive Schur algorithm. This algorithm is characterized by rapid adaptation to the changing signal, stability and robustness (Lee et al, 1981;Lopatka et al, 2005;Makowski and Zimroz, 2013). Based on Schur coefficients, a parametric time-varying spectrum is defined and subsequently converted to time-varying signal powers in subbands with the use of mel frequency scale.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, assuming that a speech signal spectrum contains 4-5 formants, it will usually be satisfactory to assume P = 10 to P = 14. On the other hand, the adopted value of the forgetting factor should depend on the rate of the signal change and should change adaptively (Makowski and Zimroz, 2013), but such an approach is difficult to realize. Therefore, a constant value of λ is employed here.…”

Section: Introductionmentioning

confidence: 99%

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Automatic speech signal segmentation based on the innovation adaptive filter

Makowski

Hossa

2014

International Journal of Applied Mathematics and Computer Science

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Speech segmentation is an essential stage in designing automatic speech recognition systems and one can find several algorithms proposed in the literature. It is a difficult problem, as speech is immensely variable. The aim of the authors' studies was to design an algorithm that could be employed at the stage of automatic speech recognition. This would make it possible to avoid some problems related to speech signal parametrization. Posing the problem in such a way requires the algorithm to be capable of working in real time. The only such algorithm was proposed by Tyagi et al., (2006), and it is a modified version of Brandt's algorithm. The article presents a new algorithm for unsupervised automatic speech signal segmentation. It performs segmentation without access to information about the phonetic content of the utterances, relying exclusively on second-order statistics of a speech signal. The starting point for the proposed method is time-varying Schur coefficients of an innovation adaptive filter. The Schur algorithm is known to be fast, precise, stable and capable of rapidly tracking changes in second order signal statistics. A transfer from one phoneme to another in the speech signal always indicates a change in signal statistics caused by vocal track changes. In order to allow for the properties of human hearing, detection of inter-phoneme boundaries is performed based on statistics defined on the mel spectrum determined from the reflection coefficients. The paper presents the structure of the algorithm, defines its properties, lists parameter values, describes detection efficiency results, and compares them with those for another algorithm. The obtained segmentation results, are satisfactory.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic speech signal segmentation based on the innovation adaptive filter

Makowski

Hossa

2014

International Journal of Applied Mathematics and Computer Science

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“…Basically, the Signal of Interest (SOI) that carries information about the presence of faults might be defined as low energy, random signal with amplitude modulation, or simpler as cyclostationary signal (of order 2) (Antoni 2005;Antoni et al 2004;Makowski and Zimroz 2013;Obuchowski et al 2013;Randall and Antoni 2011;Urbanek et al 2013;Zimroz and Bartelmus 2012). The non-informative part of the signal is the deterministic high energy part and is the part we wish to remove or suppress by advanced signal processing methods.…”

Section: Introductionmentioning

confidence: 99%

“…One of the most known solutions for constant load/speed operations exploits autoregressive (AR) modeling for deterministic high energy components (Baillie et al 1996;Endo et al 2007;Makowski et al 2013;Wang et al 2002 and many others). Typical methodology is: after establishing the model and model based inverse filter, the residual signal (informative part) is extracted and further analyzed.…”

Section: Introductionmentioning

confidence: 99%

“…The gearbox operates in bucket wheel excavator, a heavy duty mining machine subjected to cyclic load/speed variation due to digging/excavating process. To model such a data, it is proposed to use special case of AR model with time-varying coefficients (Makowski and Zimroz 2013;Poulimenos and Fassois 2006) and more specifically, due to periodicity of excavation process affecting the gearbox vibration, a periodic autoregressive model (PAR) is strongly suggested. In the chapter several topics will be discussed: real data inspired to PAR modeling, estimation details, simulations and PAR based inverse filtering for informative signal extraction.…”

Section: Introductionmentioning

confidence: 99%

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Periodic Autoregressive Modeling of Vibration Time Series From Planetary Gearbox Used in Bucket Wheel Excavator

Wyłomańska

Obuchowski

Zimroz

et al. 2014

Lecture Notes in Mechanical Engineering

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Vibration signals acquired from machines operating under non-stationary operations are difficult to process due to their time varying spectral content, statistical properties, signal to noise ratio etc. In case of damaged machine vibration analysis, the classical damage detection approach might be defined as informative and non-informative contents separation. It can be done in many ways, including model based approaches. One of the most known solutions for constant load/speed operations exploits autoregressive (AR) modeling of the deterministic high energy components that often mask the weak impulsive and stochastic part of the signal. After establishing the model, the residual signal is extracted and further analyzed. In the case presented here, AR modeling is considered inappropriate because of the variation of speed/load conditions. To illustrate importance of the problem and novelty of our approach, a planetary gearbox vibration will be analyzed. The gearbox operates in a bucket wheel excavator (heavy duty mining machine) subjected to cyclic load/speed variation due to the digging/excavating process. Due to periodicity of the excavation process, it seems appropriate to assume a periodic autoregressive (PAR) model for the deterministic high energy components. In the chapter several topics will be discussed: real data inspired motivation for PAR modeling, estimation A. Wyłomańska (B)

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