Algorithm Indicating Moment of P-Wave Arrival Based on Second-Moment Characteristic

Sokołowski, Jakub; Obuchowski, Jakub; Zimroz, Radosław; Wyłomańska, Agnieszka; Koziarz, Eugeniusz

doi:10.1155/2016/4051701

Cited by 6 publications

(5 citation statements)

References 22 publications

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“…In recent years, substantial works on segmentation methods for different applications appeared in the literature. A few interesting applications include condition monitoring [33,34] (where structural break detection method based on the adaptive regression splines technique has been proposed to recognize a change of operational regime in copper ore crusher vibration and local maxima method has been proposed in the time-frequency domain for spike detection in bearings vibration, respectively), biomedical signals (e.g., electrocardiogram) [35][36][37][38][39] (where hidden Markov models, moving average and Savitzky-Golay filter, cepstral analysis, wavelet transform, envelope-based segmentation, etc., have been used), speech analysis [40][41][42] (where nonlinear speech analysis based on the microcanonical multiscale formalism, adaptation of Appel and Brandt algorithm, innovation (Shur) adaptive filter have been discussed), econometrics [43,44] (where the regime switching model is applied), and seismic signals [45][46][47][48][49] (where, among others, cumulative sum of Gaussian probability density functions and Markov regime-switching models as well as the empirical second moment of given raw signal have been proposed for seismic signal segmentation in order to extract seismic events).…”

Section: Brief State Of the Artmentioning

confidence: 99%

A Method for Structure Breaking Point Detection in Engine Oil Pressure Data

et al. 2021

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In this paper, a heavy-duty loader operated in an underground mine is discussed. Due to extremely harsh operational conditions, an important maintenance problem is related to engine oil pressure. We have found that when the degradation process appears, the nature of variation of pressure engine oil changes. Following this observation, we have proposed a data analysis procedure for the structure break point detection. It is based on specific data pre-processing and further statistical analysis. The idea of the paper is to transform the data into a nearly monotonic function that describes the variation of machine condition or in the statistical language—change of the regime inside the process. To achieve that goal we proposed an original data processing procedure. The dataset analyzed in the paper covers one month of observation. We have received confirmation that during that period, maintenance service has been done. The purpose of our research was to remove ambiguity related to direct oil pressure analysis and visualize oil pressure variation in the diagnostic context. As a fleet of machines in the considered company covers more than 1000 loaders/trucks/drilling machines, the importance of this approach is serious from a practical point of view. We believe that it could be also an inspiration for other researchers working with industrial data.

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Section: Brief State Of the Artmentioning

confidence: 99%

A Method for Structure Breaking Point Detection in Engine Oil Pressure Data

et al. 2021

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“…Method. STA/LTA method is often used in the problem of P-wave arrival time detection [28]. e core idea is the evaluation of the characteristic function (CF) of the seismic signal using two sliding windows: one shorter and one longer.…”

Section: Sta/lta As a Verificationmentioning

confidence: 99%

“…e comparison of the classical methods can be found in [15,22,23]. In recent years, a couple of new algorithms were developed, e.g., [18][19][20][21][24][25][26][27][28][29]. Further information about their accuracy and computational time can be found in [15,23].…”

Section: Introductionmentioning

confidence: 99%

Combination of Principal Component Analysis and Time‐Frequency Representation for P‐Wave Arrival Detection

Wodecki

Hebda-Sobkowicz

Mirek

et al. 2019

Shock and Vibration

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Seismic events are phenomena which commonly occur in the mining industry. Due to their dangerous character, such information as the energy of the potential event, the location of hazardous regions with higher seismic activity is considered valuable. However, the acquisition of this information is almost impossible without the ability to detect the onset time of the seismic event. The main objectives of algorithms in finding P-wave are high accuracy, reasonable time of operation, and automatic detection of wave arrival. In this paper, an innovative method which incorporates principal component analysis (PCA) with time-frequency representation of the signal is proposed. Due to the significant difference between the spectra of recorded seismic wave and pure noise which precedes the event, time-frequency representation allows for better accuracy of signal change detection. However, with an additional domain, the complexity rises. Thus, the incorporation of PCA (which is known for high efficiency in lowering data dimensions while maintaining original information) seems to be recommended. In order to show the feasibility of the method, it will be tested on real data originating from monitoring system used in underground mine.

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“…The algorithm is expansion of P-wave indicating algorithm [18]. The procedure is based on the characteristic of signals' empirical second moment.…”

Section: Sokolowski-wylomanska-zimroz Algorithmmentioning

confidence: 99%

Comparison of recent S-wave indicating methods

Hubicka

Sokołowski

2018

E3S Web Conf.

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Abstract. Seismic event consists of surface waves and body waves. Due to the fact that the body waves are faster (P-waves) and more energetic (S-waves) in literature the problem of their analysis is taken more often. The most universal information that is received from the recorded wave is its moment of arrival. When this information is obtained from at least four seismometers in different locations, the epicentre of the particular event can be estimated [1]. Since the recorded body waves may overlap in signal, the problem of wave onset moment is considered more often for faster P-wave than S-wave. This however does not mean that the issue of S-wave arrival time is not taken at all. As the process of manual picking is time-consuming, methods of automatic detection are recommended (these however may be less accurate). In this paper four recently developed methods estimating S-wave arrival are compared: the method operating on empirical mode decomposition and Teager-Kaiser operator [2], the modification of STA/LTA algorithm [3], the method using a nearest neighbour-based approach [4] and the algorithm operating on characteristic of signals' second moments. The methods will be also compared to wellknown algorithm based on the autoregressive model [5]. The algorithms will be tested in terms of their S-wave arrival identification accuracy on real data originating from International Research Institutions for Seismology (IRIS) database.

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Algorithm Indicating Moment of P-Wave Arrival Based on Second-Moment Characteristic

Cited by 6 publications

References 22 publications

A Method for Structure Breaking Point Detection in Engine Oil Pressure Data

A Method for Structure Breaking Point Detection in Engine Oil Pressure Data

Combination of Principal Component Analysis and Time‐Frequency Representation for P‐Wave Arrival Detection

Comparison of recent S-wave indicating methods

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