Detection of grinding burn through the high and low frequency Barkhausen noise

Mičietová, Anna; Neslušan, Miroslav; Čep, Róbert; Ochodek, Vladislav; Mičieta, Branislav; Pagáč, Marek

doi:10.17559/tv-20140203083223

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…The most promising [14,16] for the mentioned tasks are response spatially invariant filter banks, i.e., filter banks based on the Maximum Response filter set such as (BFS, MR8, MR4, and MRS4). Those are previously reported and discussed in material classification tasks [12,13]. In our application to TCM task, we have chosen to use MR8 filter bank, presented in Figure 2, but to exclude the last two isotropic components, as they do not extract relevant texture features.…”

Section: Forming the Texton Based Descriptorsmentioning

confidence: 99%

“…Nevertheless, concerning the usage of vibration based signals, those systems, in most cases use standard, time-domain and/or frequency-domain extracted features or wavelet based features, mostly developed in previous works, some of those already mentioned. In [12], the same authors proposed an adaptive network-based fuzzy inference based method as their actual decision making system, using the same, previously mentioned classical frequency-based descriptors. In this work, we propose the tool wear monitoring strategy which includes novel texture based descriptors, to be applied in the TCM problems utilizing vibration sensor signals.…”

Section: Introductionmentioning

confidence: 99%

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Tool Condition Monitoring System Based on a Texture Descriptors

Antić¹,

Zeljković²,

Kuric³

2018

IJIEM

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All state-of-the-art Tool condition Monitoring systems (TCM), especially those that use vibration sensors, in the tool wear recognition task, heavily depend on the choice of descriptors that contain information concerning the tool wear state, which are extracted from the particular sensor signals. All other postprocessing techniques do not manage to increase the recognition precision if those descriptors are not discriminative enough. In this work, we propose toll wear monitoring strategy, which relies on the novel texture based descriptors. We consider the module of the Short Term Discrete Furrier Transform (STDFT) spectra obtained from the particular vibration sensors signal utterance, as the 2D textured image. This is done by identifying the time scale of STDFT as the first dimension, and the frequency scale as the second dimension of the particular textured image. The obtained textured image is then divided into particular 2D texture patches, covering part of the frequency range of interest. After applying the appropriate filter bank, for each predefined frequency band 2D textons are extracted. From those, for each band of interest, by averaging in time, we extract information regarding the Probability Density Function (PDF) of those textons in the form of lower order moments, thus obtaining the robust tool wear state descriptors. We validate the proposed features by the experiments conducted on the real TCM system, obtaining the high recognition accuracy. ApstraktSvi savremeni sistemi sistemi za nadzor alata, posebno oni koji koriste senzore vibracije u zadatku prepoznavanja habanja alata, u velikoj meri zavise od izbora obeležja, izdvojenih iz određenih senzorskih signala, a koji sadrže informacije o stanju habanja alata. Većina primenjenih tehnika postprocesiranja ne uspeva povećati preciznost prepoznavanja ako ta obeležja nisu dovoljno diskriminativna. U ovom radu predlažemo strategiju nadzora habanja, koja se oslanja na izdvajanje novih obeležja baziranih na teksturi. Posmatran je segment kratkotrajnih diskretnih Furieovih transformacija (STDFT) spektara vibracija, kao 2D teksture slike, dobijen od konkretnog senzorskog signala.Aco Antić et al. 175IJIEM Radi se identifikaciji vremenske skale STDFT kao prve dimenzije i skale frekvencije kao druga dimenzije definisane teksture slike. Dobijena tekstura slike se zatim deli na određene 2D segmente teksture, pokrivajući deo frekventnog opsega od interesa. Nakon primene odgovarajuće banke filtra, za svaki prethodno definisani frekventni opseg izdvajaju se 2D tekstoni. Dobijeni tekstoni, u posmatranom prosečnom vremenu, izdvajaju informacije o funkciji gustine verovatnoće (PDF) tih tekstona u obliku statističkih momenata nižeg reda, čime se dobijaju robusni deskriptori stanja habanja alata. Potvrda rada sistema sa predloženim obeležja je u realnim uslovima. Dobijeni rezultati daju visoku tačnost prepoznavanja.Ključne reči: habanje alata, teksturni deskriptori, vibracioni signal

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Section: Forming the Texton Based Descriptorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%