Geometrical method of selection of features of diagnostic signals

Dybała, Jacek; Radkowski, S.

doi:10.1016/j.ymssp.2005.10.011

Cited by 13 publications

(12 citation statements)

References 3 publications

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“…We have chosen the correlations of the variable #5, which plays a central role in the left exhibit of figure 6. Using the bootstrap method, the boostrap samples of length N=5000 were created for the following pairs: (5,12), (5,1), (5,7), and (5,8). Histograms of the respective correlation coefficients, calculated from the generated bootstrap samples, are displayed in Fig.…”

Section: Investigation Of the Correlations Between Features In Sets Amentioning

confidence: 99%

Investigation on Spectral Structure of Gearbox Vibration Signals by Principal Component Analysis for Condition Monitoring Purposes

Zimroz¹,

Bartkowiak²

2011

J. Phys.: Conf. Ser.

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Abstract. Spectral analysis is well-established analysis of vibrations used in diagnostics both in academia and industry. In general, one may identify components related to particular stages in the gearbox and analyze amplitudes of these components with a simple rule for decisionmaking: if amplitudes are increasing the condition becomes worse. However, usually one should analyze not single amplitude but at least several components, but: how to analyze them simultaneously? We have provided an example (case study) for planetary gearboxes in good and bad conditions (case B and case A). As diagnostic features we have used 15 amplitudes of spectral components related to fundamental planetary mesh frequency and its harmonics. Using Principal Component Analysis (PCA), it has been shown that amplitudes don't vary in the same way; change of condition affects not only amplitudes of all components in that sense, but also relation between them. We have investigated geometry of the data and it has been shown that the proportions of the explained total inertia of the three data sets ("good", "bad" and mixed good/bad) are different. We claim that it may be a novel diagnostic approach to employ multidimensional analysis for accounting not only directly observed values but also interrelations both within and between the two groups of data. Different structure of the data is associated with different condition of the machines and such assumption is specified for the first time in the literature. Obviously it requires more studies.

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Section: Investigation Of the Correlations Between Features In Sets Amentioning

confidence: 99%

Investigation on Spectral Structure of Gearbox Vibration Signals by Principal Component Analysis for Condition Monitoring Purposes

Zimroz¹,

Bartkowiak²

2011

J. Phys.: Conf. Ser.

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“…The application of a classifier based on neural networks enables the recognition of machines technical state when mathematical description is ambiguous [2][3][4]. Broad research on neural classifiers carried out recently indicates that artificial neural networks are promising alternative to conventional methods of classification [2,3,5,6]. …”

Section: S(r θ) = E T {φ(S)mentioning

confidence: 99%

“…The diagnostics of toothed gear, owing to their wide use in transmissions, is the object of interest of many industrial and scientific centers. The constructional variety of toothed-gear types inflicts that despite the existing inference procedures and algorithms of analysis of vibroacoustic signals, accuracy of diagnosis is in many cases insufficient [6,7]. Artificial intelligence methods, which allow to model different kinds of nonlinearities contained in diagnostic signals, make up competitive tool compared to classic inference algorithms [2][3][4].…”

Section: Symptoms Selection For Technical State Classification Of Toomentioning

confidence: 99%

Hardware Implementation of Artificial Neural Networks for Vibroacoustic Signals Classification

Dąbrowski¹,

Jamro²,

Cioch³

2010

Acta Phys. Pol. A

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This paper studies the architecture of a neural classifier designed to identify technical condition of machines, based on vibroacoustic signals. The designed neural network is optimized for implementation on Field Programmable Gate Arrays (FPGA) programmable devices. FPGA allows massive parallelism and thus real-time classification as each neuron can execute arithmetic operations simultaneously. The classifier of vibroacoustic signals was designed and tested for the self -organized neural network. The teaching vectors are based on estimates derived from processed vibroacoustic signals generated by rotary machines. The created classifier was applied for recognizing technical state of demonstrative toothed gear DMA1 in variable operating conditions.

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“…Pitting, chipping, or breakage of the teeth may occur in wheel gearing. Research on the detection of this type of damage is presented, inter alia, in [ 4 , 15 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. In the case of bearings, pitting on the bearing elements or cracks in the race or balls can be encountered.…”

Section: Introductionmentioning

confidence: 99%

A Method for Diagnosing Gearboxes of Means of Transport Using Multi-Stage Filtering and Entropy

Figlus

2019

Entropy

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The paper presents a method of processing vibration signals which was designed to detect damage to wheels of gearboxes for means of transport. This method uses entropy calculation, and multi-stage filtering calculated by means of digital filters and the Walsh–Hadamard transform to process signals. The presented method enables the extraction of vibration symptoms, which are symptoms of gear damage, from a complex vibration signal of a gearbox. The combination of multi-stage filtering and entropy enables the elimination of fast-changing vibration impulses, which interfere with the damage diagnosis process, and make it possible to obtain a synthetic signal that provides information about the state of the gearing. The paper demonstrates the usefulness of the developed method in the diagnosis of a gearbox in which two types of gearing damage were simulated: tooth chipping and damage to the working surface of the teeth. The research shows that the application of the proposed method of vibration of signal processing enables observation of the qualitative and quantitative changes in the entropy signal after denoising, which are unambiguous symptoms of the diagnosed damage.

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Geometrical method of selection of features of diagnostic signals

Cited by 13 publications

References 3 publications

Investigation on Spectral Structure of Gearbox Vibration Signals by Principal Component Analysis for Condition Monitoring Purposes

Investigation on Spectral Structure of Gearbox Vibration Signals by Principal Component Analysis for Condition Monitoring Purposes

Hardware Implementation of Artificial Neural Networks for Vibroacoustic Signals Classification

A Method for Diagnosing Gearboxes of Means of Transport Using Multi-Stage Filtering and Entropy

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