Qualitative and Quantitative Analysis of Stochastic Processes Based on Measured Data, Ii: Applications to Experimental Data

Gradišek, Janez; Govekar, Edvard; Grabec, Igor

doi:10.1006/jsvi.2001.4051

Cited by 13 publications

(7 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The method has been successfully tested by analyzing simulated data from known dynamical systems such as nonlinear oscillators and chaotic systems (see, e.g., Gradisek et al 2000;2002a, 2002bSiegert et al 1998). Friedrich et al (2000) introduced the method in the biological domain by investigating different types of tremor, while Kuusela et al (2003) presented analyses of heart-rate fluctuations in humans suggesting new diagnostic tools.…”

Section: Extraction Proceduresmentioning

confidence: 99%

“…Hence, contrasting the analysis of the deterministic component, an immediate display by means of vector fields is no longer feasible. Gradisek et al (2002a) combined the four extracted diffusion coefficients into individual 2×2 matrices and displayed them as geometrical objects located in the two-dimensional phase space lattice. This display allows for a local investigation of fluctuation strengths that we illustrate for the case of forearm cycling in Fig.…”

Section: Interpretation Of the Stochastic Componentmentioning

confidence: 99%

“…8. As geometrical objects we used ellipses whose diameters are given by the eigenvalues of the diffusion coefficient (2×2 matrix) and their inclination by the corresponding eigenvectors (note that Gradisek et al 2002a, used parallelograms instead of ellipses). Since ellipses change shape when comparing peak positions and velocities with intermediate movement parts, we find that the noise strength depends on the cycling region.…”

Section: Interpretation Of the Stochastic Componentmentioning

confidence: 99%

See 2 more Smart Citations

Deterministic and stochastic features of rhythmic human movement

2005

View full text Add to dashboard Cite

The dynamics of rhythmic movement has both deterministic and stochastic features. We advocate a recently established analysis method that allows for an unbiased identification of both types of system components. The deterministic components are revealed in terms of drift coefficients and vector fields, while the stochastic components are assessed in terms of diffusion coefficients and ellipse fields. The general principles of the procedure and its application are explained and illustrated using simulated data from known dynamical systems. Subsequently, we exemplify the method's merits in extracting deterministic and stochastic aspects of various instances of rhythmic movement, including tapping, wrist cycling and forearm oscillations. In particular, it is shown how the extracted numerical forms can be analysed to gain insight into the dependence of dynamical properties on experimental conditions.

show abstract

Section: Extraction Proceduresmentioning

confidence: 99%

Section: Interpretation Of the Stochastic Componentmentioning

confidence: 99%

Section: Interpretation Of the Stochastic Componentmentioning

confidence: 99%

See 1 more Smart Citation

Deterministic and stochastic features of rhythmic human movement

2005

View full text Add to dashboard Cite

show abstract

“…The signals acquired from force sensors [12,13], accelerometers [14], spindle drive currents [15], microphones (audible sound signals) [16], and acoustic emissions [17] are examined for the analysis of cutting dynamics.…”

Section: Introductionmentioning

confidence: 99%

Investigating the fluctuations in tool vibration during GFRP drilling through recurrence quantification analysis

Jessy¹,

Dinakaran

Kumar³

2015

J Mech Sci Technol

View full text Add to dashboard Cite

Glass fiber-reinforced polymers (GFRPs) are a unique class of materials in machining because unlike metals, they are not homogeneous. Given the inhomogeneous and anisotropic nature of composite materials, their machining behavior differs in many respects from that of metal machining. With metallic materials, the cutting force is uniform during drilling. Given GFRPs, drill bits are subject to variable cutting forces, whose responses to machining vary significantly. During GFRP drilling, heavy vibration is undesirable and results in rapid tool wear. The cutting condition can be controlled effectively to avoid heavy vibration by applying the appropriate cutting parameters. Hence, the most suitable cutting condition must be selected for proper process control. This study investigates the influence of vibration as a result of changes in cutting condition, drill diameter, and the number of holes. This influence is continuously monitored by the vibration signals generated during GFRP drilling with a TiN/TiALN-coated carbide drill bit. Experiments were conducted on a computer numerical control milling machine. The input parameters used for various cutting conditions were spindle speed and feed. The vibration signal was measured with an accelerometer, and its fluctuations were examined by a non-linear technique called recurrence quantification analysis (RQA). The influence of these fluctuations on vibration and their effect on tool life were also determined using RQA parameters. These parameters include percent recurrence, percent determinism, and percent laminarity. They have been used to study the effect of continuous fluctuations in vibration signals during drilling. This study confirmed that the RQA technique has potential for use in the investigation of a dynamical system.

show abstract

“…Extensive research using different sensor signals and various signal processing techniques has been performed on chatter detection. Signals acquired from force sensors [1,2], accelerometers [3], spindle drive current [4], audible sound signal from a microphone [5], acoustic emission signals [6] are used for study and analysis of cutting dynamics. Factors that decide the sensor selection are bandwidth, sensitivity, signal to noise ratio, and sensor placement.…”

Section: Introductionmentioning

confidence: 99%

Permutation entropy based real-time chatter detection using audio signal in turning process

Nair

Krishna

Namboothiri

et al. 2009

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Machine tool chatter is an unfavorable phenomenon during metal cutting, which results in heavy vibration of cutting tool. With increase in depth of cut, the cutting regime changes from chatter-free cutting to one with chatter. In this paper, we propose the use of permutation entropy (PE), a conceptually simple and computationally fast measurement to detect the onset of chatter from the time series using sound signal recorded with a unidirectional microphone. PE can efficiently distinguish the regular and complex nature of any signal and extract information about the dynamics of the process by indicating sudden change in its value. Under situations where the data sets are huge and there is no time for preprocessing and fine-tuning, PE can effectively detect dynamical changes of the system. This makes PE an ideal choice for online detection of chatter, which is not possible with other conventional nonlinear methods. In the present study, the variation of PE under two cutting conditions is analyzed. Abrupt variation in the value of PE with increase in depth of cut indicates the onset of chatter vibrations. The results are verified using frequency spectra of the signals and the nonlinear measure, normalized coarse-grained information rate (NCIR).

show abstract

Qualitative and Quantitative Analysis of Stochastic Processes Based on Measured Data, Ii: Applications to Experimental Data

Cited by 13 publications

References 13 publications

Deterministic and stochastic features of rhythmic human movement

Deterministic and stochastic features of rhythmic human movement

Investigating the fluctuations in tool vibration during GFRP drilling through recurrence quantification analysis

Permutation entropy based real-time chatter detection using audio signal in turning process

Contact Info

Product

Resources

About