On-Line Chatter Detection Using Wavelet-Based Parameter Estimation

2000 ◽  
Author(s):  
Taejun Choi ◽  
Yung C. Shin
Keyword(s):  
Parameter Estimation ◽  
Estimation Algorithm ◽  
Cutting Process ◽  
High Dimensional ◽  
Detection Accuracy ◽  
Chatter Detection ◽  
Ml Estimation ◽  
On Line ◽  
Low Dimensional ◽  
Low Dimensional Dynamics

Abstract A new method for on-line chatter detection is presented. The proposed method characterizes the significant transition from high dimensional to low dimensional dynamics in the cutting process at the onset of chatter. Based on the likeness of the cutting process to the nearly-1/f process, this wavelet-based maximum likelihood (ML) estimation algorithm is applied for on-line chatter detection. The presented chatter detection index γ is independent of the cutting conditions and gives excellent detection accuracy and permissible computational efficiency, which makes it suitable for on-line implementation. The validity of the proposed method is demonstrated through the tests with extensive actual data obtained from turning and milling processes.

On-Line Chatter Detection Using Wavelet-Based Parameter Estimation

2003 ◽  
Vol 125 (1) ◽  
pp. 21-28 ◽  
Author(s):  
Taejun Choi ◽  
Yung C. Shin
Keyword(s):  
Parameter Estimation ◽  
Estimation Algorithm ◽  
High Dimensional ◽  
Detection Accuracy ◽  
Chatter Detection ◽  
Ml Estimation ◽  
Fractal Patterns ◽  
On Line ◽  
Low Dimensional ◽  
Low Dimensional Dynamics

A new method for on-line chatter detection is presented. The proposed method characterizes the significant transition from high dimensional to low dimensional dynamics in the cutting process at the onset of chatter. Based on the observation that cutting signals contain fractal patterns, a wavelet-based maximum likelihood (ML) estimation algorithm is applied to on-line chatter detection. The presented chatter detection index γ is independent of the cutting conditions and gives excellent detection accuracy and permissible computational efficiency, which makes it suitable for on-line implementation. The validity of the proposed method is demonstrated through the tests with extensive actual data obtained from turning and milling processes.

On-Line Grinding Chatter Detection Based on Minimum Entropy Deconvolution and Autocorrelation Function

2021 ◽  
Author(s):  
Dan He ◽  
Zexing Ni ◽  
Xiufeng Wang
Keyword(s):  
Autocorrelation Function ◽  
Line Detection ◽  
Detection Accuracy ◽  
Minimum Entropy ◽  
Chatter Detection ◽  
Vibration Signals ◽  
Grinding Chatter ◽  
Grinding Conditions ◽  
On Line ◽  
Periodic Impulse

Abstract On-line detection of chatter is one of the key techniques to avoid the harmful effects caused by chatter in grinding process. The key to chatter detection is to capture reliable chatter features and thresholds. To achieve this, it is important to make clear and extract the essential characteristics of the grinding chatter signal, which has not yet been well studied. In this paper, we are going to investigate the essential characteristics of the grinding chatter signal and propose a new approach for on-line detection of grinding chatter. The proposed approach for on-line detection of grinding chatter is based on minimum entropy deconvolution and autocorrelation function, in which the minimum entropy deconvolution is employed to deconvolve the effect of transmission path, and further to restore the essential characteristics of the chatter signals. To eliminate the interference of the non-periodic impulse signals in the measured vibration signals, an autocorrelation function is introduced. Kurtosis is employed to indicate chatter according to the changes of the processed signal. The validity of the proposed method is demonstrated through the measured vibration signals obtained from grinding processes and the presented chatter detection index is independent from the grinding conditions with excellent detection accuracy and permissible computational efficiency. This demonstrates the effectiveness of proposed method in on-line implementation.

DETECTING LOW DIMENSIONAL DYNAMICS IN BIOLOGICAL EXPERIMENTS

1996 ◽  
Vol 07 (04) ◽  
pp. 429-435 ◽  
Author(s):  
XING PEI ◽  
FRANK MOSS
Keyword(s):  
Action Potentials ◽  
Sensory System ◽  
Random Processes ◽  
High Dimensional ◽  
Time Intervals ◽  
Dynamical Behaviors ◽  
Caudal Photoreceptor ◽  
The Times ◽  
Low Dimensional ◽  
Low Dimensional Dynamics

We discuss the well-known problems associated with efforts to detect and characterize chaos and other low dimensional dynamics in biological settings. We propose a new method which shows promise for addressing these problems, and we demonstrate its effectiveness in an experiment with the crayfish sensory system. Recordings of action potentials in this system are the data. We begin with a pair of assumptions: that the times of firings of neural action potentials are largely determined by high dimensional random processes or “noise”; and that most biological files are non stationary, so that only relatively short files can be obtained under approximately constant conditions. The method is thus statistical in nature. It is designed to recognize individual “events” in the form of particular sequences of time intervals between action potentials which are the signatures of certain well defined dynamical behaviors. We show that chaos can be distinguished from limit cycles, even when the dynamics is heavily contaminated with noise. Extracellular recordings from the crayfish caudal photoreceptor, obtained while hydrodynamically stimulating the array of hair receptors on the tailfan, are used to illustrate the method.

Predicting Time Series from Short-Term High-Dimensional Data

2014 ◽  
Vol 24 (12) ◽  
pp. 1430033 ◽  
Author(s):  
Huanfei Ma ◽  
Tianshou Zhou ◽  
Kazuyuki Aihara ◽  
Luonan Chen
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
High Dimensional Data ◽  
Series Data ◽  
High Dimensional ◽  
Short Term ◽  
Low Dimensional ◽  
Low Dimensional Dynamics ◽  
Term Data

The prediction of future values of time series is a challenging task in many fields. In particular, making prediction based on short-term data is believed to be difficult. Here, we propose a method to predict systems' low-dimensional dynamics from high-dimensional but short-term data. Intuitively, it can be considered as a transformation from the inter-variable information of the observed high-dimensional data into the corresponding low-dimensional but long-term data, thereby equivalent to prediction of time series data. Technically, this method can be viewed as an inverse implementation of delayed embedding reconstruction. Both methods and algorithms are developed. To demonstrate the effectiveness of the theoretical result, benchmark examples and real-world problems from various fields are studied.

scholarly journals A spiral attractor network drives rhythmic locomotion

2017 ◽  
Author(s):  
Angela M. Bruno ◽  
William N. Frost ◽  
Mark D. Humphries
Keyword(s):  
Movement Control ◽  
High Dimensional ◽  
Fictive Locomotion ◽  
Pedal Ganglion ◽  
Joint Activity ◽  
Population Activity ◽  
Neural Populations ◽  
Low Dimensional ◽  
Circuit Function ◽  
Low Dimensional Dynamics

AbstractThe joint activity of neural populations is high dimensional and complex. One strategy for reaching a tractable understanding of circuit function is to seek the simplest dynamical system that can account for the population activity. By imaging Aplysia’s pedal ganglion during fictive locomotion, here we show that its population-wide activity arises from a low-dimensional spiral attractor. Evoking locomotion moved the population into a low-dimensional, periodic, decaying orbit −a spiral −in which it behaved as a true attractor, converging to the same orbit when evoked, and returning to that orbit after transient perturbation. We found the same attractor in every preparation, and could predict motor output directly from its orbit, yet individual neurons’ participation changed across consecutive locomotion bouts. From these results, we propose that only the low-dimensional dynamics for movement control, and not the high-dimensional population activity, are consistent within and between nervous systems.

scholarly journals A spiral attractor network drives rhythmic locomotion

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Angela M Bruno ◽  
William N Frost ◽  
Mark D Humphries
Keyword(s):  
Movement Control ◽  
High Dimensional ◽  
Fictive Locomotion ◽  
Pedal Ganglion ◽  
Joint Activity ◽  
Population Activity ◽  
Neural Populations ◽  
Low Dimensional ◽  
Circuit Function ◽  
Low Dimensional Dynamics

The joint activity of neural populations is high dimensional and complex. One strategy for reaching a tractable understanding of circuit function is to seek the simplest dynamical system that can account for the population activity. By imaging Aplysia’s pedal ganglion during fictive locomotion, here we show that its population-wide activity arises from a low-dimensional spiral attractor. Evoking locomotion moved the population into a low-dimensional, periodic, decaying orbit - a spiral - in which it behaved as a true attractor, converging to the same orbit when evoked, and returning to that orbit after transient perturbation. We found the same attractor in every preparation, and could predict motor output directly from its orbit, yet individual neurons’ participation changed across consecutive locomotion bouts. From these results, we propose that only the low-dimensional dynamics for movement control, and not the high-dimensional population activity, are consistent within and between nervous systems.

scholarly journals Mapping Low-Dimensional Dynamics to High-Dimensional Neural Activity: A Derivation of the Ring Model From the Neural Engineering Framework

2021 ◽  
Vol 33 (3) ◽  
pp. 827-852
Author(s):  
Omri Barak ◽  
Sandro Romani
Keyword(s):  
Neural Network ◽  
Neural Activity ◽  
Failure Modes ◽  
Neural Population ◽  
High Dimensional ◽  
Angular Variable ◽  
Neural Engineering ◽  
Ring Model ◽  
Low Dimensional ◽  
Low Dimensional Dynamics

Empirical estimates of the dimensionality of neural population activity are often much lower than the population size. Similar phenomena are also observed in trained and designed neural network models. These experimental and computational results suggest that mapping low-dimensional dynamics to high-dimensional neural space is a common feature of cortical computation. Despite the ubiquity of this observation, the constraints arising from such mapping are poorly understood. Here we consider a specific example of mapping low-dimensional dynamics to high-dimensional neural activity—the neural engineering framework. We analytically solve the framework for the classic ring model—a neural network encoding a static or dynamic angular variable. Our results provide a complete characterization of the success and failure modes for this model. Based on similarities between this and other frameworks, we speculate that these results could apply to more general scenarios.

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