Wavelet based Fault Detection and RLS Parameter Estimation of Conductive Fibers with a Simultaneous Estimation of Time-Varying Disturbance

Research output: Journal contributionsConference article in journalResearchpeer-review

Standard

Wavelet based Fault Detection and RLS Parameter Estimation of Conductive Fibers with a Simultaneous Estimation of Time-Varying Disturbance. / Schimmack, Manuel; McGaw, David; Mercorelli, Paolo.
In: IFAC-PapersOnLine, Vol. 48, No. 3, 01.05.2015, p. 1773-1778.

Research output: Journal contributionsConference article in journalResearchpeer-review

Harvard

APA

Vancouver

Bibtex

@article{ae5040a8ae524362b47b1ffdf14316b2,
title = "Wavelet based Fault Detection and RLS Parameter Estimation of Conductive Fibers with a Simultaneous Estimation of Time-Varying Disturbance",
abstract = "This paper presents a method for a variable fault detection algorithm, based on wavelets, and offers the possibility to realize soft, hard and very hard fault detection. The proposed algorithm is based on the estimation of the variance of the local Lipschitz constant of the signal over a receding time horizon. The fault (outlier) is recognized if the local Lipschitz constant lies outside the computed boundary. Currently, to estimate parameters in the nano range. Thus the input signal requires a very high frequency, which subsequently requires a very high sampling rate. A modified Recursive Least Squares (RLS) method was used to estimate parameters of conductive multifilament fibers using on-line identification model during the manufacturing process, including inductance, within the nano range, using input-output scaling factors. In contrast, this technique uses a broader sampling rate and an input signal with low frequency to identify the parameters characterizing the linear model. This method was used to provide a scaled identification bandwidth together with a reduced sampling rate. Through the scaling of the input-output data, a general model of the identification technique was obtained to estimate time-varying sinusoidal disturbance signal during the manufacturing process. In our contribution a time-varying sinusoidal disturbance is considered in terms of magnitude and frequency. The identification is obtained using modified Least Squares Methods (LSM). In this kind of system, the inductance represents the most critical parameter to be estimated. The examined results indicated that the proposed RLS algorithm method, using a forgetting factor, is a useful method for estimating time-varying sinusoidal disturbances as well as the inductance.",
keywords = "Engineering, ARMA parameter estimation, Disturbance signals, Fault detection, Recursive least squares, SISO",
author = "Manuel Schimmack and David McGaw and Paolo Mercorelli",
year = "2015",
month = may,
day = "1",
doi = "10.1016/j.ifacol.2015.06.343",
language = "English",
volume = "48",
pages = "1773--1778",
journal = "IFAC-PapersOnLine",
issn = "2405-8971",
publisher = "Elsevier B.V.",
number = "3",
note = "15th IFAC Symposium on Information Control Problems in Manufacturing - INCOM 2015, INCOM Symposium 2015 ; Conference date: 11-05-2015 Through 13-05-2015",
url = "https://www.ifac-control.org/events/information-control-problems-in-manufacturing-15th-incom-2015",

}

RIS

TY - JOUR

T1 - Wavelet based Fault Detection and RLS Parameter Estimation of Conductive Fibers with a Simultaneous Estimation of Time-Varying Disturbance

AU - Schimmack, Manuel

AU - McGaw, David

AU - Mercorelli, Paolo

N1 - Conference code: 15

PY - 2015/5/1

Y1 - 2015/5/1

N2 - This paper presents a method for a variable fault detection algorithm, based on wavelets, and offers the possibility to realize soft, hard and very hard fault detection. The proposed algorithm is based on the estimation of the variance of the local Lipschitz constant of the signal over a receding time horizon. The fault (outlier) is recognized if the local Lipschitz constant lies outside the computed boundary. Currently, to estimate parameters in the nano range. Thus the input signal requires a very high frequency, which subsequently requires a very high sampling rate. A modified Recursive Least Squares (RLS) method was used to estimate parameters of conductive multifilament fibers using on-line identification model during the manufacturing process, including inductance, within the nano range, using input-output scaling factors. In contrast, this technique uses a broader sampling rate and an input signal with low frequency to identify the parameters characterizing the linear model. This method was used to provide a scaled identification bandwidth together with a reduced sampling rate. Through the scaling of the input-output data, a general model of the identification technique was obtained to estimate time-varying sinusoidal disturbance signal during the manufacturing process. In our contribution a time-varying sinusoidal disturbance is considered in terms of magnitude and frequency. The identification is obtained using modified Least Squares Methods (LSM). In this kind of system, the inductance represents the most critical parameter to be estimated. The examined results indicated that the proposed RLS algorithm method, using a forgetting factor, is a useful method for estimating time-varying sinusoidal disturbances as well as the inductance.

AB - This paper presents a method for a variable fault detection algorithm, based on wavelets, and offers the possibility to realize soft, hard and very hard fault detection. The proposed algorithm is based on the estimation of the variance of the local Lipschitz constant of the signal over a receding time horizon. The fault (outlier) is recognized if the local Lipschitz constant lies outside the computed boundary. Currently, to estimate parameters in the nano range. Thus the input signal requires a very high frequency, which subsequently requires a very high sampling rate. A modified Recursive Least Squares (RLS) method was used to estimate parameters of conductive multifilament fibers using on-line identification model during the manufacturing process, including inductance, within the nano range, using input-output scaling factors. In contrast, this technique uses a broader sampling rate and an input signal with low frequency to identify the parameters characterizing the linear model. This method was used to provide a scaled identification bandwidth together with a reduced sampling rate. Through the scaling of the input-output data, a general model of the identification technique was obtained to estimate time-varying sinusoidal disturbance signal during the manufacturing process. In our contribution a time-varying sinusoidal disturbance is considered in terms of magnitude and frequency. The identification is obtained using modified Least Squares Methods (LSM). In this kind of system, the inductance represents the most critical parameter to be estimated. The examined results indicated that the proposed RLS algorithm method, using a forgetting factor, is a useful method for estimating time-varying sinusoidal disturbances as well as the inductance.

KW - Engineering

KW - ARMA parameter estimation

KW - Disturbance signals

KW - Fault detection

KW - Recursive least squares

KW - SISO

UR - http://www.scopus.com/inward/record.url?scp=84953854637&partnerID=8YFLogxK

U2 - 10.1016/j.ifacol.2015.06.343

DO - 10.1016/j.ifacol.2015.06.343

M3 - Conference article in journal

AN - SCOPUS:84953854637

VL - 48

SP - 1773

EP - 1778

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

SN - 2405-8971

IS - 3

T2 - 15th IFAC Symposium on Information Control Problems in Manufacturing - INCOM 2015

Y2 - 11 May 2015 through 13 May 2015

ER -

Recently viewed

Publications

  1. Vision-Based Deep Learning Algorithm for Detecting Potholes
  2. Design of a Real Time Path of Motion Using a Sliding Mode Control with a Switching Surface
  3. A Python toolbox for the numerical solution of the Maxey-Riley equation
  4. Towards a Dynamic Interpretation of Subjective and Objective Values
  5. Using haar wavelets for fault detection in technical processes
  6. Analysis and Implementation of a Resistance Temperature Estimator Based on Bi-Polynomial Least Squares Method and Discrete Kalman Filter
  7. Inversion of fuzzy neural networks for the reduction of noise in the control loop
  8. Identification of structure-biodegradability relationships for ionic liquids - clustering of a dataset based on structural similarity
  9. Linux-based Embedded System for Wavelet Denoising and Monitoring of sEMG Signals using an Axiomatic Seminorm
  10. Applied quality assurance methods under the open source development model
  11. Real-time RDF extraction from unstructured data streams
  12. Analysis of PI controllers with anti-windup techniques on level systems
  13. Sliding-Mode-Based Input-Output Linearization of a Peltier Element for Ice Clamping Using a State and Disturbance Observer
  14. Approximate tree kernels
  15. Mathematical Modeling for Robot 3D Laser Scanning in Complete Darkness Environments to Advance Pipeline Inspection
  16. Application of design of experiments for laser shock peening process optimization
  17. Intraspecific trait variation increases species diversity in a trait-based grassland model
  18. Legitimizing Digital Transformation: From System Integration to Platformization
  19. Using data mining techniques to investigate the correlation between surface cracks and flange lengths in deep drawn sheet metals
  20. Quantification of amino acids in fermentation media by isocratic HPLC analysis of their
  21. Comparing temperature data sources for use in species distribution models
  22. Clashing Values
  23. Assessment of cognitive load in multimedia learning with dual-task methodology
  24. The Practical Significance of History: When and How History Can Be Used for Institutional Change
  25. Sliding Mode Control Strategies for Maglev Systems Based on Kalman Filtering
  26. Pluralism and diversity: Trends in the use and application of ordination methods 1990-2007
  27. Recontextualizing Anthropomorphic Metaphors in Organization Studies
  28. Individual Differences in Infants' Speech Segmentation Performance
  29. Efficacy of a web-based intervention with and without guidance for employees with risky drinking