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 -