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

机译：时变扰动同时估计的基于小波的导电纤维故障检测和RLS参数估计

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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.
机译：本文提出了一种基于小波的可变故障检测算法，为实现软，硬，超硬故障检测提供了可能。所提出的算法基于在后退时间范围内信号的局部Lipschitz常数的方差的估计。如果局部Lipschitz常数位于计算边界之外，则识别出故障（异常值）。目前，要估算纳米范围内的参数。因此，输入信号需要非常高的频率，随后需要非常高的采样率。在生产过程中，使用在线识别模型，使用改进的递归最小二乘（RLS）方法估计导电复丝纤维的参数，包括使用输入输出比例因子的纳米范围内的电感。相反，该技术使用较宽的采样率和低频输入信号来识别表征线性模型的参数。此方法用于提供缩放的标识带宽以及降低的采样率。通过按比例缩放输入输出数据，获得了识别技术的通用模型，以估算制造过程中随时间变化的正弦干扰信号。在我们的贡献中，时变正弦扰动被认为是在幅度和频率方面。使用改进的最小二乘法（LSM）获得标识。在这种系统中，电感代表要估计的最关键参数。检验结果表明，所提出的RLS算法方法使用遗忘因子，是估算时变正弦干扰以及电感的有用方法。

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