Removing spikes while preserving data and noise using wavelet filter banks

Abstract

Many diagnostic datasets suffer from the adverse effects of spikes that are embedded in data and noise. For example, this is true for electrical power system data where the switches, relays, and inverters are major contributors to these effects. Spikes are mostly harmful to the analysis of data in that they throw off real-time detection of abnormal conditions, and classification of faults. Since noise and spikes are mixed together and embedded within the data, removal of the unwanted signals from the data is not always easy and may result in losing the integrity of the information carried by the data. Additionally, in some applications noise and spikes need to be filtered independently. The proposed algorithm is a multi-resolution filtering approach based on Haar wavelets that is capable of removing spikes while incurring insignificant damage to other data. In particular, noise in the data, which is a useful indicator that a sensor is healthy and not stuck, can be preserved using our approach. Presented here is the theoretical background with some examples from a realistic testbed. 1 2

Digital Signal Processing for Analytics in Biostatistics & Biometric Applications

“…This study aims at reducing the dimensionality of dataset to reduce computational load in further processing [2]. The proposed method ranks features for learning a distance function in order to capture the semantics of the dataset [3].…”

Advanced Image Processing for Analytics in Biomedicine and Bioscience

“…Given a large set of images but few observations, an obvious idea is to reduce the degrees of freedom in the measurements by representing them with a smaller set of more "condensed" variables. This amounts to reducing the dimensionality of image dataset to reduce computational load in further processing [2].…”

Filtering out spikes from sensors in power converters system using discrete wavelet transform

“…The methodology uses the discrete DWT to generate a feature space where the spike is detected and removed; and the inverse DWT to reconstruct spike free measurements. A multi-resolution filtering approach presented in [1] suppresses measurement spikes in an electric power system. In [2] a technique is given for removing spikes based on identifying a specific pattern and then subtracting a fitted spike from the original unprocessed signal.…”