Monitoring linear antenna arrays using an exponentially weighted moving average-based fault detection scheme

Abstract

The evolution of modern wireless communications systems has dramatically increased the demand for antenna arrays. An antenna array with certain radiation characteristics is often desired. However, the actual radiation pattern of an antenna array changes when faults are introduced in the array. In this paper a statistical fault detection methodology based on the exponentially weighted moving average (EWMA) control scheme is proposed to detect possible faulty radiation patterns in linear antenna arrays. The proposed method detects the faults based on deviation in the radiation pattern from the desired ones. The difference between synthesized radiation pattern obtained using the Minimax algorithm and the measured pattern can be used as an indicator about the existence or absence of faults. To assess the fault detection abilities of the EWMA control scheme, three case studies are considered, one involving a complete failure in one element in the array, one involving partial failure in two elements, and one involving degradation caused by random noise due to interference and other factors. The simulation results for all cases show the effectiveness of the proposed EWMA fault detection method.

A statistical-based approach for fault detection and diagnosis in a photovoltaic system

“…EWMA's has been originally introduced by Roberts et al in [15], then it has been significantly applied in time series analysis [16,17]. Overall several years, EWMA control chart has been applied by lot of engineers and scientists from various areas [18,19].…”

Early detection of abnormal patient arrivals at hospital emergency department

“…Also, CUSUM is relatively slow to respond to large shifts. Therefore, EWMA-based charts are an appropriate monitoring scheme to be adopted when dealing with individual observations [26], [22]. According to the literature, EWMA is one of the most frequently used control charts for monitoring autocorrelated processes because of its flexibility (allows suitable parameters to be selected to achieve the highest possible performance) and sensitivity to small shifts.…”

“…In other words, a control chart is a picture of a process over time that helps to identify the state of the monitored process, i.e., either it is running satisfactorily or not [18]. In such framework, numerous control charts have been developed to monitor a mean of process variable over time, and include the Shewhart chart [19], the cumulative summation or CUSUM chart [20], and the EWMA [21], [22], [23]. Over many decades, the primary utilization of control charts was focused on industrial quality control applications.…”

Properties and Applications of Error Coefficient Matrix in Linear Antenna Array Design

“…In the recent past, several methods have been proposed for detection of such faults. Methods based on Genetic Algorithms (GA) [2], Neural Networks (NN) [3], Bacterial Foraging Optimization (BFO) [4], Bayesian Compressive Sensing [5], Exponentially Weighted Moving Average Scheme (EWMA) [6], etc. were proposed for fault detection, but no method has been proposed for precise location of the error in the array.…”