The Experimental Verification of Direct-Write Silver Conductive Grid and ARIMA Time Series Analysis for Crack Propagation

Abstract

The paper presents experimental verification of customized resistive crack propagation sensors as an alternative method for other common structural health monitoring (SHM) techniques. Most of these are sensitive to changes in the sensor network configuration and a baseline dataset must be collected for the analysis of the structure condition. Sensors investigated within the paper are manufactured by the direct-write process with electrically conductive, silver-microparticle-filled paint to prepare a tailored measuring grid on an epoxy or polyurethane coating as a driving/insulating layer. This method is designed to enhance the functionality and usability compared to commercially available crack gauges. By using paint with conductive metal particles, the shape of the sensor measuring grid can be more easily adapted to the structure, while, in the previous approach, only a few grid-fixed sensors are available. A fatigue test on the compact tension (CT) specimen is presented and discussed to evaluate the ability of the developed sensors to detect and monitor fatigue cracks. Additionally, the ARIMA time series algorithm is developed both for monitoring and predicting crack growth, based on the acquired data. The proposed sensors’ verification reveal their good performance to detect and monitor fatigue fractures with a relatively low measurement error and ARIMA estimated crack length compared with the crack opening displacement (COD) gauge.

Machine Fault Detection Using a Hybrid CNN-LSTM Attention-Based Model

“…Among the time series forecasting models, there are several approaches such as neurofuzzy systems [44], autoregressive integrated moving average (ARIMA) [45], LSTM [46], ensemble learning methods [47], and TFT [48]. According to Li et al [49], the TFT can improve the reliability and compactness of the forecasting and can even be applied to medium-term hourly time series data.…”

The Extraction of Copper from Chalcopyrite Concentrate with Hydrogen Peroxide in Sulfuric Acid Solution

“…There is a proven application of machine learning concepts in research conducted in the area of material characterization [51], which creates the possibility of application in the area of research considered in this paper. Since the majority of the obtained results presented in the previous considerations are made by different analyses based on the use of time series, there is great potential for the use of predictive methods in order to obtain appropriate forecast values over the data presented by those time series [52,53].…”