An OPC UA Machine Learning Server for Automated Guided Vehicle

Cupek, Rafał; Gólczyński, Łukasz; Ziębiński, Adam

doi:10.1007/978-3-030-28374-2_19

Cited by 5 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AIUT Ltd. (Gliwice, Poland) has developed a solution for monitoring and diagnosing industrial equipment that is based on the OPC UA standard [17,20], which facilitates access to all of the process data that are generated by immersive devices. This development uses PLCs as the source of input data for the OPC UA training server.…”

Section: Collecting and Analyzing The Agv Datamentioning

confidence: 99%

“…Publications [17,20,55] provide a detailed description of the developed OPC UA ML server, which provides access to the data that are generated, processed, and used by ANNs for ML algorithms. Due to the immutability of the data descriptor and the way in which the information is presented, the server ensures a stable connection if the internal data structure of the ANN changes.…”

Section: Collecting the Agv Datamentioning

confidence: 99%

“…The OPCUA of the client [17][18][19][20]; • Data normalization methods [21][22][23]; • Algorithms for detecting and restoring any partially lost data [24][25][26][27]; • Methods of correlation analysis [28][29][30]; • Learning and forecasting using neural networks [31][32][33][34][35][36][37][38].…”

mentioning

confidence: 99%

See 2 more Smart Citations

A Novel Methodology Based on a Deep Neural Network and Data Mining for Predicting the Segmental Voltage Drop in Automated Guided Vehicle Battery Cells

Pavliuk,

Cupek,

Steclik

et al. 2023

Electronics

Self Cite

View full text Add to dashboard Cite

AGVs are important elements of the Industry 4.0 automation process. The optimization of logistics transport in production environments depends on the economical use of battery power. In this study, we propose a novel deep neural network-based method and data mining for predicting segmented AGV battery voltage drop. The experiments were performed using data from the Formica 1 AGV of AIUT Ltd., Gliwice, Poland. The data were converted to a one-second resolution according to the OPCUA open standard. Pre-processing involved using an analysis of variance to detect any missing data. To do this, the standard deviation, variance, minimum and maximum values, range, linear deviation, and standard deviation were calculated for all of the permitted sigma values in one percent increments. Data with a sigma exceeding 1.5 were considered missing and replaced with a smoothed moving average. The correlation dependencies between the predicted signals were determined using the Pearson, Spearman, and Kendall correlation coefficients. Training, validation, and test sets were prepared by calculating additional parameters for each segment, including the count number, duration, delta voltage, quality, and initial segment voltage, which were classified into static and dynamic categories. The experiments were performed on the hidden layer using different numbers of neurons in order to select the best architecture. The length of the “time window” was also determined experimentally and was 12. The MAPE of the short-term forecast of seven segments and the medium-term forecast of nine segments were 0.09% and 0.18%, respectively. Each study duration was up to 1.96 min.

show abstract

Section: Collecting and Analyzing The Agv Datamentioning

confidence: 99%

Section: Collecting the Agv Datamentioning

confidence: 99%