Deep Learning based Condition Monitoring approach applied to Power Quality

Gonzalez-Abreu, Artvin-Darien; Saucedo-Dorantes, Juan José; Osornio-Ríos, Roque Alfredo; Arellano-Espitia, Francisco; Delgado-Prieto, Miguel

doi:10.1109/etfa46521.2020.9212076

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…We have decided to automate the whole procedure further using an artificial intelligence model. Deep learning and machine learning are widely used for time series analysis in the I4.0 concept solutions [19][20][21], thus, we have designed and trained our own neural network model for the micro-interruption, or even other anomaly states, detection as well. We used the publicly available part of the data in the experiments, and the created training and testing datasets are publicly available alongside the raw data.…”

Section: Experiments Designmentioning

confidence: 99%

Collecting and Pre-Processing Data for Industry 4.0 Implementation Using Hydraulic Press

Hercík

Svoboda

2023

Data

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More and more activities are being undertaken to implement the Industry 4.0 concept in industrial practice. One of the biggest challenges is the digitization of existing industrial systems and heavy industry operations, where there is huge potential for optimizing and managing these processes more efficiently, but this requires collecting large amounts of data, understanding, and evaluating it so that we can add value back based on it. This paper focuses on the collection, local pre-processing of data, and its subsequent transfer to the cloud from an industrial hydraulic press to create a comprehensive dataset that forms the basis for further digitization of the operation. The novelty lies mainly in the process of data collection and pre-processing in the framework of edge computing of large amounts of data. In the data pre-processing, data normalization methods are applied, which allow the data to be logically sorted, tagged, and linked, which also allows the data to be efficiently compressed, thus, dynamically creating a complex dataset for later use in the process digitization.

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Section: Experiments Designmentioning

confidence: 99%

Collecting and Pre-Processing Data for Industry 4.0 Implementation Using Hydraulic Press

Hercík

Svoboda

2023

Data

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Deep Learning in Intelligent Power and Energy Systems

Mota

Pinto

Vale

et al. 2022

Intelligent Data Mining and Analysis in Power and Energy Systems

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Optimal Data Reduction of Training Data in Machine Learning-Based Modelling: A Multidimensional Bin Packing Approach

2022

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In these days, when complex, IT-controlled systems have found their way into many areas, models and the data on which they are based are playing an increasingly important role. Due to the constantly growing possibilities of collecting data through sensor technology, extensive data sets are created that need to be mastered. In concrete terms, this means extracting the information required for a specific problem from the data in a high quality. For example, in the field of condition monitoring, this includes relevant system states. Especially in the application field of machine learning, the quality of the data is of significant importance. Here, different methods already exist to reduce the size of data sets without reducing the information value. In this paper, the multidimensional binned reduction (MdBR) method is presented as an approach that has a much lower complexity in comparison on the one hand and deals with regression, instead of classification as most other approaches do, on the other. The approach merges discretization approaches with non-parametric numerosity reduction via histograms. MdBR has linear complexity and can be facilitated to reduce large multivariate data sets to smaller subsets, which could be used for model training. The evaluation, based on a dataset from the photovoltaic sector with approximately 92 million samples, aims to train a multilayer perceptron (MLP) model to estimate the output power of the system. The results show that using the approach, the number of samples for training could be reduced by more than 99%, while also increasing the model’s performance. It works best with large data sets of low-dimensional data. Although periodic data often include the most redundant samples and thus provide the best reduction capabilities, the presented approach can only handle time-invariant data and not sequences of samples, as often done in time series.

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Deep Learning based Condition Monitoring approach applied to Power Quality

Cited by 3 publications

References 8 publications

Collecting and Pre-Processing Data for Industry 4.0 Implementation Using Hydraulic Press

Collecting and Pre-Processing Data for Industry 4.0 Implementation Using Hydraulic Press

Deep Learning in Intelligent Power and Energy Systems

Optimal Data Reduction of Training Data in Machine Learning-Based Modelling: A Multidimensional Bin Packing Approach

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