Challenges Associated with Sensors and Data Fusion for AGV-Driven Smart Manufacturing

Appropriate maintenance of industrial equipment keeps production systems in good health and ensures the stability of production processes. In specific production sectors, such as the electrical power industry, equipment failures are rare but may lead to high costs and substantial economic losses not only for the power plant but for consumers and the larger society. Therefore, the power production industry relies on a variety of approaches to maintenance tasks, ranging from traditional solutions and engineering know-how to smart, AI-based analytics to avoid potential downtimes. This review shows the evolution of maintenance approaches to support maintenance planning, equipment monitoring and supervision. We present older techniques traditionally used in maintenance tasks and those that rely on IT analytics to automate tasks and perform the inference process for failure detection. We analyze prognostics and health-management techniques in detail, including their requirements, advantages and limitations. The review focuses on the power-generation sector. However, some of the issues addressed are common to other industries. The article also presents concepts and solutions that utilize emerging technologies related to Industry 4.0, touching on prescriptive analysis, Big Data and the Internet of Things. The primary motivation and purpose of the article are to present the existing practices and classic methods used by engineers, as well as modern approaches drawing from Artificial Intelligence and the concept of Industry 4.0. The summary of existing practices and the state of the art in the area of predictive maintenance provides two benefits. On the one hand, it leads to improving processes by matching existing tools and methods. On the other hand, it shows researchers potential directions for further analysis and new developments.

Section: Industrial Internet Of Thingsmentioning

confidence: 99%

“…Edge computing [274]-this allows for data processing and computations close to the device (for example, performing the Wavelet transform, the Fast Fourier transform or data merging and aggregation [266]). This approach handles data velocity but has limited storage, passing collected and computed data for further processing.…”

mentioning

confidence: 99%

From Corrective to Predictive Maintenance—A Review of Maintenance Approaches for the Power Industry

Moleda

Małysiak-Mrozek

Ding

et al. 2023

“…The structure of the single memory cell is presented in Figure 4. The ℎ vector is a hidden state of the cell (also called output) which corresponds to the short-term state (2), while the 𝑐 vector is a cell state corresponding to the long-term state (3). States of the cell are controlled using gates: the forget gate 𝑓 , as the first, decides which long-term information will continue to pass the network-it decides which information stays and which gets deleted (4).…”

Section: Lstm Networkmentioning

confidence: 99%

“…Both scientists and companies are genuinely interested in automated warehouses [1,2]. It can already be seen in such places where goods are transported by autonomous robots-automated guided vehicles (AGVs) [3,4]. This usually happens along strictly defined routes, but humans are not involved in the transport process.…”

Section: Introductionmentioning

confidence: 99%

Using the LSTM Neural Network and the UWB Positioning System to Predict the Position of Low and High Speed Moving Objects

Paszek,

Grzechca

2023

Self Cite

Automation of transportation will play a crucial role in the future when people driving vehicles will be replaced by autonomous systems. Currently, the positioning systems are not used alone but are combined in order to create cooperative positioning systems. The ultra-wideband (UWB) system is an excellent alternative to the global positioning system (GPS) in a limited area but has some drawbacks. Despite many advantages of various object positioning systems, none is free from the problem of object displacement during measurement (data acquisition), which affects positioning accuracy. In addition, temporarily missing data from the absolute positioning system can lead to dangerous situations. Moreover, data pre-processing is unavoidable and takes some time, affecting additionally the object’s displacement in relation to its previous position and its starting point of the new positioning process. So, the prediction of the position of an object is necessary to minimize the time when the position is unknown or out of date, especially when the object is moving at high speed and the position update rate is low. This article proposes using the long short-term memory (LSTM) artificial neural network to predict objects’ positions based on historical data from the UWB system and inertial navigation. The proposed solution creates a reliable positioning system that predicts 10 positions of low and high-speed moving objects with an error below 10 cm. Position prediction allows detection of possible collisions—the intersection of the trajectories of moving objects.

“…One of the main challenges of SDT is how to deal with heterogeneous and complex datasets with different formats [27]. The present work incorporates datasets from both homogenous and heterogeneous SSs with similar and different formats.…”

Section: Classification Techniques Application Of Classification Tech...mentioning

confidence: 99%

Data Mining and Fusion Framework for In-Home Monitoring Applications

Ekerete,

Garcia-Constantino,

Nugent

et al. 2023

Sensor Data Fusion (SDT) algorithms and models have been widely used in diverse applications. One of the main challenges of SDT includes how to deal with heterogeneous and complex datasets with different formats. The present work utilised both homogenous and heterogeneous datasets to propose a novel SDT framework. It compares data mining-based fusion software packages such as RapidMiner Studio, Anaconda, Weka, and Orange, and proposes a data fusion framework suitable for in-home applications. A total of 574 privacy-friendly (binary) images and 1722 datasets gleaned from thermal and Radar sensing solutions, respectively, were fused using the software packages on instances of homogeneous and heterogeneous data aggregation. Experimental results indicated that the proposed fusion framework achieved an average Classification Accuracy of 84.7% and 95.7% on homogeneous and heterogeneous datasets, respectively, with the help of data mining and machine learning models such as Naïve Bayes, Decision Tree, Neural Network, Random Forest, Stochastic Gradient Descent, Support Vector Machine, and CN2 Induction. Further evaluation of the Sensor Data Fusion framework based on cross-validation of features indicated average values of 94.4% for Classification Accuracy, 95.7% for Precision, and 96.4% for Recall. The novelty of the proposed framework includes cost and timesaving advantages for data labelling and preparation, and feature extraction.