Open Access
Review
Table 4
Main primary information sources reviewed.
| No. | References | Application | Type | Cls. | Techique |
|---|---|---|---|---|---|
| E1 | Hastbacka et al. [8] | Predictive maintenance | DM | TP | Semantic analysis of control systems and pattern recognition |
| E2 | Rix et al. [9] | Predictive maintenance, quality monitoring | DM | PR | Predictive data analysis, integration of open standards |
| E3 | Srinivasan et al. [10] | Industrial processes optimization | DM | PR | Predictive data analysis, pattern recognition |
| E4 | Fleischmann et al. [11] | Predictive maintenance and quality monitoring | DM | RE | Data analysis of energy and temperature in crimping processes |
| E5 | Sriyakul et al. [12] | Predictive maintenance and real-time product quality monitoring | DM | RE | Estimating unknown parameters, evaluates variables (energy, temperature) to detect wear conditions |
| E6 | Cupek et al. [13] | Monitoring of energy consumption in discrete production lines | DM | CL | K-means clustering |
| E7 | Gutiérrez et al. [14] | Automatic network configuration in real-time | DM | SP | Extraction of traffic parameters and learning in networks |
| E8 | Hormann et al. [15] | Processes optimization and intrusion detection | DM | VI | Feature extraction, text processing using regular expressions |
| E9 | Kretschmer et al. [16] | Data management | DM | LM | Persistent industrial data storage |
| E10 | Neu et al. [17] | Security in industrial networks | DM | CA | J48 algorithm [37] in WEKA, traffic behavior analysis |
| E11 | Bosi et al. [18] | Improving operational efficiency, Predictive maintenance, data integration | DM | VI | Feature extraction and analysis |
| E12 | Qin et al. [19] | Quality monitoring of teaching | DM | NN | Pattern extraction and evaluation of educational data using GRU neural networks |
| E13 | Mathias et al. [20] | Quality inspection of welding processes through the analysis of electrical signals | DM | CL, CA | Clustering algorithms, time series analysis and multi-label classifiers |
| E14 | Vrana [21] | Nondestructive Evaluation (NDE), Industrial Internet of Things | DM | TP | Pattern extraction with digital twins and statistical models |
| E15 | Rubart et al. [22] | Industrial processes optimization | DM | TP | Data analysis and semantic annotations |
| E16 | Arevalo et al. [23] | Fault detection | DM | AR | Failure Mode and Effects Analysis for knowledge extraction |
| E17 | Fuhrmann et al. [24] | Control and monitoring of injection molding machinery in industrial production environment | NLP | TOK, PAR | Voice interaction and intent recognition using RASA framework |
| E18 | Soller et al. [25] | Predictive maintenance, anomaly detection in production | DM | OD, NN | One-Class Support Vector Machine, Isolation Forest and Autoencoder |
| E19 | Wu and Yang [26] | Industrial Internet of Things, improving automation of data subscription in smart factories | NLP | TOK | Word2Vec model to calculate text similarity, collaborative filtering to evaluate similarity between subscribers and topics |
| E20 | Bakken [27] | Sensor data extraction and processing in the electrical domain | DM | TP | Data extraction for analysis, using a language designed to simplify access to time series data |
| E21 | Tufek [28] | Extracting and formalizing semantics from industrial standards to enhance machine interoperability | NLP | NER | Rule classification using NER and Machine Learning |
| E22 | Tufek et al. [29] | Automate the extraction of compliance rules from OPC-UA companion specifications | NLP | NER | Semantic rule extraction using NER |
| E23 | Bareedu et al. [30] | Semantic validation rules from industrial standards | DM, NLP | DT, TOK | Extract and analyze text, patterns and rules from structured and unstructured data |
| E24 | Hornsteiner et al. [31] | End-of-line process of an automotive supplier (robotic inspections, laser engraving and cleaning) | DM | AR | Rule and model based techniques using network traffic |
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