AI Article Synopsis
- Manufacturing companies are becoming "smarter" through Industry 4.0 by using multiple sensors for better machine and worker monitoring, which helps improve processes, cut costs, and enhance safety.
- The paper reviews advanced data fusion techniques that combine diverse sensor data to optimize monitoring systems and create a guide for the early stages of manufacturing analytics.
- It highlights that current methods in data fusion and preprocessing go beyond state-of-the-art, while also identifying existing weaknesses and areas for future research.
Article Abstract
Manufacturing companies increasingly become "smarter" as a result of the Industry 4.0 revolution. Multiple sensors are used for industrial monitoring of machines and workers in order to detect events and consequently improve the manufacturing processes, lower the respective costs, and increase safety. Multisensor systems produce big amounts of heterogeneous data. Data fusion techniques address the issue of multimodality by combining data from different sources and improving the results of monitoring systems. The current paper presents a detailed review of state-of-the-art data fusion solutions, on data storage and indexing from various types of sensors, feature engineering, and multimodal data integration. The review aims to serve as a guide for the early stages of an analytic pipeline of manufacturing prognosis. The reviewed literature showed that in fusion and in preprocessing, the methods chosen to be applied in this sector are beyond the state-of-the-art. Existing weaknesses and gaps that lead to future research goals were also identified.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8914726 | PMC |
| http://dx.doi.org/10.3390/s22051734 | DOI Listing |
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