Real-time operational load monitoring of a composite aerostructure using FPGA-based computing system
Operational load monitoring (OLM) is an industrial process related to structural health monitoring, where fatigue of the structure is tracked. Artificial intelligence methods, such as artificial neural networks (ANNs) or Gaussian processes, are utilized to improve efficiency of such processes. This...
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| Format: | Article |
| Language: | English |
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Polish Academy of Sciences
2023-11-01
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| Series: | Bulletin of the Polish Academy of Sciences: Technical Sciences |
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| Online Access: | https://journals.pan.pl/Content/129091/PDF/BPASTS_2024_72_1_3953.pdf |
| _version_ | 1797289363168559104 |
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| author | Waldemar Mucha |
| author_facet | Waldemar Mucha |
| author_sort | Waldemar Mucha |
| collection | DOAJ |
| description | Operational load monitoring (OLM) is an industrial process related to structural health monitoring, where fatigue of the structure is tracked. Artificial intelligence methods, such as artificial neural networks (ANNs) or Gaussian processes, are utilized to improve efficiency of such processes. This paper focuses on moving such processes towards green computing by deploying and executing the algorithm on low-power consumption FPGA where high-throughput and truly parallel computations can be performed. In the following paper, the OLM process of typical aerostructure (hat-stiffened composite panel) is performed using ANN. The ANN was trained using numerically generated data, of every possible load case, to be working with sensor measurements as inputs. The trained ANN was deployed to Xilinx Artix-7 A100T FPGA of a real-time microcontroller. By executing the ANN on FPGA (where every neuron of a given layer can be processed at the same time, without limiting the number of parallel threads), computation time could be reduced by 70% as compared to standard CPU execution. Series of real-time experiments were performed that have proven the efficiency and high accuracy of the developed FPGA-based algorithm. Adjusting the ANN algorithm to FPGA requirements takes some effort, however it can lead to high performance increase. FPGA has the advantages of many more potential parallel threads than a standard CPU and much lower consumption than a GPU. This is particularly important taking into account potential embedded and remote applications, such as widely performed monitoring of airplane structures. |
| first_indexed | 2024-03-07T19:03:52Z |
| format | Article |
| id | doaj.art-f36480d67fb040f9bf7ebbe9f7150926 |
| institution | Directory Open Access Journal |
| issn | 2300-1917 |
| language | English |
| last_indexed | 2024-03-07T19:03:52Z |
| publishDate | 2023-11-01 |
| publisher | Polish Academy of Sciences |
| record_format | Article |
| series | Bulletin of the Polish Academy of Sciences: Technical Sciences |
| spelling | doaj.art-f36480d67fb040f9bf7ebbe9f71509262024-03-01T11:06:21ZengPolish Academy of SciencesBulletin of the Polish Academy of Sciences: Technical Sciences2300-19172023-11-01721https://doi.org/10.24425/bpasts.2023.148251Real-time operational load monitoring of a composite aerostructure using FPGA-based computing systemWaldemar Mucha0https://orcid.org/0000-0002-9724-1817Department of Computational Mechanics and Engineering, Silesian University of Technology, Konarskiego 18A, 44-100 Gliwice, PolandOperational load monitoring (OLM) is an industrial process related to structural health monitoring, where fatigue of the structure is tracked. Artificial intelligence methods, such as artificial neural networks (ANNs) or Gaussian processes, are utilized to improve efficiency of such processes. This paper focuses on moving such processes towards green computing by deploying and executing the algorithm on low-power consumption FPGA where high-throughput and truly parallel computations can be performed. In the following paper, the OLM process of typical aerostructure (hat-stiffened composite panel) is performed using ANN. The ANN was trained using numerically generated data, of every possible load case, to be working with sensor measurements as inputs. The trained ANN was deployed to Xilinx Artix-7 A100T FPGA of a real-time microcontroller. By executing the ANN on FPGA (where every neuron of a given layer can be processed at the same time, without limiting the number of parallel threads), computation time could be reduced by 70% as compared to standard CPU execution. Series of real-time experiments were performed that have proven the efficiency and high accuracy of the developed FPGA-based algorithm. Adjusting the ANN algorithm to FPGA requirements takes some effort, however it can lead to high performance increase. FPGA has the advantages of many more potential parallel threads than a standard CPU and much lower consumption than a GPU. This is particularly important taking into account potential embedded and remote applications, such as widely performed monitoring of airplane structures.https://journals.pan.pl/Content/129091/PDF/BPASTS_2024_72_1_3953.pdfoperational load monitoringreal time computationsfpgaaerostructuresneural networksartificial intelligencestructural health monitoringgreen computing |
| spellingShingle | Waldemar Mucha Real-time operational load monitoring of a composite aerostructure using FPGA-based computing system Bulletin of the Polish Academy of Sciences: Technical Sciences operational load monitoring real time computations fpga aerostructures neural networks artificial intelligence structural health monitoring green computing |
| title | Real-time operational load monitoring of a composite aerostructure using FPGA-based computing system |
| title_full | Real-time operational load monitoring of a composite aerostructure using FPGA-based computing system |
| title_fullStr | Real-time operational load monitoring of a composite aerostructure using FPGA-based computing system |
| title_full_unstemmed | Real-time operational load monitoring of a composite aerostructure using FPGA-based computing system |
| title_short | Real-time operational load monitoring of a composite aerostructure using FPGA-based computing system |
| title_sort | real time operational load monitoring of a composite aerostructure using fpga based computing system |
| topic | operational load monitoring real time computations fpga aerostructures neural networks artificial intelligence structural health monitoring green computing |
| url | https://journals.pan.pl/Content/129091/PDF/BPASTS_2024_72_1_3953.pdf |
| work_keys_str_mv | AT waldemarmucha realtimeoperationalloadmonitoringofacompositeaerostructureusingfpgabasedcomputingsystem |