An Improved Grasshopper Optimization Algorithm Based Echo State Network for Predicting Faults in Airplane Engines
In today's age of industrialization, sensor devices installed on equipment generate a vast amount of data. One of the engineers' main jobs is utilizing these data to provide better solutions to industrial problems. This availability of extensive data partly led to the creation of predictiv...
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9180254/ |
| _version_ | 1818624282469597184 |
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| author | Abubakar Bala Idris Ismail Rosdiazli Ibrahim Sadiq M. Sait Diego Oliva |
| author_facet | Abubakar Bala Idris Ismail Rosdiazli Ibrahim Sadiq M. Sait Diego Oliva |
| author_sort | Abubakar Bala |
| collection | DOAJ |
| description | In today's age of industrialization, sensor devices installed on equipment generate a vast amount of data. One of the engineers' main jobs is utilizing these data to provide better solutions to industrial problems. This availability of extensive data partly led to the creation of predictive maintenance (PdM). In PdM, existing and previous conditions of devices are used to predict their future behavior for optimal maintenance. Most of these PdM approaches are typical time-series predictions. Machine learning tools like Recurrent Neural Networks (RNNs) are excellent tools for time-series predictions. However, most RNNs suffer from training issues due to the unstable gradient problem. Thus, networks such as the Echo State Network (ESN), were designed to solve them. The ESN solves the gradient problem by training only the output weights using simple linear regression. Despite this ease, the selection of ESN parameters and topology is a considerable design challenge. This problem is often formulated as a typical optimization problem. Metaheuristic algorithms are known to be excellent tools for solving optimization problems. Hence, in this work, we design an improved Grasshopper Optimization Algorithm (GOA) based ESN. The proposed technique uses a new solution representation with a simplified attraction and repulsion mechanisms to enhance performance. Our target application is to predict the Remaining Useful Life (RUL) of turbofan engines. The method outperforms the Cuckoo Search (CS), Differential Evolution (DE), Particle Swarm Optimization (PSO), Binary PSO (BPSO), the original GOA, the classical ESN, deep ESN, and LSTM. We have provided all implemented codes and data at the GitHub repository. https://github.com/bala-221/Airplane-fault-prediction. |
| first_indexed | 2024-12-16T18:54:29Z |
| format | Article |
| id | doaj.art-b02cf25eb6284ca59be85854a4f07c32 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T18:54:29Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b02cf25eb6284ca59be85854a4f07c322022-12-21T22:20:35ZengIEEEIEEE Access2169-35362020-01-01815977315978910.1109/ACCESS.2020.30203569180254An Improved Grasshopper Optimization Algorithm Based Echo State Network for Predicting Faults in Airplane EnginesAbubakar Bala0https://orcid.org/0000-0003-1178-2522Idris Ismail1Rosdiazli Ibrahim2Sadiq M. Sait3https://orcid.org/0000-0002-4796-0581Diego Oliva4https://orcid.org/0000-0001-8781-7993Electrical and Electronics Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar, MalaysiaElectrical and Electronics Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar, MalaysiaElectrical and Electronics Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar, MalaysiaComputer Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi ArabiaDepartamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Guadalajara, MexicoIn today's age of industrialization, sensor devices installed on equipment generate a vast amount of data. One of the engineers' main jobs is utilizing these data to provide better solutions to industrial problems. This availability of extensive data partly led to the creation of predictive maintenance (PdM). In PdM, existing and previous conditions of devices are used to predict their future behavior for optimal maintenance. Most of these PdM approaches are typical time-series predictions. Machine learning tools like Recurrent Neural Networks (RNNs) are excellent tools for time-series predictions. However, most RNNs suffer from training issues due to the unstable gradient problem. Thus, networks such as the Echo State Network (ESN), were designed to solve them. The ESN solves the gradient problem by training only the output weights using simple linear regression. Despite this ease, the selection of ESN parameters and topology is a considerable design challenge. This problem is often formulated as a typical optimization problem. Metaheuristic algorithms are known to be excellent tools for solving optimization problems. Hence, in this work, we design an improved Grasshopper Optimization Algorithm (GOA) based ESN. The proposed technique uses a new solution representation with a simplified attraction and repulsion mechanisms to enhance performance. Our target application is to predict the Remaining Useful Life (RUL) of turbofan engines. The method outperforms the Cuckoo Search (CS), Differential Evolution (DE), Particle Swarm Optimization (PSO), Binary PSO (BPSO), the original GOA, the classical ESN, deep ESN, and LSTM. We have provided all implemented codes and data at the GitHub repository. https://github.com/bala-221/Airplane-fault-prediction.https://ieeexplore.ieee.org/document/9180254/Artificial neural networksairplanesecho state networkevolutionary algorithmsgrasshopper optimization algorithm (GOA)metaheuristics |
| spellingShingle | Abubakar Bala Idris Ismail Rosdiazli Ibrahim Sadiq M. Sait Diego Oliva An Improved Grasshopper Optimization Algorithm Based Echo State Network for Predicting Faults in Airplane Engines IEEE Access Artificial neural networks airplanes echo state network evolutionary algorithms grasshopper optimization algorithm (GOA) metaheuristics |
| title | An Improved Grasshopper Optimization Algorithm Based Echo State Network for Predicting Faults in Airplane Engines |
| title_full | An Improved Grasshopper Optimization Algorithm Based Echo State Network for Predicting Faults in Airplane Engines |
| title_fullStr | An Improved Grasshopper Optimization Algorithm Based Echo State Network for Predicting Faults in Airplane Engines |
| title_full_unstemmed | An Improved Grasshopper Optimization Algorithm Based Echo State Network for Predicting Faults in Airplane Engines |
| title_short | An Improved Grasshopper Optimization Algorithm Based Echo State Network for Predicting Faults in Airplane Engines |
| title_sort | improved grasshopper optimization algorithm based echo state network for predicting faults in airplane engines |
| topic | Artificial neural networks airplanes echo state network evolutionary algorithms grasshopper optimization algorithm (GOA) metaheuristics |
| url | https://ieeexplore.ieee.org/document/9180254/ |
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