Data-Driven Condition Monitoring of a Hydraulic Press Using Supervised Learning and Neural Networks
The automated evaluation of machine conditions is key for efficient maintenance planning. Data-driven methods have proven to enable the automated mapping of complex patterns in sensor data to the health state of a system. However, generalizable approaches for the development of such solutions in the...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-08-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/15/17/6217 |
| _version_ | 1797495679726125056 |
|---|---|
| author | Faried Makansi Katharina Schmitz |
| author_facet | Faried Makansi Katharina Schmitz |
| author_sort | Faried Makansi |
| collection | DOAJ |
| description | The automated evaluation of machine conditions is key for efficient maintenance planning. Data-driven methods have proven to enable the automated mapping of complex patterns in sensor data to the health state of a system. However, generalizable approaches for the development of such solutions in the framework of industrial applications are not established yet. In this contribution, a procedure is presented for the development of data-driven condition monitoring solutions for industrial hydraulics using supervised learning and neural networks. The proposed method involves feature extraction as well as feature selection and is applied on simulated data of a hydraulic press. Different steps of the development process are investigated regarding the design options and their efficacy in fault classification tasks. High classification accuracies could be achieved with the presented approach, whereas different faults are shown to require different configurations of the classification models. |
| first_indexed | 2024-03-10T01:53:06Z |
| format | Article |
| id | doaj.art-93c9f414900d48a5a8faca4249fcb5ea |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T01:53:06Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-93c9f414900d48a5a8faca4249fcb5ea2023-11-23T13:02:18ZengMDPI AGEnergies1996-10732022-08-011517621710.3390/en15176217Data-Driven Condition Monitoring of a Hydraulic Press Using Supervised Learning and Neural NetworksFaried Makansi0Katharina Schmitz1RWTH Aachen University, Institute for Fluid Power Drives and Systems (ifas), 5074 Aachen, GermanyRWTH Aachen University, Institute for Fluid Power Drives and Systems (ifas), 5074 Aachen, GermanyThe automated evaluation of machine conditions is key for efficient maintenance planning. Data-driven methods have proven to enable the automated mapping of complex patterns in sensor data to the health state of a system. However, generalizable approaches for the development of such solutions in the framework of industrial applications are not established yet. In this contribution, a procedure is presented for the development of data-driven condition monitoring solutions for industrial hydraulics using supervised learning and neural networks. The proposed method involves feature extraction as well as feature selection and is applied on simulated data of a hydraulic press. Different steps of the development process are investigated regarding the design options and their efficacy in fault classification tasks. High classification accuracies could be achieved with the presented approach, whereas different faults are shown to require different configurations of the classification models.https://www.mdpi.com/1996-1073/15/17/6217condition monitoringfault detection and diagnosisindustrial hydraulicshydraulic presssupervised learningneural networks |
| spellingShingle | Faried Makansi Katharina Schmitz Data-Driven Condition Monitoring of a Hydraulic Press Using Supervised Learning and Neural Networks Energies condition monitoring fault detection and diagnosis industrial hydraulics hydraulic press supervised learning neural networks |
| title | Data-Driven Condition Monitoring of a Hydraulic Press Using Supervised Learning and Neural Networks |
| title_full | Data-Driven Condition Monitoring of a Hydraulic Press Using Supervised Learning and Neural Networks |
| title_fullStr | Data-Driven Condition Monitoring of a Hydraulic Press Using Supervised Learning and Neural Networks |
| title_full_unstemmed | Data-Driven Condition Monitoring of a Hydraulic Press Using Supervised Learning and Neural Networks |
| title_short | Data-Driven Condition Monitoring of a Hydraulic Press Using Supervised Learning and Neural Networks |
| title_sort | data driven condition monitoring of a hydraulic press using supervised learning and neural networks |
| topic | condition monitoring fault detection and diagnosis industrial hydraulics hydraulic press supervised learning neural networks |
| url | https://www.mdpi.com/1996-1073/15/17/6217 |
| work_keys_str_mv | AT fariedmakansi datadrivenconditionmonitoringofahydraulicpressusingsupervisedlearningandneuralnetworks AT katharinaschmitz datadrivenconditionmonitoringofahydraulicpressusingsupervisedlearningandneuralnetworks |