PREDICTIVE DIAGNOSIS IN AXIAL PISTON PUMPS A STUDY FOR HIGH FREQUENCY CONDITION INDICATORS UNDER VARIABLE OPERATING CONDITIONS
Increasing reliability, availability and safety requirements as well as an increasing amount of data acquisition systems have enabled condition-based maintenance in mobile and industrial machinery. In this paper, we present a methodology to develop a robust diagnostic approach. This includes the con...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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The Prognostics and Health Management Society
2023-01-01
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| Series: | International Journal of Prognostics and Health Management |
| Subjects: |
| _version_ | 1797812584841216000 |
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| author | Oliver Gnepper Hannes Hitzer Olaf Enge-Rosenblatt |
| author_facet | Oliver Gnepper Hannes Hitzer Olaf Enge-Rosenblatt |
| author_sort | Oliver Gnepper |
| collection | DOAJ |
| description | Increasing reliability, availability and safety requirements as well as an increasing amount of data acquisition systems have enabled condition-based maintenance in mobile and industrial machinery. In this paper, we present a methodology to develop a robust diagnostic approach. This includes the consideration of variable operating conditions in the data acquisition process as well as a versatile, non domain-specific feature extraction technique. By doing so, we train anomaly detection models for different fault types and different fault intensities in variable displacement axial piston pumps. Our specific interest points to the investigation of high-frequency condition indicators with a sampling rate of 1 MHz. Furthermore, we compare those to industry standard sensors, sampled with up to 20 kHz.
By considering variable operating conditions, we are able to quantify the influence of the operating point. The results show, that high-frequency features are a suitable condition-indicator across several operating points and can be used to detect faults more easily. Although set up on a test-bench, the experimental design allows to draw conclusions about realistic field operational conditions. |
| first_indexed | 2024-03-13T07:39:42Z |
| format | Article |
| id | doaj.art-75922772a7a847c8bfce1690b16524b5 |
| institution | Directory Open Access Journal |
| issn | 2153-2648 |
| language | English |
| last_indexed | 2024-03-13T07:39:42Z |
| publishDate | 2023-01-01 |
| publisher | The Prognostics and Health Management Society |
| record_format | Article |
| series | International Journal of Prognostics and Health Management |
| spelling | doaj.art-75922772a7a847c8bfce1690b16524b52023-06-03T18:02:53ZengThe Prognostics and Health Management SocietyInternational Journal of Prognostics and Health Management2153-26482023-01-01141https://doi.org/10.36001/ijphm.2023.v14i1.3393PREDICTIVE DIAGNOSIS IN AXIAL PISTON PUMPS A STUDY FOR HIGH FREQUENCY CONDITION INDICATORS UNDER VARIABLE OPERATING CONDITIONSOliver Gnepper0https://orcid.org/0000-0001-6430-620XHannes Hitzer1Olaf Enge-Rosenblatt2https://orcid.org/0000-0002-6069-7423Fraunhofer IIS/EAS, Fraunhofer Institute for Integrated Circuits, Division Engineering of Adaptive Systems, Dresden, GermanyBosch Rexroth AG, Horb am Neckar, Germany Fraunhofer IIS/EAS, Fraunhofer Institute for Integrated Circuits, Division Engineering of Adaptive Systems, Dresden, GermanyIncreasing reliability, availability and safety requirements as well as an increasing amount of data acquisition systems have enabled condition-based maintenance in mobile and industrial machinery. In this paper, we present a methodology to develop a robust diagnostic approach. This includes the consideration of variable operating conditions in the data acquisition process as well as a versatile, non domain-specific feature extraction technique. By doing so, we train anomaly detection models for different fault types and different fault intensities in variable displacement axial piston pumps. Our specific interest points to the investigation of high-frequency condition indicators with a sampling rate of 1 MHz. Furthermore, we compare those to industry standard sensors, sampled with up to 20 kHz. By considering variable operating conditions, we are able to quantify the influence of the operating point. The results show, that high-frequency features are a suitable condition-indicator across several operating points and can be used to detect faults more easily. Although set up on a test-bench, the experimental design allows to draw conclusions about realistic field operational conditions.prognostics and health managementmachine learningaxial piston unitfault detectionvariable operating conditionscondition monitoringanomaly detectionvibration signals |
| spellingShingle | Oliver Gnepper Hannes Hitzer Olaf Enge-Rosenblatt PREDICTIVE DIAGNOSIS IN AXIAL PISTON PUMPS A STUDY FOR HIGH FREQUENCY CONDITION INDICATORS UNDER VARIABLE OPERATING CONDITIONS International Journal of Prognostics and Health Management prognostics and health management machine learning axial piston unit fault detection variable operating conditions condition monitoring anomaly detection vibration signals |
| title | PREDICTIVE DIAGNOSIS IN AXIAL PISTON PUMPS A STUDY FOR HIGH FREQUENCY CONDITION INDICATORS UNDER VARIABLE OPERATING CONDITIONS |
| title_full | PREDICTIVE DIAGNOSIS IN AXIAL PISTON PUMPS A STUDY FOR HIGH FREQUENCY CONDITION INDICATORS UNDER VARIABLE OPERATING CONDITIONS |
| title_fullStr | PREDICTIVE DIAGNOSIS IN AXIAL PISTON PUMPS A STUDY FOR HIGH FREQUENCY CONDITION INDICATORS UNDER VARIABLE OPERATING CONDITIONS |
| title_full_unstemmed | PREDICTIVE DIAGNOSIS IN AXIAL PISTON PUMPS A STUDY FOR HIGH FREQUENCY CONDITION INDICATORS UNDER VARIABLE OPERATING CONDITIONS |
| title_short | PREDICTIVE DIAGNOSIS IN AXIAL PISTON PUMPS A STUDY FOR HIGH FREQUENCY CONDITION INDICATORS UNDER VARIABLE OPERATING CONDITIONS |
| title_sort | predictive diagnosis in axial piston pumps a study for high frequency condition indicators under variable operating conditions |
| topic | prognostics and health management machine learning axial piston unit fault detection variable operating conditions condition monitoring anomaly detection vibration signals |
| work_keys_str_mv | AT olivergnepper predictivediagnosisinaxialpistonpumpsastudyforhighfrequencyconditionindicatorsundervariableoperatingconditions AT hanneshitzer predictivediagnosisinaxialpistonpumpsastudyforhighfrequencyconditionindicatorsundervariableoperatingconditions AT olafengerosenblatt predictivediagnosisinaxialpistonpumpsastudyforhighfrequencyconditionindicatorsundervariableoperatingconditions |