Next Article in Journal
Performance Evaluation of Autonomous Driving Control Algorithm for a Crawler-Type Agricultural Vehicle Based on Low-Cost Multi-Sensor Fusion Positioning
Next Article in Special Issue
Bearing Health Monitoring Using Relief-F-Based Feature Relevance Analysis and HMM
Previous Article in Journal
Application of Hyperspectral Imaging for Assessment of Tomato Leaf Water Status in Plant Factories
Previous Article in Special Issue
Hybrid Fault Diagnosis of Bearings: Adaptive Fuzzy Orthonormal-ARX Robust Feedback Observer
Article

Low-Speed Bearing Fault Diagnosis Based on Permutation and Spectral Entropy Measures

1
Ikerlan Technology Research Centre, Basque Research and Technology Alliance (BRTA), P°. J. Ma. Arizmendiarrieta 2, 20500 Arrasate-Mondragón, Spain
2
Control, Modeling, Identification, and Applications (CoDAlab), Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB), Eduard Maristany 16, 08019 Barcelona, Spain
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(13), 4666; https://doi.org/10.3390/app10134666
Received: 27 May 2020 / Revised: 30 June 2020 / Accepted: 3 July 2020 / Published: 6 July 2020
(This article belongs to the Special Issue Bearing Fault Detection and Diagnosis)
Despite its influence on wind energy service life, condition-based maintenance is still challenging to perform. For offshore wind farms, which are placed in harsh and remote environments, damage detection is critically important to schedule maintenance tasks and reduce operation and maintenance costs. One critical component to be monitored on a wind turbine is the pitch bearing, which can operate at low speed and high loads. Classical methods and features for general purpose bearings cannot be applied effectively to wind turbine pitch bearings owing to their specific operating conditions (high loads and non-constant very low speed with changing direction). Thus, damage detection of wind turbine pitch bearings is currently a challenge. In this study, entropy indicators are proposed as an alternative to classical bearing analysis. For this purpose, spectral and permutation entropy are combined to analyze a raw vibration signal from a low-speed bearing in several scenarios. The results indicate that entropy values change according to different types of damage on bearings, and the sensitivity of the entropy types differs among them. The study offers some important insights into the use of entropy indicators for feature extraction and it lays the foundation for future bearing prognosis methods. View Full-Text
Keywords: fault diagnosis; pitch bearing; condition monitoring; entropy; low speed; vibration fault diagnosis; pitch bearing; condition monitoring; entropy; low speed; vibration
Show Figures

Figure 1

MDPI and ACS Style

Sandoval, D.; Leturiondo, U.; Pozo, F.; Vidal, Y. Low-Speed Bearing Fault Diagnosis Based on Permutation and Spectral Entropy Measures. Appl. Sci. 2020, 10, 4666. https://doi.org/10.3390/app10134666

AMA Style

Sandoval D, Leturiondo U, Pozo F, Vidal Y. Low-Speed Bearing Fault Diagnosis Based on Permutation and Spectral Entropy Measures. Applied Sciences. 2020; 10(13):4666. https://doi.org/10.3390/app10134666

Chicago/Turabian Style

Sandoval, Diego, Urko Leturiondo, Francesc Pozo, and Yolanda Vidal. 2020. "Low-Speed Bearing Fault Diagnosis Based on Permutation and Spectral Entropy Measures" Applied Sciences 10, no. 13: 4666. https://doi.org/10.3390/app10134666

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop