[go: up one dir, main page]

Mohammadi et al., 2020 - Google Patents

DeepWind: an accurate wind turbine condition monitoring framework via deep learning on embedded platforms

Mohammadi et al., 2020

Document ID
6611774421065046701
Author
Mohammadi H
Arshad R
Rautmare S
Manjunatha S
Kuschel M
Jentzsch F
Platzner M
Boschmann A
Schollbach D
Publication year
Publication venue
2020 25th IEEE international conference on emerging technologies and factory automation (ETFA)

External Links

Snippet

Condition monitoring of critical components in wind turbines is of utmost importance to enable low cost and preventive maintenance and to minimize downtime. In this paper we propose DEEPWIND, an end-to-end condition monitoring and fault detection framework that …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GASES [GHG] EMISSION, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/72—Wind turbines with rotation axis in wind direction
    • Y02E10/722—Components or gearbox
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
    • G05B23/0254—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
    • G05B23/0283—Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL]

Similar Documents

Publication Publication Date Title
Afridi et al. Artificial intelligence based prognostic maintenance of renewable energy systems: A review of techniques, challenges, and future research directions
Gao et al. An overview on fault diagnosis, prognosis and resilient control for wind turbine systems
Mohammadi et al. DeepWind: an accurate wind turbine condition monitoring framework via deep learning on embedded platforms
Wang et al. SCADA data based condition monitoring of wind turbines
Hsu et al. Wind turbine fault diagnosis and predictive maintenance through statistical process control and machine learning
Liu et al. The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review
Liu et al. Wind turbine blade bearing fault diagnosis under fluctuating speed operations via Bayesian augmented Lagrangian analysis
Joshuva et al. Fault diagnostic methods for wind turbine: A review
Gao et al. Real-time monitoring, fault prediction and health management for offshore wind turbine systems
Baltazar et al. A review on neurocomputing based wind turbines fault diagnosis and prognosis
Yao et al. A new condition-monitoring method based on multi-variable correlation learning network for wind turbine fault detection
Li et al. Research on condition operation monitoring of power system based on supervisory control and data acquisition model
Sethi et al. Vibration signal-based diagnosis of wind turbine blade conditions for improving energy extraction using machine learning approach
Tchakoua et al. New trends and future challenges for wind turbines condition monitoring
Ma et al. SCADA data-driven spatio-temporal graph convolutional neural network for wind turbine fault diagnosis
Dinh et al. Implementation of digital twin-assisted condition monitoring and fault diagnosis for wind turbines
Lydia et al. Condition monitoring in wind turbines: a review
Dabetwar et al. Mass imbalance diagnostics in wind turbines using deep learning with data augmentation
CN114294186A (en) Method and device for detecting icing of wind turbine blades based on generative adversarial network
CN120197003A (en) A helicopter anomaly detection method based on unsupervised dual-domain model
CN119878468A (en) Fan blade clearance monitoring method, system, electronic equipment and storage medium
CN118242232A (en) Offshore wind turbine running state evaluation method and related device
Yan et al. Inter-turn Short Circuit Fault Diagnosis and Severity Estimation for Wind Turbine Generators
Hoxha et al. Supervised classification with SCADA data for condition monitoring of wind turbines
Emeksiz et al. Some prognostic and diagnostic methods for determining wind turbine failures-A review