Fault Detection and Diagnosis Methods for Fluid Power Pitch System Components—A Review

Asmussen, Magnus Færing; Liniger, Jesper; Pedersen, Henrik C.

doi:10.3390/en14051305

Cited by 12 publications

(5 citation statements)

References 37 publications

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“…Most of the time, the pitch of an audio signal is used to describe the vibration of a frequency. Many researcher can say that a high frequency audio wave has a high pitch, and a low frequency audio wave has a low pitch [37]. librosa.piptrack figures out how high or low the sound files are.…”

Section: ) Pitchmentioning

confidence: 99%

BMNet-5: A Novel Approach of Neural Network to Classify the Genre of Bengali Music Based on Audio Features

et al. 2022

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Music genre classification (MGC) is the process of putting genre labels on music by analyzing the sounds or words. With the rapid growth of music data repositories, MGC can be used in a lot of ways to organize and manage music recommendation systems, advertising, and streaming services. But there have been a lot of works on classifying English music using different statistical and machine learning methods, but there hasn't been much progress in classifying Bengali music. Also, Deep Learning (DL) methods have been used in a few important ways to classify different types of music. The content and uniqueness of Bengali music make it much more interesting. Also, there is still a lot to learn about how to use the DL approach in Bengali music. So, Bengali music genre classification is a pretty new area of research in the field of Deep Learning. In this paper, we developed a unique technique called BMNet-5 to perform a multiclass classification of Bangla music genres such as "Bangla Adhunik," "Bangla Hip-Hop," "Bangla Band Music," "Nazrulgeeti," "Palligeeti," and "Rabindra Sangeet." We show the effectiveness of the suggested technique by extracting features from a dataset of 1742 Bangla music pieces and evaluating the automated classification judgments. The proposed BMNet-5 is based on a neural network designed to predict music genre from audio inputs. Our suggested model outperformed the corresponding previous research with an accuracy of 90.32%. The BMNet-5 model is then tested for performance consistency using K-fold cross validation with varying k values. Finally, we use the suggested model to train the interpretable SHAP model for all the genre of the Bangla music dataset, and the development of an explainable outcome may have a significant advantage.

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Section: ) Pitchmentioning

confidence: 99%

BMNet-5: A Novel Approach of Neural Network to Classify the Genre of Bengali Music Based on Audio Features

et al. 2022

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“…The yawing system regulates the yaw angle γ to align the turbine rotor with the wind direction to maximize power, to place the rotor out of the wind in storm conditions or to resolve cable twist in the tower. Pitch and yaw systems can also be hydraulically actuated instead of using an electric drive [142,143].…”

Section: Pitch and Yaw Systemsmentioning

confidence: 99%

Digital Twins for Wind Energy Conversion Systems: A Literature Review of Potential Modelling Techniques Focused on Model Fidelity and Computational Load

et al. 2021

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The Industry 4.0 concept of a Digital Twin will bring many advantages for wind energy conversion systems, e.g., in condition monitoring, predictive maintenance and the optimisation of control or design parameters. A virtual replica is at the heart of a digital twin. To construct a virtual replica, appropriate modelling techniques must be selected for the turbine components. These models must be chosen with the intended use case of the digital twin in mind, finding a proper balance between the model fidelity and computational load. This review article presents an overview of the recent literature on modelling techniques for turbine aerodynamics, structure and drivetrain mechanics, the permanent magnet synchronous generator, the power electronic converter and the pitch and yaw systems. For each component, a balanced overview is given of models with varying model fidelity and computational load, ranging from simplified lumped parameter models to advanced numerical Finite Element Method (FEM)-based models. The results of the literature review are presented graphically to aid the reader in the model selection process. Based on this review, a high-level structure of a digital twin is proposed together with a virtual replica with a minimum computational load. The concept of a multi-level hierarchical virtual replica is presented.

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“…It is however also interesting to detect subtler failures, where a pitch system component sees its functionality degraded in a way that may impact turbine operation in the longer term without necessarily prompting an immediate reaction from the safety system, i.e., the pitch system still tracks the reference cylinder position, but its operational envelope is reduced, so it cannot handle wind conditions well within its design specifications. The potential benefits of a system capable of such detection are evident, and they have long elicited ample research, as shown by the detailed and current review of the literature provided by Asmussen et al [32], who point out that realistic methods able to cope with the stochastic nature of wind turbine behavior are yet to be developed (hence, this paper).…”

Section: Introductionmentioning

confidence: 99%

A Sensor Data Processing Algorithm for Wind Turbine Hydraulic Pitch System Diagnosis

et al. 2021

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Modern wind turbines depend on their blade pitch systems for start-ups, shutdowns, and power control. Pitch system failures have, therefore, a considerable impact on their operation and integrity. Hydraulic pitch systems are very common, due to their flexibility, maintainability, and cost; hence, the relevance of diagnostic algorithms specifically targeted at them. We propose one such algorithm based on sensor data available to the vast majority of turbine controllers, which we process to fit a model of the hydraulic pitch system to obtain significant indicators of the presence of the critical failure modes. This algorithm differs from state-of-the-art, model-based algorithms in that it does not numerically time-integrate the model equations in parallel with the physical turbine, which is demanding in terms of in situ computation (or, alternatively, data transmission) and is highly susceptible to drift. Our algorithm requires only a modest amount of local sensor data processing, which can be asynchronous and intermittent, to produce negligible quantities of data to be transmitted for remote storage and analysis. In order to validate our algorithm, we use synthetic data generated with state-of-the-art aeroelastic and hydraulic simulation software. The results suggest that a diagnosis of the critical wind turbine hydraulic pitch system failure modes based on our algorithm is viable.

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Fault Detection and Diagnosis Methods for Fluid Power Pitch System Components—A Review

Cited by 12 publications

References 37 publications

BMNet-5: A Novel Approach of Neural Network to Classify the Genre of Bengali Music Based on Audio Features

BMNet-5: A Novel Approach of Neural Network to Classify the Genre of Bengali Music Based on Audio Features

Digital Twins for Wind Energy Conversion Systems: A Literature Review of Potential Modelling Techniques Focused on Model Fidelity and Computational Load

A Sensor Data Processing Algorithm for Wind Turbine Hydraulic Pitch System Diagnosis

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