Faults and wear in hydraulic pitch actuators of wind turbines significantly contribute to downtime of remote off-shore wind farms. This study presents a method for estimating incipient friction in the hydraulic actuat...
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Faults and wear in hydraulic pitch actuators of wind turbines significantly contribute to downtime of remote off-shore wind farms. This study presents a method for estimating incipient friction in the hydraulic actuator, which is directly associated with faults and wear in the pitch system. An additional sensor and a sliding mode differentiator are employed to estimate the total friction, which is then used in a modified least-squares scheme to obtain the Coulomb friction coefficient. Statistical change detection is used to robustify the friction monitoring scheme against model uncertainty and sensor noise. Simulation tests demonstrate the efficacy of the proposed method. Copyright (c) 2024 The Authors.
Subspace-based fault detection methods are widely used for linear time-invariant systems. For linear time-periodic systems, those methods cannot be theoretically used, due to the intrinsic assumptions associated with ...
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ISBN:
(纸本)9781713872344
Subspace-based fault detection methods are widely used for linear time-invariant systems. For linear time-periodic systems, those methods cannot be theoretically used, due to the intrinsic assumptions associated with those methods in the context of linear time-invariant models. based on the approximation of time-periodic systems as time-invariant ones, those methods can still be applied and adapted to perform change detection for time-periodic systems, through a Gaussian residual built upon the identified modal parameters and their estimated variances. The proposed method is tested and validated on a small numerical model of a rotating wind turbine, with detection and isolation of a blade stiffness reduction leading to rotor anisotropy. Copyright (c) 2023 The Authors.
Faults and wear in hydraulic pitch actuators of wind turbines significantly contribute to downtime of remote of-shore wind farms. This study presents a method for estimating incipient friction in the hydraulic actuato...
详细信息
Faults and wear in hydraulic pitch actuators of wind turbines significantly contribute to downtime of remote of-shore wind farms. This study presents a method for estimating incipient friction in the hydraulic actuator, which is directly associated with faults and wear in the pitch system. An additional sensor and a sliding mode differentiator are employed to estimate the total friction, which is then used in a modified least-squares scheme to obtain the Coulomb friction coefficient. Statistical change detection is used to robustify the friction monitoring scheme against model uncertainty and sensor noise. Simulation tests demonstrate the efficacy of the proposed method.
Subspace-based fault detection methods are widely used for linear time-invariant systems. For linear time-periodic systems, those methods cannot be theoretically used, due to the intrinsic assumptions associated with ...
详细信息
Subspace-based fault detection methods are widely used for linear time-invariant systems. For linear time-periodic systems, those methods cannot be theoretically used, due to the intrinsic assumptions associated with those methods in the context of linear time-invariant models. based on the approximation of time-periodic systems as time-invariant ones, those methods can still be applied and adapted to perform change detection for time-periodic systems, through a Gaussian residual built upon the identified modal parameters and their estimated variances. The proposed method is tested and validated on a small numerical model of a rotating wind turbine, with detection and isolation of a blade stiffness reduction leading to rotor anisotropy.
In modern manufacturing, each stage of industrial processes is accurately measured via multiple sensors and, consequently, a large amount of data is made available for analytics, monitoring and control purposes. A pos...
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In modern manufacturing, each stage of industrial processes is accurately measured via multiple sensors and, consequently, a large amount of data is made available for analytics, monitoring and control purposes. A possible use of such data is to detect anomalies in order to prevent potential damages and hazards. In this paper, we will consider a sensor setup returning distributed time series measurements that can be used for failure identification. In particular, an anomaly detection strategy based on Vector Autoregressive (VAR) modeling for multivariate time series will be presented and analyzed in detail. The effectiveness of the proposed methodology will be assessed on experimental data from a real industrial case study. Copyright (C) 2021 The Authors.
In modern manufacturing, each stage of industrial processes is accurately measured via multiple sensors and, consequently, a large amount of data is made available for analytics, monitoring and control purposes. A pos...
详细信息
In modern manufacturing, each stage of industrial processes is accurately measured via multiple sensors and, consequently, a large amount of data is made available for analytics, monitoring and control purposes. A possible use of such data is to detect anomalies in order to prevent potential damages and hazards. In this paper, we will consider a sensor setup returning distributed time series measurements that can be used for failure identification. In particular, an anomaly detection strategy based on Vector Autoregressive (VAR) modeling for multivariate time series will be presented and analyzed in detail. The effectiveness of the proposed methodology will be assessed on experimental data from a real industrial case study.
Abstract Safety requirements of technological processes trigger an increased demand for elaborate fault diagnosis tools. However, abrupt changes in system behavior are hard to formulate with continuous models but easi...
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Abstract Safety requirements of technological processes trigger an increased demand for elaborate fault diagnosis tools. However, abrupt changes in system behavior are hard to formulate with continuous models but easier to represent in terms of hybrid systems. Therefore, we propose a set-based approach for complete fault diagnosis of hybrid polynomial systems formulated as a feasibility problem. We employ mixed-integer linear program relaxation of this formulation to exploit the presence of discrete variables. We improve the relaxation with additional constraints for the discrete variables. The efficiency of the method is illustrated with a simple two-tank example subject to multiple faults.
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