Early Fault Detection in Wind Turbines

Multi-dimensional composite sensing technology and monitoring system for wind power to improve wind turbine reliability and reduce maintenance costs. The system uses a multi-sensor setup on the turbine body to monitor various components like bearings, gears, foundations, bolts, and oil. Sensors like accelerometers, inclinometers, gyroscopes, ultrasonic probes, and oil sensors provide comprehensive condition monitoring. Data is transmitted to a monitoring station and server for analysis and alarms. It allows early fault detection, prognosis, and optimization of wind turbine maintenance.

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Intelligent system for predicting wind turbine blade failures using deep learning to analyze blade temperatures, pressures, and images. The system extracts features from blade temperature and pressure time series, blade images, and correlates them to detect blade faults. It uses a neural network to fuse the features and a classifier to determine if a blade fault warning should be issued. The system trains the neural network with supplementary loss based on probability density consistency between features to improve feature fusion quality.

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Deep learning-based predictive maintenance system for wind turbines to improve reliability and reduce downtime compared to manual maintenance. The system uses sensors to monitor turbine parameters, processes the data, analyzes it with deep learning models to detect faults, and generates control signals to address issues before failures occur. The system sends the data to a central monitoring unit for analysis and decision making. It aims to provide more accurate, efficient, and automated maintenance compared to manual recording and inspection.

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Comprehensive online monitoring system for wind turbines to detect and diagnose issues with various components. The system uses a master station and four slave stations connected in sequence. The stations have sensors for blade load, main shaft torque, icing, atmospheric pressure, bearings, vibrations, currents, temperatures, and humidity at locations like the tower bottom, top, cabin, gearbox, generator, and motor.

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Wind farm monitoring system interaction method that improves fault diagnosis accuracy and efficiency to reduce maintenance costs. The method involves monitoring wind turbine component data, analyzing it using a basic diagnosis system to generate initial fault results, then allowing authorized personnel to customize the diagnosis criteria and repeat the analysis to get final results. Alarms are then provided based on multiple results and diagnostic reports are generated. This iterative customization improves fault diagnosis accuracy compared to fixed criteria.

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A real-time monitoring system for wind turbine tower tilt and sink based on multiple sensors that provides enhanced tower monitoring capabilities and alerts for wind turbines. The system uses sensors inside the wind turbine to detect tower tilt, settlement, internal environmental conditions, etc. The sensor data is transmitted wirelessly and processed by a central unit. Alarms are triggered if thresholds are exceeded and notifications sent via lights, sounds, SMS, calls, and web/app alerts. This multi-modal alerting improves response time and reliability compared to single methods. The cloud platform allows remote monitoring and analysis.

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Wind turbine fault diagnosis system that can efficiently monitor and diagnose faults in wind turbines with mixed equipment from different manufacturers. The system uses a modular design with a data acquisition module, signal acquisition card, signal processing module, and display module. The signal acquisition card can adapt to different data communication methods and formats of the wind turbine equipment. The signal processing module extracts fault features from the acquired signals. The display module presents the fault diagnosis results. This allows the system to monitor and diagnose faults across diverse wind turbine equipment types with mixed communication protocols.

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Wind turbine unit condition monitoring and fault diagnosis method using PHM technology that provides improved monitoring and diagnosis compared to traditional methods. The method involves monitoring wind turbine system health by fusing vibration and binary data using Copula functions. It also uses denoising and feature extraction techniques followed by SVM modeling to diagnose faults. This allows monitoring system-wide signals and binary variables, diagnosing both temporary and early faults, and improving fault detection rate and warning time.

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A wind turbine tower condition monitoring system that improves accuracy, reliability, automation, and safety compared to existing tower monitoring methods. The system uses sensors on the tower bolts and inclination angles to detect issues like loosening or leaning. A data acquisition module collects the sensor data, a router transmits it, and a data processing module analyzes it to detect problems. This allows real-time, automated monitoring and early warning of tower conditions.

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Lean management and control method for power grids using big data to improve accuracy and reliability of wind turbine control in wind farms. The method involves generating a sensor relationship diagram for the wind turbines and sensors, and leveraging big data techniques to analyze and model the sensor relationships. This allows predicting sensor faults and compensating for faulty sensors using data from healthy sensors. The steps include: 1) creating a sensor relationship diagram with units representing wind turbines and edges representing sensor connections; 2) generating initial features for each unit (wind turbine or sensor) using operating parameters; 3) training a relationship generation model to predict relationships between unit features based on the diagram; 4) using the model to predict faulty sensor behavior based on data from healthy sensors.

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A remote online monitoring system for wind power gearboxes that provides more accurate and reliable condition monitoring compared to vibration and temperature sensors alone. The system uses additional sensors like strain gauges at the gearbox input, pressure sensors at the oil inlet and outlet, and connects them to a computer via controllers. This allows monitoring of factors like torque, bending moment, oil pressure, in addition to vibration and temperature. The computer collects the sensor data and sends it over the network for analysis and alerts if needed. The system architecture includes firewall, router, and receiving terminal to securely transmit the monitored data offsite.

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A remote fault detection system for offshore wind turbines that allows monitoring and analysis of the turbine components to predict faults and improve maintenance. The system uses sensors to monitor blades, transmission shafts, and generators, transmits the data to a control center, performs calculations and analysis, and uses neural networks to predict faults based on operating conditions. This allows remote fault detection and diagnosis for offshore turbines where maintenance is difficult.

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Wind turbine fault monitoring system using current analysis to detect mechanical and electrical faults with higher accuracy compared to vibration-based methods. The system involves placing harmonic sensors on the main power cables of the wind turbine stator to measure current harmonics. By analyzing the harmonic content, it can diagnose deterioration and failures in components like generators, transformers, and cables. Harmonic energy is related to component frequencies and degradation levels. Increased harmonic content indicates energy loss, component failure signs, or overall equipment deterioration.

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A monitoring alarm system for wind turbines in power plants that provides real-time fault detection and quick fault localization to improve turbine reliability and uptime. The system monitors temperature and amplitude signals from critical points on the turbine. It processes the signals to extract diagnostic parameters that are insensitive to operating conditions but sensitive to faults. Threshold alarms are set for these parameters. If a parameter exceeds its threshold, the system alerts the operator and quickly traces the fault location to enable faster repairs. This allows early detection and isolation of faults before they propagate, reducing downtime and maintenance costs.

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Wind turbine condition monitoring system that uses wireless sensors to provide real-time monitoring of wind turbine components without the need for wired connections. The system has multiple wireless sensors placed at various locations in the turbine. These sensors collect data which is wirelessly transmitted to a central control module. The control module processes the data and provides feedback on any issues. This allows proactive maintenance and fault prediction by monitoring multiple components wirelessly instead of relying on wired harnesses.

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Monitoring and managing wind power generators using multi-sensor data to improve fault detection and diagnosis. The system collects sensor data from various systems inside the wind turbine like the wind measurement, pitch control, generator cooling, etc. It identifies abnormal fluctuations in the sensor data over time. By analyzing the characteristics of abnormal fluctuations, it can discover hidden faults and correlations between sensors. This allows identifying and diagnosing multi-component failures that may not be immediately apparent from individual sensor data.

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Wind turbine fault diagnosis and health monitoring system that uses sensors on the blades to comprehensively gather blade operating condition parameters. It analyzes real-time vibration, speed, and temperature data to identify wind turbine faults and monitor health. Preprocessing techniques like wavelet denoising and outlier removal are applied to the signals. Neural networks diagnose vibration faults. Anomaly detection determines critical early warning states compared to historical data.

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Fault protection system for wind turbine power generation systems that can detect and respond to faults in permanent magnet generators. The system monitors parameters like generator speed, converter status, and circuit breaker operation to determine the wind turbine operating mode. It then sets expected values for parameters like voltage and speed based on the mode. If actual values deviate, it identifies and responds to fault conditions. This allows more accurate and reliable fault detection in permanent magnet generators compared to just monitoring current thresholds.

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A fault diagnosis system for wind turbine drivetrains using a network of sensors in key components to provide more accurate and comprehensive fault detection compared to traditional methods. The system uses a mesh networking strategy with sensors in components like gearboxes, shafts, bearings, and lubrication systems that transmit data wirelessly. It collects parameters like vibration, temperature, lubrication quality, and audio. The sensor data is transmitted using 5G frequencies for stability. The collected data is analyzed to diagnose faults and prioritize maintenance. The system provides more detailed and accurate fault detection compared to just monitoring vibration or a few parameters.

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Fault diagnosis device and method for wind turbine transmission systems that enables proactive maintenance and fault mitigation by monitoring and analyzing equipment conditions using sensors, wireless communication, and machine learning models. The device has a monitoring system with sensors for parameters like lubricating oil temperature, quality, and vibration, as well as audio monitoring. It wirelessly transmits the data to a central server using a mesh network. The diagnosis system extracts features from the sensor data and uses a causal inference model to predict faults and their causes. This provides specific fault mitigation actions and allows confirming the failure root cause by observing sensor changes.

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