This study is primary to develop relevant techniques for the bearing of wind turbine, such as the intelligent monitoring system, the performance assessment, future trend prediction and possible fault classification etc. The main technique of system monitoring and diagnosis is divided into three algorithms, such as the performance assessment, performance prediction and fault diagnosis, respectively. Among them, the Logistic Regression (LR) is adopted to assess the bearing performance condition, the Autoregressive Moving Average (ARMA) is adopted to predict the future variation trend of bearing, and the Support Vector Machine (SVM) is adopted to classify and diagnose the possible fault of bearing. Through testing, this intelligent monitoring system can achieve real-time vibration monitoring, current performance assessment, future performance trend prediction and possible fault classification for the bearing of wind turbine. The monitor and analysis data and knowledge not only can be used as the basis of predictive maintenance, but also can be stored in the database for follow-up off-line analysis and used as the reference for improvement of operation parameter and wind turbine system design.
The evolution of the wind turbine to generate carbon-free renewable energy is rapidly growing. Thus, performing maintenance and inspection tasks in high altitude environments or difficult to access places, and even bad weather conditions, poses a problem for the periodic inspection process of the wind turbine industry. This paper describes the design and development of a scaled-down prototype climbing robot for wind turbine maintenance to perform critical tower operations. Thus, the unique feature of this maintenance robot is the winding mechanism, which uses a tension force to grip on the tower surface without falling to the ground either in static or dynamic situations, with the locomotion to perform a straight up–down motion in a circular truncated cone and the stability to work at significant heights. The robot computer-aided design (CAD) model of the mechanical mechanism, force and structural analysis, and the testing of the prototype model, are addressed in this paper. The key hardware developments that were utilized to build a low-cost, reliable and compact climbing robot are the embedded microprocessors, brushed DC motors, stepper motors and steel rope. This paper concludes with a successful preliminary experiment of a scaled down prototype proving the functionality of the concept. The potential applications for this robot are industrial maintenance, inspection and exploration, security and surveillance, cleaning, painting, and welding at extreme height conditions.
Wind energy is becoming a common source of renewable energy in the world. Wind turbines are increasing in number, both for onshore and offshore applications. One challenge with wind turbines is in detecting anomalies that cause their breakdown. Due to the complex nature of the wind turbine assembly, it is quite an extensive process to detect causes of malfunctions in the system. This study uses the Mahalanobis distance (MD) to detect anomalies in wind turbine operation, using SCADA alarm data as a comparison. Different predictive models were generated as the bases for analyses in MD computations. Using the SCADA alarm data as a reference, trend patterns that deviated from the threshold value were compared. Results showed that the MD could be used to detect anomalies within a group of data sets, with behaviors learned based on the model used. A large portion of those data sets deviated from the threshold level, corresponding to serious alarms in the SCADA data. We concluded that the MD can detect anomalies in different wind turbine components, based on this study. MD analysis of models can be used in conditions monitoring systems of wind turbines.
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