Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Computers in Industry - Special issue: E-maintenance
Neuro-fuzzy networks and their application to fault detection of dynamical systems
Engineering Applications of Artificial Intelligence
Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference
Expert Systems with Applications: An International Journal
Prior knowledge based identification of takagi-sugeno-kang fuzzy models for static nonlinear systems
Prior knowledge based identification of takagi-sugeno-kang fuzzy models for static nonlinear systems
On fuzzy logic applications for automatic control, supervision, and fault diagnosis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Global geometric similarity scheme for feature selection in fault diagnosis
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
The fast growing wind industry has shown a need for more sophisticated fault prognosis analysis in the critical and high value components of a wind turbine (WT). Current WT studies focus on improving their reliability and reducing the cost of energy, particularly when WTs are operated offshore. WT Supervisory Control and Data Acquisition (SCADA) systems contain alarms and signals that could provide an early indication of component fault and allow the operator to plan system repair prior to complete failure. Several research programmes have been made for that purpose; however, the resulting cost savings are limited because of the data complexity and relatively low number of failures that can be easily detected in early stages. A new fault prognosis procedure is proposed in this paper using a-priori knowledge-based Adaptive Neuro-Fuzzy Inference System (ANFIS). This has the aim to achieve automated detection of significant pitch faults, which are known to be significant failure modes. With the advantage of a-priori knowledge incorporation, the proposed system has improved ability to interpret the previously unseen conditions and thus fault diagnoses are improved. In order to construct the proposed system, the data of the 6 known WT pitch faults were used to train the system with a-priori knowledge incorporated. The effectiveness of the approach was demonstrated using three metrics: (1) the trained system was tested in a new wind farm containing 26 WTs to show its prognosis ability; (2) the first test result was compared to a general alarm approach; (3) a Confusion Matrix analysis was made to demonstrate the accuracy of the proposed approach. The result of this research has demonstrated that the proposed a-priori knowledge-based ANFIS (APK-ANFIS) approach has strong potential for WT pitch fault prognosis.