Power wind mill fault detection via one-class ν-SVM vibration signal analysis

Vibration analysis is one of the most used techniques for predictive maintenance in high-speed rotating machinery. Using the information contained in the vibration signals, a system for alarm detection and diagnosis of failures in mechanical components of power wind mills is devised. As previous failure data collection is unfeasible in real life scenarios, the method to be employed should be capable of discerning between failure and normal data, being only trained with the latter type. Other interesting capability of such a method is the possibility of measuring the evolution of the failure. Taking into account these restrictions, a method that uses the one-class-ν-SVM paradigm is employed. In order to test its adequacy, three different scenarios are tested: (a) a simulated scenario, (b) a controlled experimental scenario with real vibrational data, and (c) a real scenario using vibrational data captured from a windmill power machine installed in a wind farm in North West Spain. The results showed not only the capabilities of the method for detecting the failure in advance to the breakpoint of the component in all three scenarios, but also its capacity to present a qualitative indication on the evolution of the defect. Finally, the results of the SVM paradigm are compared to one of the most used novelty detection methods, obtaining more accurate results under noisy circumstances.