Fault Diagnosis Method for Wind Power Equipment Based on Hidden Markov Model

Abstract

The aging of mechanical equipment brings a lot of inconvenience to its application. Therefore, research on fault diagnosis technology of mechanical equipment is of great significance for maintaining equipment safety, improving production efficiency and reliability. Bearings and gears are one of the key components of mechanical equipment, and their working conditions seriously affect the changes in equipment performance. Therefore, the fault diagnosis and performance degradation assessment of bearings and gears have always been the research focus of equipment fault diagnosis. This paper proposes a multichannel information fusion method based on the coupled hidden Markov model, discusses the application of the coupled hidden Markov model in bearing fault diagnosis and performance degradation assessment, and studies the multichannel information fusion method based on the coupled hidden Markov model. Channel monitoring data performance degradation evaluation modeling and performance index calculation method are used, and the calculation method of adaptive alarm limit is given by using the performance index. Finally, the natural failure test data and accelerated fatigue test data of gears and rolling bearings are used to analyze and verify the effectiveness of the coupled hidden Markov model for evaluating the performance degradation of complete and incomplete data. The results prove that the selected performance indicators can be quantified, reflecting the degree of bearing performance degradation.