Abstract
To enhance the effect of fault diagnosis, a new fualt diagnosis method based on supervised incremental local tangent space alignment (SILTSA) and support vector machine (SVM) is proposed. The supervised learning approach is embedded into the incremental local tangent space alignment algorithm, to realize fault feature extraction and new data processing for equipment fault signal, and then correctly classify the faults by non-linear support vector machines. The experiment result for roller bearing fault diagnosis shows that SILTSA-SVM method has better diagnosis effect to related methods
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have