Intelligent condition based monitoring of rotating machines using sparse auto-encoders
2013 IEEE Conference on prognostics and health management (PHM), 2013•ieeexplore.ieee.org
Support Vector Machine (SVM) has been very popular for use in machine fault diagnosis as
classifier. In most of the complex machine learning problems, the main challenge lies in
finding good features. Sparse autoencoders have the ability to learn good features from the
input data in an unsuperivised fashion. Sparse auto-encoders and other deep architectures
are already showing very good results in text classification, speaker and speech recognition
and face recognition as well. In this paper, we compare the performance of sparse …
classifier. In most of the complex machine learning problems, the main challenge lies in
finding good features. Sparse autoencoders have the ability to learn good features from the
input data in an unsuperivised fashion. Sparse auto-encoders and other deep architectures
are already showing very good results in text classification, speaker and speech recognition
and face recognition as well. In this paper, we compare the performance of sparse …
Support Vector Machine (SVM) has been very popular for use in machine fault diagnosis as classifier. In most of the complex machine learning problems, the main challenge lies in finding good features. Sparse autoencoders have the ability to learn good features from the input data in an unsuperivised fashion. Sparse auto-encoders and other deep architectures are already showing very good results in text classification, speaker and speech recognition and face recognition as well. In this paper, we compare the performance of sparse autoencoders with soft max regression, fast classifier based on Mahalanobis distance and SVM in fault diagnosis of air compressors.
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