Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding
نویسندگان
چکیده
منابع مشابه
Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding
Fault diagnosis is essentially a kind of pattern recognition. The measured signal samples usually distribute on nonlinear low-dimensional manifolds embedded in the high-dimensional signal space, so how to implement feature extraction, dimensionality reduction and improve recognition performance is a crucial task. In this paper a novel machinery fault diagnosis approach based on a statistical lo...
متن کاملFault Diagnosis Method Based on a New Supervised Locally Linear Embedding Algorithm for Rolling Bearing
In view of the complexity and nonlinearity of rolling bearings, this paper presents a new supervised locally linear embedding method (R-NSLLE) for feature extraction. In general, traditional LLE can capture the local structure of a rolling bearing. However it may lead to limited effectiveness if data is sparse or non-uniformly distributed. Moreover, like other manifold learning algorithms, the ...
متن کاملBearing Fault Diagnosis Based on Vibration Signals
The vibration signal obtained from operating machines contains information relating to machine condition as well as noise. Further processing of the signal is necessary to elicit information particularly relevant to bearing faults. Many techniques have been employed to process the vibration signals in bearing faults detection and diagnosis. Two common techniques, time domain techniques and freq...
متن کاملNeighbor Line-Based Locally Linear Embedding
Locally linear embedding (Lle) is a powerful approach for mapping high-dimensional data nonlinearly to a lower-dimensional space. However, when the training examples are not densely sampled, Lle often returns invalid results. In this paper, the Nle (Neighbor Line-based Lle) approach is proposed, which generates some virtual examples with the help of neighbor line such that the Lle learning can ...
متن کاملGuided Locally Linear Embedding
Nonlinear dimensionality reduction is the problem of retrieving a low-dimensional representation of a manifold that is embedded in a high-dimensional observation space. Locally Linear Embedding (LLE), a prominent dimensionality reduction technique is an unsupervised algorithm; as such, it is not possible to guide it toward modes of variability that may be of particular interest. This paper prop...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Sensors
سال: 2015
ISSN: 1424-8220
DOI: 10.3390/s150716225