Rough Sets and Relational Learning

Milton, Richard; Maheswari, V. Uma; Siromoney, Arul

doi:10.1007/978-3-540-27794-1_15

Cited by 7 publications

(3 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The rough set approach is of fundamental importance to AI and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, and knowledge discovery from databases [20].…”

Section: Knowledge Acquisition By Rough Setsmentioning

confidence: 99%

Intelligent diagnosis method for a centrifugal pump using features of vibration signals

Wang

Peng

2008

Neural Comput & Applic

View full text Add to dashboard Cite

In the field of machinery diagnosis, the utilization of vibration signals is effective in the detection of fault, because the signals carry dynamic information about the machine state. However, knowledge of a distinguishing fault is ambiguous because definite relationships between symptoms and fault types cannot be easily identified. This paper presents an intelligent diagnosis method for a centrifugal pump system using features of vibration signals at an early stage. The diagnosis algorithm is derived using wavelet transform, rough sets and a partially linearized neural network (PNN). ReverseBior wavelet function is used to extract fault features from measured vibration signals and to capture hidden fault information across optimum frequency regions. As the input parameters for the neural network, the nondimensional symptom parameters that can reflect the characteristics of a signal are defined in the amplitude domain. The diagnosis knowledge for the training of the PNN can be acquired by using the rough sets. We also propose a diagnosis method based on the PNN, one which can deal with the ambiguity problem of condition diagnosis, and distinguish fault types on the basis of the possibility distributions of symptom parameters automatically. The decision method of optimum frequency region for extracting feature signals is also discussed using real plant data. Practical examples of diagnosis for a centrifugal pump system are shown in order to verify the efficiency of the method.

show abstract

Section: Knowledge Acquisition By Rough Setsmentioning

confidence: 99%

Intelligent diagnosis method for a centrifugal pump using features of vibration signals

Wang

Peng

2008

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Intelligent Method for Condition Diagnosis of Pump System Using Discrete Wavelet Transform, Rough Sets and Neural Network

Wang

Chen

2007

2007 Second International Conference on Bio-Inspired Computing: Theories and Applications

View full text Add to dashboard Cite

An intelligent method for condition diagnosis of a pump system is proposed using the discrete wavelet transform (DWT), rough sets (RS), and a neural network to detect faults and distinguish fault types at an early stage. The Daubechies wavelet function is used to extract fault features from measured vibration signals and to capture hidden fault information across an optimum frequency region. We also propose a new diagnosis method based on a fuzzy neural network realized by the partially-linearized neural network (PNN), one which can automatically distinguish fault types on the basis of the probability distributions of symptom parameters. The diagnosis knowledge for the training of the PNN can be acquired by using the RS. The PNN can deal with the ambiguity problem of diagnosis, and is always convergent. Practical examples of diagnosis for a centrifugal pump system are shown in order to verify the efficiency of the method.

show abstract

“…Relational Learning is based on multiple tables. A survey of research in Rough Sets and Relational Learning is presented in [MUS04]. Statistical relational learning (SRL) is a recent area of research, concerned with developing techniques to learn statistical models from relational data, and information is available at http://robotics.stanford.edu/srl/.…”

Section: Introductionmentioning

confidence: 99%