Comparison of dimensionality reduction techniques for the fault diagnosis of mono block centrifugal pump using vibration signals
| Autor: | M Elangovan, N. R. Sakthivel, S. Saravanmurugan, V. Sugumaran, Binoy B. Nair |
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| Rok vydání: | 2014 |
| Předmět: |
Engineering
Computer Networks and Communications Decision tree Statistical features Biomaterials Naive Bayes classifier Data acquisition Dimensionality reduction techniques Robustness (computer science) Civil and Structural Engineering Fluid Flow and Transfer Processes Data processing business.industry Bayes Net Mechanical Engineering Dimensionality reduction Mono block centrifugal pump Metals and Alloys Nonlinear dimensionality reduction Pattern recognition Centrifugal pump Naïve Bayes Electronic Optical and Magnetic Materials lcsh:TA1-2040 Hardware and Architecture Visual analysis Artificial intelligence lcsh:Engineering (General). Civil engineering (General) business |
| Zdroj: | Engineering Science and Technology, an International Journal, Vol 17, Iss 1, Pp 30-38 (2014) |
| ISSN: | 2215-0986 |
| DOI: | 10.1016/j.jestch.2014.02.005 |
| Popis: | Bearing fault, Impeller fault, seal fault and cavitation are the main causes of breakdown in a mono block centrifugal pump and hence, the detection and diagnosis of these mechanical faults in a mono block centrifugal pump is very crucial for its reliable operation. Based on a continuous acquisition of signals with a data acquisition system, it is possible to classify the faults. This is achieved by the extraction of features from the measured data and employing data mining approaches to explore the structural information hidden in the signals acquired. In the present study, statistical features derived from the vibration data are used as the features. In order to increase the robustness of the classifier and to reduce the data processing load, dimensionality reduction is necessary. In this paper dimensionality reduction is performed using traditional dimensionality reduction techniques and nonlinear dimensionality reduction techniques. The effectiveness of each dimensionality reduction technique is also verified using visual analysis. The reduced feature set is then classified using a decision tree. The results obtained are compared with those generated by classifiers such as Naive Bayes, Bayes Net and kNN. The effort is to bring out the better dimensionality reduction technique–classifier combination. |
| Databáze: | OpenAIRE |
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