Discriminant Feature Extraction of Motor Current Signal Analysis and Vibration for Centrifugal Pump Fault Detection

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

The monitoring condition of the centrifugal pump is closely related to fault detection and diagnosis. It usually uses the vibration signals. However, under certain conditions it is not possible to install the accelerometer on the machine due to certain conditions and environments. Current signals can be used to replace vibration signals. This method is called motor current signature analysis (MCSA). The raw signal of the current and current spectrum in frequency domain can be used for fault detection. The statistical features of the current raw signal contain information on the signal characteristics. However, these raw features are not sensitive enough to weak fault symptoms or are not suitable for severe faults, thus can affect fault detection and classification accuracy. To overcome this problem, discriminant feature extraction is carried out for fault detection in centrifugal pumps (CP). Discriminant features are divided into four phases. In the first phase, a healthy pump signal is selected. In the second phase, the healthy condition signal is cross-correlated with the centrifugal pump current signal in several fault classes and the result of the extraction from the cross correlation is a new feature set. In the third phase, the raw statistical features in the time, frequency and time-frequency domains are extracted from both healthy current signals and CP current signals of different classes. In the last phase, wavelet packet transform (WPT) energy is extracted from the current signals. The result of these features will be combined into a discriminant feature pool. The pool discriminant feature will be used as input in making a classifier for the centrifugal pump fault detection system. This study also used motor bearing speed data for comparison. The main topic of this paper is to design a fault detection system for centrifugal pumps using current signals. Based on the performance test using precision, error rate, and recall. The motor bearing speed vibration signal has better performance than the CP fault detection classification with the current signal. However, there is only a slight difference between the two. From this research, the current signal and motor bearing speed vibration signal can detect fault to the centrifugal pump well.

Original languageEnglish
Title of host publicationProceedings of the 2021 International Conference on Instrumentation, Control, and Automation, ICA 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages207-212
Number of pages6
ISBN (Electronic)9781665432955
DOIs
Publication statusPublished - 2021
Event7th International Conference on Instrumentation, Control, and Automation, ICA 2021 - Bandung, Indonesia
Duration: 25 Aug 202127 Aug 2021

Publication series

NameProceedings of the 2021 International Conference on Instrumentation, Control, and Automation, ICA 2021

Conference

Conference7th International Conference on Instrumentation, Control, and Automation, ICA 2021
Country/TerritoryIndonesia
CityBandung
Period25/08/2127/08/21

Keywords

  • Centrifugal Pump
  • Classifier
  • Fault Detection
  • MCSA
  • Vibration

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