Year: 2012
Type: book-chapter
Abstract: Centrifugal Pumps 16 motor-pump system that is faulty.The unique contribution of this work is that it uses only the motor electrical signals to detect and isolate faults in the motor and the pump.Moreover, it does not presume the existing "health" condition of either the motor or the pump and detects the degradation of the system from the current state. Literature reviewMost of the literature on fault detection of centrifugal pumps is based on techniques that require the measurement of either vibration or other process based signals.There are very few peer-reviewed publications that deal with non-invasive/non-intrusive techniques to diagnose faults in centrifugal pumps.Even fewer literatures are available on the isolation of faults between the pump and the motor driving the pump.In this chapter, only the publications that deal with detecting centrifugal pump faults using motor electrical signals are reviewed.In (Dister, 2003), the authors review the latest techniques that are used in pump diagnostics.Hardware and software algorithms required to make accurate assessment of the pump condition are also discussed.Lists of typical problems that develop in the pump along with the conventional methods of detection are presented.In (Siegler, 1994), the authors describe the development and application of signal processing routines for the detection of eroded impeller condition of a centrifugal pump found in submarines.Fault features are extracted from the power spectrum and a neural networks-based classification scheme based on the nearest neighborhood technique classifies about 90%of the test cases correctly.In (Casada, 1994(Casada, , 1995(Casada, , 1996a) ) and (Casada & Bunch, 1996b), motor current and power analysis is used to detect some operational and structural problems such as clogged suction strainer and equipment misalignment.Load related peaks from the power or current spectrum are used as fault indicators in the proposed scheme.A comparative study between the vibration spectrum-based, power spectrum-based and torque spectrum-based detection methods is also described in detail.The authors conclude that the motor-monitored parameters are much more sensitive than the vibration transducers in detecting effects of unsteady process conditions resulting from both system and process specific sources.In (Kenull et al., 1997), the energy content of the motor current signal in specific frequency ranges are used as fault indicators to detect faults that occur in centrifugal pumps, namely, partial flow operation, cavitation, reverse rotation, etc.The work in (Dalton & Patton, 1998) deals with the development of a multi-model fault diagnosis system of an industrial pumping system.Two different approaches to modelbased fault detection are outlined based on observers and parameter estimation.In (Perovic, Unsworth & Higham, 2001), fault signatures are extracted from the motor current spectrum by relating the spectral features to the individual faults to detect cavitation, blockage and damaged impeller condition.A fuzzy logic system is also developed to classify the three faults.The authors conclude that the probability of fault detection varies from 50% to 93%.The authors also conclude that adjustments to the rules or the membership functions are required so that differences in the pump design and operating flow regimes can be taken into consideration.In (Schmalz & Schuchmann, 2004), the spectral energy within the band of about 5 Hz to 25 Hz is calculated and is used to detect the presence of cavitation or low flow condition in centrifugal pumps.In (Welch et al., 2005) and(Haynes et al., 2002), the electrical signal analysis is extended to condition monitoring of aircraft fuel pumps.The front bearing wear of auxiliary pumps is selected to demonstrate the effectiveness of the proposed algorithm.The authors after considerable study establish that the best indicator of front www.intechopen.com
Source: InTech eBooks
DOI: 10.5772/26439
Authors: Parasuram P., Alexander G.
Institutions: Corning (United States), Texas A&M University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W1538963598