Sensorless Detection of Impeller Cracks in Motor Driven Centrifugal Pumps

2008 ◽  
Author(s):  
Parasuram P. Harihara ◽  
Alexander G. Parlos
Keyword(s):  
Fault Detection ◽  
Induction Motor ◽  
Detection Algorithm ◽  
Vibration Monitoring ◽  
Design Parameters ◽  
False Alarms ◽  
Centrifugal Pumps ◽  
Electrical Measurements ◽  
Detection Scheme ◽  
Mechanical Sensors

Electrical signal analysis has been in use for quite some time to detect and diagnose induction motor faults. In most industrial applications, induction motors are used to drive dynamic loads such as pumps, compressors, fans, etc. Failure of either the motors or the driven loads results in an unscheduled downtime which in turn leads to loss of production. These operational disruptions could be avoided if the equipment degradation is detected in its early stages prior to reaching catastrophic failure conditions. Hence the need arises for cost-effective detection schemes not only for assessing the condition of electric motors but also the driven loads. This paper presents an experimentally demonstrated sensor-less approach to detect impeller cracks in centrifugal pumps. The proposed method is sensorless in the sense that it does not use any mechanical and/or process pump sensors to detect impeller faults. Rather motor electrical measurements are used for the intended purpose. Mechanical sensors have high costs and low reliability, and frequently fail more often than the system being monitored. Hence add-on mechanical sensors reduce the overall system reliability. In this study, fault detection is accomplished using only the line voltages and phase currents of the electric motor driving the pump. The developed detection algorithm is insensitive to electrical power supply and load variations. Furthermore, it does not require prior knowledge of either a motor or the pump model or design parameters and hence the detection algorithm can be easily ported to motor-pump systems of varying manufacturers and sizes. The proposed fault detection scheme has been tested on data collected from a centrifugal pump driven by a 3-φ, 3 hp induction motor. Several cracks on the pump impeller are staged to validate the detection effectiveness of the proposed scheme and compare its effectiveness with respect to continuous vibration monitoring. In addition to these staged faults, experiments are also conducted to demonstrate the prevention of false alarms by the algorithm. Results from all of these experiments are presented to substantiate the performance of the sensorless pump fault detection scheme.

Sensorless Detection of Cavitation in Centrifugal Pumps

2006 ◽  
Author(s):  
Parasuram P. Harihara ◽  
Alexander G. Parlos
Keyword(s):  
Mechanical Load ◽  
A Priori ◽  
Detection Threshold ◽  
Cost Effective ◽  
Detection Algorithm ◽  
Design Parameters ◽  
Centrifugal Pumps ◽  
Detection Scheme ◽  
Pump Design ◽  
Electric Power Supply

Analysis of electrical signatures has been in use for some time for estimating the condition of induction motors, by extracting spectral indicators from motor current waveforms. In most applications, motors are used to drive dynamic loads, such as pumps, fans, and blowers, by means of power transmission devices, such as belts, couplers, gear-boxes. Failure of either the electric motors or the driven loads is associated with operational disruption. The large costs associated with the resulting idle equipment and personnel can often be avoided if the degradation is detected in its early stages prior to reaching failure conditions. Hence the need arises for cost-effective detection schemes not only for assessing the condition of the motor but also of the driven load. This prompts one to consider approaches that use no add-on sensors, in order to avoid any reduction in overall system reliability and increased costs. This paper presents an experimentally demonstrated sensorless approach to detecting varying levels of cavitation in centrifugal pumps. The proposed approach is sensorless in the sense that no mechanical sensors are required on either the pump or the motor driving the pump. Rather, onset of pump cavitation is detected using only the line voltages and phase currents of the electric motor driving the pump. Moreover, most industrial motor switchgear are equipped with potential transformers and current transformers which can be used to measure the motor voltages and currents. The developed fault detection scheme is insensitive to electric power supply and mechanical load variations. Furthermore, it does not require a priori knowledge of a motor or pump model or any detailed motor or pump design parameters; a model of the system is adaptively estimated on-line. The developed detection algorithm has been tested on data collected from a centrifugal pump connected to a 3 φ, 3 hp induction motor. Several cavitation levels are staged with increased severity. In addition to these staged pump faults, extensive experiments are also conducted to test the false alarm performance of the algorithm. Results from these experiments allow us to offer the conclusion that for the cases under consideration, the proposed model-based detection scheme reveals cavitation detection times that are comparable to those obtained from vibration analysis with a detection threshold that is significantly lower than used in industrial practice.

scholarly journals Evaluation of the Correlation Coefficient as a Prognostic Indicator for Electromechanical Servomechanism Failures

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Matteo D. L. Dalla Vedova ◽  
Paolo Maggiore ◽  
Lorenzo Pace ◽  
Alessio Desando
Keyword(s):  
Fault Detection ◽  
Short Circuit ◽  
Prognostic Indicator ◽  
Detection Algorithm ◽  
Anomalous Behavior ◽  
False Alarms ◽  
Test Environment ◽  
Electromechanical Actuator ◽  
Degradation Pattern ◽  
Identification Technique

In order to identify incipient failures due to a progressive wear of a primary flight command electromechanical actuator, several approaches could be employed; the choice of the best ones is driven by the efficacy shown in fault detection/identification, since not all the algorithms might be useful for the proposed purpose. In other words, some of them could be suitable only for certain applications while they could not give useful results for others. Developing a fault detection algorithm able to identify the precursors of the abovementioned electromechanical actuator (EMA) failure and its degradation pattern is thus beneficial for anticipating the incoming malfunction and alerting the maintenance crew such to properly schedule the servomechanism replacement. The research presented in the paper was focused to develop a fault detection/identification technique, able to identify symptoms alerting that an EMA component is degrading and will eventually exhibit an anomalous behavior, and to evaluate its potential use as prognostic indicator for the considered progressive faults (i.e. frictions and mechanical backlash acting on transmission, stator coil short circuit, rotor static eccentricity). To this purpose, an innovative model based fault detection technique has been developed merging several information achieved by means of Fast Fourier Transform (FFT) analysis and proper "failure precursors" (calculated by comparing the actual EMA responses with the expected ones). To assess the performance of the proposed technique, an appropriate simulation test environment was developed: the results showed an adequaterobustness and confidence was gained in the ability to early identify an eventual EMA malfunctioning with low risk of false alarms or missed failures.

A Neural Network Model Based Approach to Detect Seal and Impeller Failures in Centrifugal Pumps

2007 ◽  
Author(s):  
Balaje T. Thumati ◽  
Jeffery Birt ◽  
Neha Bassi ◽  
Jag Sarangapani
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Failure Modes ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Outlet Temperature ◽  
Centrifugal Pumps ◽  
Test Bed ◽  
Detection Scheme ◽  
Outlet Flow

With the increased complexity of today’s industrial processes, maintaining equipment by preventing unscheduled downtime using monitoring hardware is a key challenge. Industrial statistics indicate that seal and impeller failures are predominant failure modes in centrifugal pumps and they are not adequately addressed in the literature. In this paper, a neural network (NN) based Nonlinear Autoregressive Moving Average with Exogenous input (NARMAX) model is used to develop fault detection scheme for detecting seal and impeller failures in centrifugal pumps. A rigorous methodology of detecting failures at the incipient stage is introduced. First a nonlinear relationship among the monitored parameters (inlet and outlet pressure, outlet flow, inlet and outlet temperature, and acceleration) where the previous values of the indicative parameters are used as inputs to the NARMAX model and the output being the value at the current instance is captured. The NARMAX modeled outputs are compared with the actual measured values in order to generate residuals. By choosing a suitable threshold, we could minimize false and missed alarms. Mathematical procedure for selection of threshold is derived in this paper. Along with the NARMAX model, an online approximator is used in the fault detection scheme for understanding the faults in the system. Experiments on the centrifugal pump seal and impeller failures were conducted by using a laboratory test bed. Experimental results show that the proposed fault detection scheme is able to successfully detect failures.

scholarly journals LFT Bond Graph for Online Robust Fault Detection and Isolation of Hybrid Multi-Source System

2021 ◽  
Vol 2065 (1) ◽  
pp. 012010
Author(s):  
Mahdi Boukerdja ◽  
Youness Radi ◽  
Omprakash ◽  
Sumit Sood ◽  
Belkacem Ould-Bouamama ◽  
...  
Keyword(s):  
Fault Detection ◽  
Online Monitoring ◽  
Detection Algorithm ◽  
Bond Graph ◽  
Fault Detection And Isolation ◽  
False Alarms ◽  
Parameter Uncertainties ◽  
Safety Issues ◽  
Robust Fault Detection ◽  
Production Of Hydrogen

Abstract Green hydrogen is undoubtedly the most promising energy vector of the future because it is captured by renewable and inexhaustible sources, such as wind and/or solar energy, and can be stored over the long in high-pressure cylinders, which can be used to feed the fuel cells to produce the electricity without emitting any pollutants. The system incorporated renewable sources and process used to produce the green hydrogen is the hybrid multi-source system (HMS). The production of hydrogen needs a reliable HMS, which always requires online monitoring for real-time Fault Detection and Isolation (FDI) because the risk of accidents in HMS and safety issues increases due to the possibility of faults. However, online monitoring of FDI is challenging due to the multi-physics dynamics of HMS and the inclusion of uncertain parameters and several disturbances. This paper proposes an online robust fault detection algorithm to detect system faults based on the properties of the graphical linear fractional transformation bond graph (LFT-BG) modeling approach. Here, the analytical redundancy relations (ARRs) and their uncertain parts extracted from the LFT-BG model are used to develop an online robust FDI algorithm for HMS. Numerical evaluations of ARRs and their uncertain parts, respectively, generate the residual signals known as “faults indicators” and their uncertain bounds known as “adaptive thresholds.” These thresholds evolve with system variables in the presence of parameter uncertainties for ensuring robust FDI for HMS to minimize false alarms. The validation of this approach is carried out using 20sim software that is familiar with BG modeling.

scholarly journals Robustification of fault detection algorithm in a three‐phase induction motor using MCSA for various single and multiple faults

2021 ◽  
Vol 15 (5) ◽  
pp. 593-615
Author(s):  
K. C. Deekshit Kompella ◽  
Naga Sreenivasu Rongala ◽  
Srinivasa Rao Rayapudi ◽  
Venu Gopala Rao Mannam
Keyword(s):  
Fault Detection ◽  
Induction Motor ◽  
Detection Algorithm ◽  
Multiple Faults ◽  
Three Phase

scholarly journals Induction Motor Broken Rotor Bar Detection Based on Rotor Flux Angle Monitoring

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 794 ◽  
Author(s):  
Mitja Nemec ◽  
Vanja Ambrožič ◽  
Rastko Fišer ◽  
David Nedeljković ◽  
Klemen Drobnič
Keyword(s):  
Fault Detection ◽  
Induction Motor ◽  
Frequency Component ◽  
Detection Algorithm ◽  
Broken Rotor Bars ◽  
Fault Severity ◽  
Internal States ◽  
Rotor Flux ◽  
The Difference ◽  
Operating Points

This paper presents a method for the detection of broken rotor bars in an induction motor. After introducing a simplified dynamic model of an induction motor with broken cage bars in a rotor field reference frame which allows for observation of its internal states, a fault detection algorithm is proposed. Two different motor estimation models are used, and the difference between their rotor flux angles is extracted. A particular frequency component in this signal appears only in the case of broken rotor bars. Consequently, the proposed algorithm is robust enough to load oscillations and/or machine temperature change, and also indicates the fault severity. The method has been verified at different operating points by simulations as well as experimentally. The fault detection is reliable even in cases where traditional methods give ambiguous verdicts.

Simplified Fault Detection Algorithm for Voltage Source Fed Induction Motor

2018 ◽  
Vol 5 (1) ◽  
pp. 1401-1410
Author(s):  
M. Dilip Kumar ◽  
S.F. Kodad ◽  
B. Sarvesh
Keyword(s):  
Fault Detection ◽  
Induction Motor ◽  
Detection Algorithm ◽  
Voltage Source

Multiple faults detection and isolation approach applied to a kraft recovery boiler

TAPPI Journal ◽  
2014 ◽  
Vol 13 (1) ◽  
pp. 33-41
Author(s):  
YVON THARRAULT ◽  
MOULOUD AMAZOUZ
Keyword(s):  
Fault Detection ◽  
Early Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Principal Component ◽  
Fault Isolation ◽  
Fault Detection And Isolation ◽  
Detection Scheme ◽  
Multiple Faults ◽  
Recovery Boiler

Recovery boilers play a key role in chemical pulp mills. Early detection of defects, such as water leaks, in a recovery boiler is critical to the prevention of explosions, which can occur when water reaches the molten smelt bed of the boiler. Early detection is difficult to achieve because of the complexity and the multitude of recovery boiler operating parameters. Multiple faults can occur in multiple components of the boiler simultaneously, and an efficient and robust fault isolation method is needed. In this paper, we present a new fault detection and isolation scheme for multiple faults. The proposed approach is based on principal component analysis (PCA), a popular fault detection technique. For fault detection, the Mahalanobis distance with an exponentially weighted moving average filter to reduce the false alarm rate is used. This filter is used to adapt the sensitivity of the fault detection scheme versus false alarm rate. For fault isolation, the reconstruction-based contribution is used. To avoid a combinatorial excess of faulty scenarios related to multiple faults, an iterative approach is used. This new method was validated using real data from a pulp and paper mill in Canada. The results demonstrate that the proposed method can effectively detect sensor faults and water leakage.

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