Condition Monitoring Of Pumps Research Articles

Encapsulating commercial accelerometers with epoxy and fluoroelastomer for harsh hydrocarbon fluid environment.

Traditionally, in the oil and gas industry, accelerometers are mounted externally on motors for condition monitoring of vertically suspended, closed suction hydrocarbon pumps due to their inability to withstand harsh downhole environments, preventing the detection of impeller failures. This study addresses the need for encapsulation solutions for accelerometers submerged in hydrocarbon fluid environments. It evaluates the feasibility of epoxy and fluoroelastomer as encapsulation materials for long-term immersion in high-temperature hydrocarbon fluid and determines their impact on the accelerometer's performance. Extensive testing involved submersion in high-temperature hydrocarbon fluid at 150°C for over 10,000h and six months in brine. Material characterization, including mass variation, microscopic imaging, and FTIR spectroscopy, revealed negligible degradation. Encapsulated accelerometers effectively detected vibrations with an acceptable alteration in amplitude. In comparison with commercial alternatives, our encapsulation outperformed them. While oil traces became evident within just 24h in the alternatives, our solution exhibited no signs of leakage. This research pioneers a novel packaging solution employing epoxy and fluoroelastomer for side-exit commercial sensors tailored for high-temperature hydrocarbon fluid applications, addressing a critical gap in the industry. Our work enhances reliability and safety for vertical oil pump condition monitoring in downhole applications, benefiting the oil and gas sector.

Online Flow Estimation for Condition Monitoring of Pumps in Aircraft Hydraulics

Hydraulic systems in conventional civil aviation are currently monitored in a very rudimentary way. Normally, measured values are compared with a fixed threshold. If these measured values are outside the predefined limits, the entire hydraulic system is usually shut down. To overcome this deficit, a study regarding a novel prognostic health management method for aircraft hydraulic pumps, which allows a statement about the pump condition, is presented in this paper. The method is based on measuring differential pressure and temperature at a suitable resistance. In the first part of the study, the overall concept for monitoring the motor pump unit is analyzed. This is followed by a discussion of possible measurement methods and suitable resistors to determine the condition of the pump. In the second part of the study, the implementation for online monitoring of the pump is discussed. After a suitable approximation is found, the quality of the proposed method is evaluated with real hydraulic power generation and consumers.

Review of Machine Learning Based Fault Detection for Centrifugal Pump Induction Motors

Centrifugal pumps are an integral part of many industrial processes and are used extensively in water supply, sewage, heating and cooling systems. While there are several review papers on machine learning-based fault diagnosis on induction motors, its application to centrifugal pumps has received relatively little attention. This work attempts to summarize and review recent research and development in machine learning-based pump condition monitoring and fault diagnosis. The paper starts with a brief explanation of pump operation including common pump faults and the main principles of the motor current signature analysis (MCSA) method. This is followed by a detailed explanation of various machine learning-based methods including the types of detected faults, experimental details and reported accuracies. The performances of different approaches are then presented systematically in a unified table. Finally, the authors discuss practical aspects and challenges related to data collection, storage and real-world implementation.

A novel antibody population optimization based artificial immune system for rotating equipment anomaly detection

Rotating machines are one of the most common equipments in modern industry, effective fault detection and diagnosis methods are vital to equipment health monitoring. In industrial production, the known information of fault types is insufficient generally, especially for constructing complex equipment and components. In previous studies of equipment fault detection, accurate fault classification and diagnosis methods have been presented, while seldom takes the condition of paucity of fault data into account. Therefore, this paper presents a novel antibody population optimization based artificial immune system (APO-AIS) for rotating equipment anomaly detection. The proposed approach can detect abnormal events while monitoring the operating condition. Meanwhile, an antigen-based antibody selecting method, a density-based antibody screening method and an optimized judgment rule based on individual difference are presented for improving the iteration evolution. The presented methods and optimized judgment rule enhance the robustness and reduces training burden for the proposed approach, which leads to accurate anomaly detection in strong background noise and in practical industrial environment. The effectiveness and robustness of the proposed method has been proven experimentally by bearing fault diagnosing and centrifugal pump condition monitoring in this paper.

Research on Bearing Fault Diagnosis of Submersible Pump Motor Based on LMD and SVDD

The motor is a key component of the submersible pump. The health of the motor would greatly affect the safety and efficiency of the submersible pump. The bearing fault is one of the most common faults in motors. Therefore, detection and diagnosis of bearing faults are essential in the condition monitoring of pumps. In this paper, the local average decomposition (LMD) method is used to analyze the bearing vibration signals of submersible pump motor and extract feature vectors. A fault diagnostic model is established by the support vector data description (SVDD) to determine whether the submersible pump motor is faulty. The developed model exhibits practical significance in condition monitoring of submersible pump motor bearings.

Diagnosis of Centrifugal Pump Faults Using Vibration Methods

Pumps are the largest single consumer of power in industry. This means that faulty pumps cause a high rate of energy loss with associated performance degradation, high vibration levels and significant noise radiation. This paper investigates the correlations between pump performance parameters including head, flow rate and energy consumption and surface vibration for the purpose of both pump condition monitoring and performance assessment. Using an in-house pump system, a number of experiments have been carried out on a centrifugal pump system using five impellers: one in good condition and four others with different defects, and at different flow rates for the comparison purposes. The results have shown that each defective impeller performance curve (showing flow, head, efficiency and NPSH (Net Positive Suction Head) is different from the benchmark curve showing the performance of the impeller in good condition. The exterior vibration responses were investigated to extract several key features to represent the healthy pump condition, pump operating condition and pump energy consumption. In combination, these parameter allow an optimal decision for pump overhaul to be made [1].

Use of histogram features for decision tree-based fault diagnosis of monoblock centrifugal pump

Monoblock centrifugal pumps are a crucial part of many industrial plants. Early detection of faults in pumps can increase their reliability, reduce energy consumption, service and maintenance costs, and increase their life-cycle and safety, thus resulting in a significant reduction in life-time costs. It is clear that the fault diagnosis and condition monitoring of pumps are important issues that cannot be ignored. Machine learning-based approach to fault detection and diagnosis is becoming very popular, mainly due to their high accuracy when compared to older statistical methods. There are set of related activities involved in machine learning approach namely, data acquisition from the monoblock centrifugal pump, feature extraction from the acquired data, feature selection, and finally feature classification. This paper presents the use of C4.5 decision tree algorithm for fault diagnosis through histogram feature extracted from vibration signals of good and faulty conditions of monoblock centrifugal pump. The performance of the proposed system is compared to that of a Naive Bayes-based system to validate the superiority of the proposed system.

A method for calculation of optimum data size and bin size of histogram features in fault diagnosis of mono-block centrifugal pump

Mono-block centrifugal pump plays a key role in various applications. Any deviation in the functions of centrifugal pump would lead to a monetary loss. Thus, it becomes very essential to avoid the economic loss due to malfunctioning of centrifugal pump. It is clear that the fault diagnosis and condition monitoring of pumps are important issues that cannot be ignored. Over the past 25years, much research has been focused on vibration based techniques. Machine learning approach is one of the most widely used techniques using vibration signals in fault diagnosis. There are set of connected activities involved in machine learning approach namely, data acquisition, feature extraction, feature selection, and feature classification. Training and testing the classifier are the two important activities in the process of feature classification. When the histogram features are used as the representative of the vibration signals, a proper guideline has not been proposed so far to choose number of bins and number of samples required to train the classifier. This paper illustrates a systematic method to choose the number of bins and the minimum number of samples required to train the classifier with statistical stability so as to get best classification accuracy. In this study, power analysis method was employed to find the minimum number of samples required and a decision tree algorithm namely J48 was used to validate the results of power analysis and to find the optimum number of bins.

Wavelet Packets Analysis Based Method for Hydraulic Pump Condition Monitoring

The hydraulic pump is a key component in hydraulic system.Monitoring its operation state and carrying out fault diagnosis are of important significance to the reliability of the whole hydraulic system.Based on the wavelet packet decomposition(WPD) and wavelet coefficients residual analysis method, the fault diagnosis approach by employing the outlet pressure of the pump is proposed for the condition monitoring of hydraulic pump.By analyzing the characteristic of the discharge pressure of the hydraulic pump, the wavelet packet analysis is applied to the pressure signal to extract the feature spectrum of the hydraulic pump.The relationships of the feature signals in different frequency bands corresponding to the different fault factors are established.The wavelet packet energy residuals are then used to diagnose the pump health status.The sensitivities of the fault diagnosis with different wavelet function are also discussed.For eliminating the border distortions in WPD, a twin sliding window scheme is created to improve the performance of the diagnosing system.The validating test results show that this WPD and residual analysis based method can effectively improve the accuracy of fault diagnosis in comparison to the conventional fast Fourier transform(FFT) method.

Vibration-based fault diagnosis of pump using fuzzy technique

This paper focuses on a problem of vibration-based condition monitoring and fault diagnosis of pumps used in oil field to recover petroleum. The vibration-based machine condition monitoring and fault diagnosis incorporate a number of machinery fault detection and diagnostic techniques. Many machinery fault diagnostic techniques utilize automatic signal classification in order to increase accuracy and reduce errors caused by subjective human judgment. In this paper, fuzzy logic principle is used as a fault diagnostic technique to describe the uncertain and ambiguous relationship between different fault symptoms and the events, analyze the fuzzy information existing in the different phases of fault diagnosis and condition monitoring of the pumps, and classify frequency spectra representing various pump faults. The diagnostic features are extracted from frequency spectra of the vibration signals of the pump. The frequency spectra representing a number of different fault conditions are then processed using fuzzy membership function, which is established by means of dynamic signal processing based on the condition variables. Correct classification and condition recognition of different pump fault spectra are realized when fuzzy comprehensive discrimination according to the defuzzy diagnosis rules is applied. The work conducted, proposing the new method of the pump fault identification based on fuzzy logic technique, shows the great potentiality and the strong ability to classify and identify machinery faults.