Fault Diagnosis Of Pump Research Articles

Scale-Fractal Detrended Fluctuation Analysis for Fault Diagnosis of a Centrifugal Pump and a Reciprocating Compressor.

Reciprocating compressors and centrifugal pumps are rotating machines used in industry, where fault detection is crucial for avoiding unnecessary and costly downtime. A novel method for fault classification in reciprocating compressors and multi-stage centrifugal pumps is proposed. In the feature extraction stage, raw vibration signals are processed using multi-fractal detrended fluctuation analysis (MFDFA) to extract features indicative of different types of faults. Such MFDFA features enable the training of machine learning models for classifying faults. Several classical machine learning models and a deep learning model corresponding to the convolutional neural network (CNN) are compared with respect to their classification accuracy. The cross-validation results show that all models are highly accurate for classifying the 13 types of faults in the centrifugal pump, the 17 valve faults, and the 13 multi-faults in the reciprocating compressor. The random forest subspace discriminant (RFSD) and the CNN model achieved the best results using MFDFA features calculated with quadratic approximations. The proposed method is a promising approach for fault classification in reciprocating compressors and multi-stage centrifugal pumps.

High imbalance fault diagnosis of aviation hydraulic pump based on data augmentation via local wavelet similarity fusion

Data augmentation methods, such as SMOTE and deep generative networks, have the potential to solve class-imbalance issue in aviation hydraulic pump fault diagnosis. Nevertheless, the scarcity of faulty samples often leads augmented samples generated using such methods suffer from blurred category boundaries, limited diversity, and mode collapse. To avoid these issues, a new data augmentation method namely Local Wavelet Similarity Fusion (LWSF) is proposed, which augments faulty samples by preserving and distorting the wavelet packet coefficients (WPCs) of the original faulty samples. An important property is that LWSF can generate high-quality synthesis samples without complex model training. First, faulty samples are decomposed into a series of frequency bands using wavelet packet decomposition. Second, the amplitudes of WPCs at a few randomly selected frequency bands are distorted, which is accomplished through a similarity-weighted fusion of the target WPCs with the closest related WPCs matched from reference samples, with the goal of keeping distortion within an appropriate range. Third, these WPCs are used to reconstruct the samples, which serve as the augmented samples. Besides, to enhance diversity, normal samples are also employed to assist in the distortion. Finally, the effectiveness of LWSF is validated by a series of experimental comparisons. In an imbalanced fault diagnosis task, the accuracy and F1-score obtained by LWSF are 97.16 % and 97.35 %, respectively, which are 3.59 % and 7.30 % higher than the best one among the five compared methods.

A light deep adaptive framework toward fault diagnosis of a hydraulic piston pump

The health condition of hydraulic axial piston pumps is crucial for the safety and reliability of hydraulic transmission systems. Diagnosis results of traditional methods indicate the high reliance on the experience, and current deep model-based methods are confronted with the difficulty of parameter tuning. A light adaptive deep framework is therefore constructed to reduce reliance of diagnosis on expert experience and still achieve the automatic screening of model hyperparameters. First, multi-sensor and multiple-channel signals of a piston pump are acquired for comprehensive raw data input. The raw one-dimensional signals are then converted into two-dimensional images using continuous wavelet transform. Then, a light deep model is built based on the convolutional operation and batch normalization techniques. The final model is obtained via global optimization of Bayesian algorithm. Next, the improved deep model is adopted for failure recognition of the essential components in the piston pump based on the transformed images. The average diagnosis accuracy achieves 97.46%, 98.71%, and 99.94% based on vibration signal, acoustic signal, and pressure signal respectively. The results reveal that the typical fault of the piston pump can be recognized intelligently and accurately with the proposed diagnosis method.

Dynamic analysis of nonlinear features for high-precision fault diagnosis of large pumps and compressors in oil and gas fields

Dynamic analysis of nonlinear features for high-precision fault diagnosis of large pumps and compressors in oil and gas fields

Adversarial Domain Adaptation Approach for Axial Piston Pump Fault Diagnosis Under Small Sample Condition Based on Measured and Simulated Signals

Adversarial Domain Adaptation Approach for Axial Piston Pump Fault Diagnosis Under Small Sample Condition Based on Measured and Simulated Signals

Pump Fault Detection Based on MFCC-MLCNN

Detection of industrial water pumping systems is both a practical and important area of research in industrial production. The accuracy of fault detection is crucial because failure of fault detection can lead to pump damage and reduced productivity. In order to timely and accurately identify the working status of water supply pumps, a pump fault detection method based on Mel Frequency Cepstrum Coefficient with Migration Learning Convolutional Neural Networks (MFCC-MLCNN) is proposed. The sound signals of water pumps under different operating conditions are preprocessed to calculate their MFCC features as static features, and further processed to obtain the first-order difference MFCC features as well as the second-order difference MFCC features as dynamic features. In this study, the audio dataset of water pumps recorded with ambient noise in a real industrial environment is used. Usually, fault detection datasets are unbalanced because the amount of fault data is limited. A practical way to deal with this problem is to use deep migration learning, where high accuracy can be achieved with limited labeled data. In this study, a migration learning convolutional neural network is introduced to establish a pump fault diagnosis model. The experimental results show that the method proposed in this study can effectively recognize the working state of water supply pumps with a small amount of sample data and model parameters.

A Novel Multi–Scale One–Dimensional Convolutional Neural Network for Intelligent Fault Diagnosis of Centrifugal Pumps

Centrifugal pumps are susceptible to various faults, particularly under challenging conditions such as high pressure. Swift and accurate fault diagnosis is crucial for enhancing the reliability and safety of mechanical equipment. However, monitoring data under fault conditions in centrifugal pumps are limited. This study employed an experimental approach to gather original monitoring data (vibration signal data) across various fault types. We introduce a multi–scale sensing Convolutional Neural Network (MS–1D–CNN) model for diagnosing faults in centrifugal pumps. The network structure is further optimized by examining the impact of various hyperparameters on its performance. Subsequently, the model’s efficacy in diagnosing centrifugal pump faults has been comprehensively validated using experimental data. The results demonstrate that, under both single and multiple operating conditions, the model not only reduces reliance on manual intervention but also improves the accuracy of fault diagnosis.

Research on fault diagnosis of electric submersible pump based on improved convolutional neural network with Bayesian optimization

A conventional intelligent fault diagnosis approach for electric submersible pumps (ESPs) is heavily dependent on manual expertise for feature extraction. Meanwhile, a conventional convolutional neural network (CNN) exhibits an excess of parameters and requires a substantial volume of training samples. In this paper, a fault diagnosis algorithm of ESPs based on a Bayesian optimization-one-dimensional convolutional neural network-support vector machine (BO-1DCNN-SVM) is proposed by combining a 1DCNN with a SVM and using the algorithm BO to tune the improved 1DCNN. First, the method uses the self-extracting feature capability of the 1DCNN to solve the problem that traditional diagnosis methods over-rely on manual experience extraction. Meanwhile, the last layer of the convolutional neural network Softmax layer is replaced by the SVM to effectively process a few sample data. The accuracy and generalization ability of the fault classification of the electric submersible pump are improved. Then, the Bayesian optimization algorithm is used to find the optimal combination of hyperparameters for the improved 1DCNN-SVM model to further improve the prediction performance of the fault diagnosis model. Finally, the experimental results achieved a classification accuracy of 96.64%, which is 5% higher than existing CNN approaches with data samples of similar scale in this paper. The proposed method also proved to be highly accurate and robust in fault diagnosis.

An Intelligent Framework for Fault Diagnosis of Centrifugal Pump Leveraging Wavelet Coherence Analysis and Deep Learning.

This paper proposes an intelligent framework for the fault diagnosis of centrifugal pumps (CPs) based on wavelet coherence analysis (WCA) and deep learning (DL). The fault-related impulses in the CP vibration signal are often attenuated due to the background interference noises, thus affecting the sensitivity of the traditional statistical features towards faults. Furthermore, extracting health-sensitive information from the vibration signal needs human expertise and background knowledge. To extract CP health-sensitive features autonomously from the vibration signals, the proposed approach initially selects a healthy baseline signal. The wavelet coherence analysis is then computed between the healthy baseline signal and the signal obtained from a CP under different operating conditions, yielding coherograms. WCA is a signal processing technique that is used to measure the degree of linear correlation between two signals as a function of frequency. The coherograms carry information about the CP vulnerability towards the faults as the color intensity in the coherograms changes according to the change in CP health conditions. To utilize the changes in the coherograms due to the health conditions of the CP, they are provided to a Convolution Neural Network (CNN) and a Convolution Autoencoder (CAE) for the extraction of discriminant CP health-sensitive information autonomously. The CAE extracts global variations from the coherograms, and the CNN extracts local variations related to CP health. This information is combined into a single latent space vector. To identify the health conditions of the CP, the latent space vector is classified using an Artificial Neural Network (ANN). The proposed method identifies faults in the CP with higher accuracy as compared to already existing methods when it is tested on the vibration signals acquired from real-world industrial CPs.

Can pretrained networks be used in fault diagnosis of monoblock centrifugal pump?

Monoblock centrifugal pumps are used daily and play an essential role in agriculture, wastewater treatment, food, paper, gas and oil industries. Extended and endless use of monoblock centrifugal pump can affect the desired pump characteristics, compromising the efficiency of the pump. The condition of a pump is judged based on the high vibration, unusual noise, leakage, etc. Maintaining pump health is essential to increase the productivity in industry. Countless experiments were carried out by researchers to maintain the efficiency of the pump. The main shortcomings of traditional fault diagnosis are the need for a high degree of human intellect and professional knowledge. Such drawbacks have made the researchers provide autonomous and intelligent diagnostic tools. The objective of this article is to investigate the possibility of use of deep learning technique to identify the type of fault present in the monoblock centrifugal pump. First, vibration signals from the monoblock centrifugal pump were acquired and plotted as images that serve as an input for deep learning (DL) algorithms. The deep learning system can analyse the status of the monoblock centrifugal pump and learn from the vibration signal plots. In this paper, pre-trained networks such as ResNet-50, AlexNet, GoogleNet and VGG-16 were used to diagnose the fault in the monoblock centrifugal pump. The outcome of the hyperparameters namely learning rate (LR), batch size (BS), split ratio (SR) and solver were considered and the best performing network was recommended for the monoblock centrifugal pump fault diagnostics. Findings: A higher classification accuracy of 100% with lower training time of 177 s was obtained while using ResNet-50.

Multisensor Information Fusion for Fault Diagnosis of Axial Piston Pump Based on Improved WPD and SSA-KSTTM

Multisensor Information Fusion for Fault Diagnosis of Axial Piston Pump Based on Improved WPD and SSA-KSTTM

Research on Common Fault Diagnosis of Mud Pump Based on Wavelet Packet Analysis

The utilization of vibration signals to diagnose faults in centrifugal pumps is a crucial research direction in this field. Traditional frequency domain processing methods, such as Fast Fourier Transform (FFT), may not be effective in analyzing complex signals where multiple types of faults occur simultaneously at the same frequency. This paper proposes the use of wavelet packet analysis as an alternative. By analyzing fault conditions caused by blade damage and clearance wear in a mud pump and comparing the results to those obtained through the FFT analysis method, the superior accuracy of wavelet packet analysis is demonstrated.

A Transfer-Based Convolutional Neural Network Model with Multi-Signal Fusion and Hyperparameter Optimization for Pump Fault Diagnosis.

Pumps are one of the core components of drilling equipment, and their fault diagnosis is of great significance. The data-driven approach has made remarkable achievements in the field of pump fault diagnosis; however, most of them are easily affected by complex background conditions and usually suffer from data scarcity problems in real-industrial scenarios, which limit their application in practical engineering. To overcome the above shortcoming, a novel framework for a model named Hyperparameter Optimization Multiple-Signal Fusion Transfer Convolution Neural Network is proposed in this paper. A convolutional neural network model based on transfer learning is built to promote well-learned knowledge transfer over different background conditions, improve robustness, and generalize the model to cross-domain diagnosis tasks. The multi-signal fusion strategy is involved in capturing system state information for establishing the mapping relationship between the raw signal and fault pattern by integrating the multi-physical signal with the weight allocation protocol. The hyperparameter optimization method is explored in conjunction with the transfer-based model by integrating Grid Search with the Gradient Descent algorithm for further improvement of diagnosis performance. Results show that the proposed model can effectively realize the fault diagnosis of pumps under different background conditions, achieving 95% accuracy.

A Hydraulic Axial Piston Pump Fault Diagnosis Based on Instantaneous Angular Speed under Non-Stationary Conditions

Due to the intense noise interference in hydraulic systems, it is extremely difficult to detect component faults through vibration signals. Diagnostic performance is also constrained by highly time-varying and non-stationary operating conditions. This study proposes to use instantaneous angular speed (IAS) signals that are both operational and state parameters as sources of information. Firstly, the instantaneous angular speed fluctuation (IASF) of a piston pump is analyzed theoretically, and it is concluded that its fluctuating components contain the health status information of the components. The IASF can then be obtained by subtracting the speed trend term from IAS signals obtained via a magneto-electric speed sensor. A synchro-extraction of the normal S transform (SNST) is proposed to process it via line-pass filtering. Finally, the filtered and reconstructed IASF signal is utilized to draw a two-dimensional polar coordinate map online. A non-stationary-condition test is carried out on the test platform to monitor the morphological characteristics of the valve plate under normal, slight, and severe wear conditions. The polar plot shows significant increases in speed fluctuations and oscillation times within a range from 180° to 270°. The relevant research results reflect that the IAS signal can provide a new method for monitoring the operating status of and conducting fault diagnoses for hydraulic equipment.

Fault diagnosis of electric submersible pumps using vibration signals

Fault diagnosis of electric submersible pumps using vibration signals

SO-IMCKD processed signal improving MSCNN model’s fault diagnosis accuracy for drilling pump fluid end

Drilling pump is the ‘heart’ of drilling construction. The key to accurate fault diagnosis is to extract useful fault features from noisy raw signals. In order to improve the accuracy of fault diagnosis of drilling pump fluid end, this paper proposes a fault diagnosis method based on multi-scale convolutional neural network (MSCNN) combined with the snake optimization optimized maximum correlation kurtosis deconvolution (SO-IMCKD). First, the SO algorithm is employed to optimize the filter length and the shift number of IMCKD to process the raw signal, enhancing the fault features from the raw signal. Second, the continuous wavelet transform is used to convert the enhanced signals into time-frequency images which are input into an established MSCNN to extract the fault feature more effectively. Finally, by changing the training batchsize of the MSCNN model, the identification effect of the model to the normal state, minor damage, and serious damage of the fluid end is analyzed. The identification of nine states of the fluid end is successfully carried out, with an average diagnostic accuracy of 99.93%. Moreover, the adaptability of the proposed method is verified with the Mechanical Failure Prevention Technology Association dataset. The method has high accuracy and good adaptability, which has desired prospect for drilling pump fault diagnosis and bearing fault diagnosis.

Fault Diagnosis of Electric Submersible Pumps Using a Three‐Stage Multiscale Feature Transformation Combined with CNN–SVM

A convolutional neural network support vector machine (CNN–SVM) method based on multichannel feature fusion is used for progressive fault diagnosis of offshore oil and gas wells. The excellent classification performance of CNN is attributed to its ability to extract feature representations from large amounts of easily distinguishable data. However, the capability of CNN is severely constrained by the noisy and small sample amount of the electric submersible pump fault data to be studied in this article. First, 12 representative statistical features are extracted from the raw data to reduce the noise. Then, the feature mapping model is designed based on CNN migration learning. Finally, SVM is used instead of softmax function to adopt representative features directly from the mapping model for fault classification. Comparative experimental results show that the accuracy of fault diagnosis using feature‐extracted samples is better than using the raw samples directly. The proposed CNN–SVM approach has the best classification results compared to SVM, BPNN, CNN, BPNN–SVM, CNN–Attention, CNN–LSTM, and CNN–LSTM–Attention, which implies that manual feature extraction is still an indispensable tool in the fault diagnosis process.

A few-shot sample augmentation algorithm based on SCAM and DEPS for pump fault diagnosis

In recent years, pumps have become critical components in agriculture, industry, and the military, necessitating extensive development and implementation of the fault diagnosis method. In the majority of existing fault classification models, the connection between performance improvement and the amount of training data remains high, yet real-world samples are difficult to obtain. Combining domain migration theory and sample expansion method, this paper introduces a few-shot learning fault diagnosis method. Employing the T-SNE visualization algorithm, we examine the validity of the self-calibration attention mechanism (SCAM) and distribution edge prediction strategy (DEPS). The accomplishment demonstrated that the proposed algorithm could effectively map the expanded sample space within a separate interval, thereby avoiding the problem of feature aliasing caused by the overlap of sample features among similar categories and significantly enhancing the quality and quantity of training samples. The experimental analysis indicates that the proposed methodology can effectively increase the accuracy of few-shot tasks, especially in the 9way-15shot task, where it maintains a performance of 72 %, which leading the mean accuracy calculated from the others of about 30%. It is believed that much of the work has superior applicability to other few-shot diagnosis cases.

Construction machinery hydraulic transmission system fault analysis and elimination methods

Abstract The hydraulic system plays the role of power transmission in the working process of construction machinery, but their complex structure leads to their frequent failure, which in turn leads to serious consequences. Therefore, this paper first takes the fault diagnosis and troubleshooting of the hydraulic system of construction machinery as the research theme and introduces the working principle of the hydraulic system of construction machinery and common fault mechanism and common methods of fault monitoring and diagnosis. Secondly, it proposes to analyze the key components of the hydraulic system based on the power bonding diagram theory and establish its bonding diagram model to analyze the working principle and power flow characteristics of key components. Finally, the pressure and velocity characteristic parameters are identified with the help of wavelet technology, and the energy distribution and energy entropy value of the hydraulic cylinder velocity signal are obtained by wavelet packet transform. The simulation results show that the wavelet-based analysis model proposed in this paper has good fault tolerance and robustness for fault diagnosis of the hydraulic system of construction machinery, and the correct rate of hydraulic pump fault diagnosis is up to 97%, with a correct average rate increased by 16%; the correct rate of hydraulic cylinder fault diagnosis is up to 95%, with a correct average rate increased by 13%. The wavelet analysis model can be widely applied to the fault diagnosis of hydraulic systems of various construction machinery, and it effectively improves the performance of hydraulic fault analysis systems and provides a key technology for fault identification and troubleshooting of construction machinery.

A Novel Ensemble Fault Diagnosis Model for Main Circulation Pumps of Converter Valves in VSC-HVDC Transmission Systems.

The intelligent fault diagnosis of main circulation pumps is crucial for ensuring their safe and stable operation. However, limited research has been conducted on this topic, and applying existing fault diagnosis methods designed for other equipment may not yield optimal results when directly used for main circulation pump fault diagnosis. To address this issue, we propose a novel ensemble fault diagnosis model for the main circulation pumps of converter valves in voltage source converter-based high voltage direct current transmission (VSG-HVDC) systems. The proposed model employs a set of base learners already able to achieve satisfying fault diagnosis performance and a weighting model based on deep reinforcement learning that synthesizes the outputs of these base learners and assigns different weights to obtain the final fault diagnosis results. The experimental results demonstrate that the proposed model outperforms alternative approaches, achieving an accuracy of 95.00% and an F1 score of 90.48%. Compared to the widely used long and short-term memory artificial neural network (LSTM), the proposed model exhibits improvements of 4.06% in accuracy and 7.85% in F1 score. Furthermore, it surpasses the latest existing ensemble model based on the improved sparrow algorithm, with enhancements of 1.56% in accuracy and 2.91% in F1 score. This work presents a data-driven tool with high accuracy for the fault diagnosis of main circulation pumps, which plays a critical role in maintaining the operational stability of VSG-HVDC systems and satisfying the unmanned requirements of offshore flexible platform cooling systems.