Fault Diagnosis Of Pump Research Articles

Fault Diagnosis of Axial Piston Pump Based on Polynomial Chirplet Transform and Variational Mode Decomposition Under Variable Speed Conditions

Fault Diagnosis of Axial Piston Pump Based on Polynomial Chirplet Transform and Variational Mode Decomposition Under Variable Speed Conditions

Using MLP‐GABP and SVM with wavelet packet transform‐based feature extraction for fault diagnosis of a centrifugal pump

AbstractThis paper explores artificial intelligent training schemes based on multilayer perceptron, considering back propagation and genetic algorithm (GA). The hybrid scheme is compared with the traditional support vector machine approach in the literature to analyze both fault and normal scenarios of a centrifugal pump. A comparative analysis of the performance of the variables was carried out using both schemes. The study used features extracted for three decomposition levels based on wavelet packet transform. In order to investigate the effectiveness of the extracted features, two mother wavelets were investigated. The salient part of this work is the optimization of the hidden layers numbers using GA. Furthermore, this optimization process was extended to the multilayer perceptron neurons. The evaluation of the model system performance used for the study shows better response of the extracted features, and hidden layers variables including the selected neurons. Moreover, the applied training algorithm used in the work was able to enhance the classifications obtained considering the hybrid artificial intelligent scheme been proposed. This work has achieved a number of contributions like GA‐based selection of hidden layers and neuron, applied in neural network of centrifugal pump condition classification. Furthermore, a hybrid training method combining GA and back propagation (BP) algorithms has been applied for condition classification of a centrifugal pump. The obtained results have shown the good ability of the proposed methods and algorithms.

Fault Diagnosis of Axial Piston Pump Based on Extreme-Point Symmetric Mode Decomposition and Random Forests

Aiming at fault diagnosis of axial piston pumps, a new fusion method based on the extreme-point symmetric mode decomposition method (ESMD) and random forests (RFs) was proposed. Firstly, the vibration signal of the axial piston pump was decomposed by ESMD to get several intrinsic mode functions (IMFs) and an adaptive global mean curve (AGMC) on the local side. Secondly, the total energy was selected as the data of feature extraction by analyzing the whole oscillation intensity of the signal. Thirdly, the data were preprocessed and the labels were set, and then, they were adopted as the training and testing set of machine learning samples. Lastly, the RFs model was created based on machine learning service (MLS) to diagnose the faults of the axial piston pump on the cloud. Using the test and verifying the data set for comparative testing, the fault diagnosis precision rates of the model are above 90.6%, the recall rates are more than 90.9%, the F1 score is higher than 90.7%, and the accuracy rate of this model reached 97.14%. A benchmark data simulation of mechanical transmission systems and an experimental data investigation of an axial piston pump are performed to manifest the superiority of the present method by comparing with classification and regression trees (CART) and support vector machine (SVM).

Hydraulic pump fault diagnosis based on chaotic characteristics of speed signals under non-stationary conditions

Pumps are vital components in hydraulic equipment, and their malfunction is directly related to the normal operation of the whole system. The hydraulic system is accompanied by the conversion of multi-domain energy during operation. In particular, it exhibits non-stationarity and nonlinearity under variable operating conditions, which brings difficulties to fault diagnosis. In this paper, the instantaneous angular speed (IAS) signal obtained by the equal-angle measurement is studied to diagnose pump faults under non-stationary conditions. For this purpose, a synchroextracting normal S transform method is proposed to extract the time-frequency feature components properly. Then, the extracted signal is reconstructed into a high-dimensional phase space based on the improved C-C method. On this basis, the quantitative analysis indicators of pump state are obtained by the G-P algorithm and small-data method, including correlation dimension, Kolmogorov entropy, and largest Lyapunov exponent. Pump faults can be classified in a chaotic space by using these sensitive features. The results show that the proposed method is capable of diagnosing different states of pumps and robust to the variation of load values.

Recent Advancement of Deep Learning Applications to Machine Condition Monitoring Part 1: A Critical Review

With the huge success of applying deep learning (DL) methodologies to image recognition and natural language processing in recent years, researchers are now keen to use them in the machine condition monitoring (MCM) context. There are numerous papers in applying various DL techniques, such as auto-encoder, restricted Boltzmann machine, convolutional neural network and recurrent neural network, etc., to MCM problems ranging from component-level condition monitoring (machine tool wear prediction, bearing fault diagnosis and classification and hydraulic pump fault diagnosis) to system-level health management (aircraft and spacecraft diagnosis). In this paper, we give a brief overview in the area of DL for MCM with a focus on reviewing the most recent papers published since 2019. In Part 1, we present some critical views regarding whether any breakthrough has been achieved from an MCM domain expert perspective, with the main conclusion that DL has great potential for MCM applications, and a major breakthrough could come soon since the shortfalls lie more in data than in the DL methodologies. Our overall impression is that (a) DL models are not really showing their great potentials with only a small training data; (b) faulty-condition data is hard to come by for training DL, but normal condition data is abundant, so anomaly detection makes more sense; (c) applying DL only to the Case Western Reserve University (CWRU) bearing fault dataset is not sufficient for real world industrial applications as it was from a very simple test rig, and applying DL to data from complex systems like helicopter gearbox data may deliver much more convincing results. In Part 2, we enhance the main conclusion of the critical review with supplement views and a case study on analysing Bell-206B helicopter main gearbox planet bearing failure data using some traditional MCM techniques in contrast to applying the long short-term memory (LSTM) DL method. We can conclude from the case study that the DL-based methods are not necessarily always superior to the traditional MCM techniques for dataset from moderately complex machinery.

A Hydraulic Pump Fault Diagnosis Method Based on the Modified Ensemble Empirical Mode Decomposition and Wavelet Kernel Extreme Learning Machine Methods.

To address the problem that the faults in axial piston pumps are complex and difficult to effectively diagnose, an integrated hydraulic pump fault diagnosis method based on the modified ensemble empirical mode decomposition (MEEMD), autoregressive (AR) spectrum energy, and wavelet kernel extreme learning machine (WKELM) methods is presented in this paper. First, the non-linear and non-stationary hydraulic pump vibration signals are decomposed into several intrinsic mode function (IMF) components by the MEEMD method. Next, AR spectrum analysis is performed for each IMF component, in order to extract the AR spectrum energy of each component as fault characteristics. Then, a hydraulic pump fault diagnosis model based on WKELM is built, in order to extract the features and diagnose faults of hydraulic pump vibration signals, for which the recognition accuracy reached 100%. Finally, the fault diagnosis effect of the hydraulic pump fault diagnosis method proposed in this paper is compared with BP neural network, support vector machine (SVM), and extreme learning machine (ELM) methods. The hydraulic pump fault diagnosis method presented in this paper can diagnose faults of single slipper wear, single slipper loosing and center spring wear type with 100% accuracy, and the fault diagnosis time is only 0.002 s. The results demonstrate that the integrated hydraulic pump fault diagnosis method based on MEEMD, AR spectrum, and WKELM methods has higher fault recognition accuracy and faster speed than existing alternatives.

Hydraulic Pump Fault Diagnosis Method Based on EWT Decomposition Denoising and Deep Learning on Cloud Platform

An axial piston pump fault diagnosis algorithm based on empirical wavelet transform (EWT) and one-dimensional convolutional neural network (1D-CNN) is presented. The fault vibration signals and pressure signals of axial piston pump are taken as the analysis objects. Firstly, the original signals are decomposed by EWT, and each signal component is screened and reconstructed according to the energy characteristics. Then, the time-domain features and the frequency-domain features of the denoised signal are extracted, and features of time domain and frequency domain are fused. Finally, the 1D-CNN model was deployed to the WISE-Platform as a Service (WISE-PaaS) cloud platform to realize the real-time fault diagnosis of axial piston pump based on the cloud platform. Compared with ensemble empirical mode decomposition (EEMD) and complementary ensemble empirical mode decomposition (CEEMD), the results show that the axial piston pump fault diagnosis algorithm based on EWT and 1D-CNN has higher fault identification accuracy.

Axial Piston Pump Fault Diagnosis Method Based on Symmetrical Polar Coordinate Image and Fuzzy C-Means Clustering Algorithm

In this paper, a fault diagnosis method based on symmetric polar coordinate image and Fuzzy C-Means clustering algorithm is proposed to solve the problem that the fault signal of axial piston pump is not intuitive under the time-domain waveform diagram. In this paper, the sampled vibration signals of axial piston pump were denoised firstly by the combination of ensemble empirical mode decomposition and Pearson correlation coefficient. Secondly, the data, after noise reduction, was converted into images, called snowflake images, according to symmetric polar coordinate method. Different fault types of axial piston pump can be identified by observing the snowflake images. After that, in order to evaluate the research results objectively, the obtained images were converted into Gray-Level Cooccurrence Matrixes. Their multiple eigenvalues were extracted, and the eigenvectors consisting of multiple eigenvalues were classified by Fuzzy C-Means clustering algorithm. Finally, according to the accuracy of classification results, the feasibility of applying the symmetric polar coordinate method to axial piston pump fault diagnosis has been validated.

Intelligent Fault Diagnosis of Hydraulic Piston Pump Based on Wavelet Analysis and Improved AlexNet.

Hydraulic piston pump is the heart of hydraulic transmission system. On account of the limitations of traditional fault diagnosis in the dependence on expert experience knowledge and the extraction of fault features, it is of great meaning to explore the intelligent diagnosis methods of hydraulic piston pump. Motivated by deep learning theory, a novel intelligent fault diagnosis method for hydraulic piston pump is proposed via combining wavelet analysis with improved convolutional neural network (CNN). Compared with the classic AlexNet, the proposed method decreases the number of parameters and computational complexity by means of modifying the structure of network. The constructed model fully integrates the ability of wavelet analysis in feature extraction and the ability of CNN in deep learning. The proposed method is employed to extract the fault features from the measured vibration signals of the piston pump and realize the fault classification. The fault data are mainly from five different health states: central spring failure, sliding slipper wear, swash plate wear, loose slipper, and normal state, respectively. The results show that the proposed method can extract the characteristics of the vibration signals of the piston pump in multiple states, and effectively realize intelligent fault recognition. To further demonstrate the recognition property of the proposed model, different CNN models are used for comparisons, involving standard LeNet-5, improved 2D LeNet-5, and standard AlexNet. Compared with the models for contrastive analysis, the proposed method has the highest recognition accuracy, and the proposed model is more robust.

Analysis of Vibration Characteristics of Centrifugal Pump Mechanical Seal under Wear and Damage Degree

Mechanical seal is a kind of shaft sealing equipment. Face wear is one of the main causes of mechanical seal failure. Mechanical seal condition is also related to the reduction of energy consumption and carbon emission. Therefore, we need to detect the centrifugal pump seal condition. At present, vibration signal is a common method for fault monitoring and diagnosis of centrifugal pump. In this paper, the vibration signal under the condition of damaged centrifugal pump seal is measured by studying the characteristics of vibration signal after the end face damage of centrifugal pump. Statistical indicators such as RMS and kurtosis were taken to analyze the average energy and shock wave energy of vibration signal. The time‐frequency characteristics of vibration signal are analyzed by frequency spectrum. The results show that there are a large extent variation of vibration amplitude in the direction of base and axis and a weak variation of vibration amplitude in the direction of radial and vertical. With the increasing of flow rate, the RMS of vibration signal falls at first, then keeps steady, and mounts at last when the flow rate is over the design flow rate. It can be shown from the time‐frequency spectrum that there is a shock wave and pause signals caused by the shock wave, which are reflected by the higher frequency band components of the vibration signal that can provide a reference to the diagnosis of the occurrence of damaged mechanical seal. From the analysis, the energy of vibration signal is related to the running condition, we can find that the occurrence of mechanical seal wear makes the centrifugal pump to produce high‐frequency vibration signal, and the axial vibration is the strongest and the instability in the fluid makes the vibration signal produce high amplitude characteristics. Analyzing the vibration signal characteristics of centrifugal pumps with damaged mechanical seal is of great significance to find the mechanical seal failure of the centrifugal pumps and adjust the operating parameters.

A Novel Framework for Centrifugal Pump Fault Diagnosis by Selecting Fault Characteristic Coefficients of Walsh Transform and Cosine Linear Discriminant Analysis

In this paper, we propose a three-stage lightweight framework for centrifugal pump fault diagnosis. First, the centrifugal pump vibration signatures are fast transformed using a Walsh transform, and Walsh spectra are obtained. To overcome the hefty noise produced by macro-structural vibration, the proposed method selects the fault characteristic coefficients of the Walsh spectrum. In the second stage, statistical features in the time and Walsh spectrum domain are extracted from the selected fault characteristic coefficients of the Walsh transform. These extracted raw statistical features result in a hybrid high-dimensional space. Not all these extracted features help illustrate the condition of the centrifugal pump. To overcome this issue, novel cosine linear discriminant analysis is introduced in the third stage. Cosine linear discriminant analysis is a dimensionality reduction technique which selects similar interclass features and adds them to the illustrative feature pool, which contains key discriminant features that represent the condition of the centrifugal pump. To achieve maximum between-class separation, linear discriminant analysis is then applied to the illustrative feature pool. This combination of illustrative feature pool creation and linear discriminant analysis forms the proposed application of cosine linear discriminant analysis. The reduced discriminant feature set obtained from cosine linear discriminant analysis is then given as an input to the K-nearest neighbor classifier for classification. The classification results obtained from the proposed method outperform the previously presented state-of-the-art methods in terms of fault classification accuracy.

Composite Multi-Scale Basic Scale Entropy Based on CEEMDAN and Its Application in Hydraulic Pump Fault Diagnosis

The hydraulic pump plays a very important role in the safe and stable operation of the hydraulic system. Once it fails, it will cause immeasurable losses to the entire hydraulic system. But in practice, because hydraulic pump often works under strong noise background, the fault characteristics of its vibration signals are often very weak and difficult to extract. To solve this problem, this paper proposes an effective time series dynamic feature extraction method, which is based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and composite multi-scale basic scale entropy (CMBSE). On this basis, a new hydraulic pump fault diagnosis method is proposed by combining t-distributed stochastic neighbor embedding (t-SNE) and whale optimization algorithm kernel extreme learning machine (WOA-KELM). First, CEEMDAN is used to decompose the fault signals of the hydraulic pump, and CMBSE is used to quantify the decomposed IMF components to obtain the fault characteristics of the different states of the hydraulic pump, and then use t-SNE to visualize the dimensionality reduction, and finally input into the WOA-KELM-based fault classifier for state identification. The experimental results show that this method can effectively extract the weak signal features under strong noise background, and has broad application prospects for hydraulic pump fault diagnosis.

Application of Fault Diagnosis of Seawater Hydraulic Pump Based on Transfer Learning

The working environment of seawater axial piston hydraulic pump is harsh, and it is difficult to diagnose due to insufficient fault database. In contrast, pumps of the same type but using hydraulic oil have an adequate fault database and are easy to diagnose. In view of the above situation, a fault diagnosis method of seawater hydraulic piston pump based on transfer learning is proposed. The method decomposes the original sampled fault signal by complementary ensemble empirical mode decomposition (CEEMD) to obtain the intrinsic mode function (IMF) that can characterize the original signal. The singular value decomposition (SVD) is performed on the IMF. Then, the obtained singular value is used as a feature parameter to construct a feature vector. The feature data of seawater hydraulic pump and oil pump are used as target data and auxiliary data to form training data. The training data is trained based on the iterative adjustment of the weight through the TrAdaBoost transfer learning algorithm. Finally, the results of diagnosis and classification are compared with traditional machine learning. When the number of training data is 5 groups, the accuracy of transfer learning is 30.5% higher than that of traditional machine learning. The results show that transfer learning has great advantages in the case of a small number of samples.

Fault diagnosis of aero-hydraulic pump based on casing vibration signal

To effectively extract the characteristics of weak imbalance fault of aircraft hydraulic pump, autocorrelation function (AF) is combined with wavelet transform (AFWT) instead of threshold denoising. Meanwhile, power ratio (PR) was obtained by extracted characteristic frequency and applied to the identification of weak imbalance fault. A contrastive analysis was conducted among different signals, including acceleration and displacement signal. The results indicate that displacement signal, rather than acceleration signal, can effectively identify a weak imbalance fault.

An Integrated Deep Learning Method towards Fault Diagnosis of Hydraulic Axial Piston Pump.

A hydraulic axial piston pump is the essential component of a hydraulic transmission system and plays a key role in modern industry. Considering varying working conditions and the implicity of frequent faults, it is difficult to accurately monitor the machinery faults in the actual operating process by using current fault diagnosis methods. Hence, it is urgent and significant to investigate effective and precise fault diagnosis approaches for pumps. Owing to the advantages of intelligent fault diagnosis methods in big data processing, methods based on deep learning have accomplished admirable performance for fault diagnosis of rotating machinery. The prevailing convolutional neural network (CNN) displays desirable automatic learning ability. Therefore, an integrated intelligent fault diagnosis method is proposed based on CNN and continuous wavelet transform (CWT), combining the feature extraction and classification. Firstly, CWT is used to convert the raw vibration signals into time-frequency representations and achieve the extraction of image features. Secondly, a new framework of deep CNN is established via designing the convolutional layers and sub-sampling layers. The learning process and results are visualized by t-distributed stochastic neighbor embedding (t-SNE). The results of the experiment present a higher classification accuracy compared with other models. It is demonstrated that the proposed approach is effective and stable for fault diagnosis of a hydraulic axial piston pump.

Investigation on performance of a centrifugal pump with multi-malfunction

Herein, the performance and inner flow characteristics of a single-stage single-suction centrifugal pump with multi-malfunctions (broken blade and seal ring abrasion) were determined through tests and numerical simulation. The vibration, inner flow, pressure, and radial force of the centrifugal pump were analyzed in detail. Compared with those of a normal pump, the head and efficiency of the pump with multi-malfunctions decreased by 10.56 and 10.09%, respectively, under the design flow rate. The general vibration level most significantly increased at the foot of the pump. The axial passing frequency of each measuring point increased in varying degrees, and new characteristic frequencies appeared at 5, 2, and 3 axial passing frequencies. The simulation results showed that in the pump with multi-functions, the pressure gradient near the broken blade was distinctly reduced, and the periodicity of the impeller radial force became weaker and more concentrated, thus exhibiting different performance than the normal pump. The peak-to-peak value of the pressure pulsation near the tongue increased by 8.5%, whereas that at the pump outlet decreased by 6.8%. Moreover, a vortex appeared at the inlet and another at the middle of the impeller, and the low-pressure zone near the impeller inlet expanded to the middle of the impeller. The results of this work can be used as reference for pump fault diagnosis.

A new hybrid model of sparsity empirical wavelet transform and adaptive dynamic least squares support vector machine for fault diagnosis of gear pump

Gear pump is the key component in hydraulic drive system, and it is very significant to fault diagnosis for gear pump. The combination of sparsity empirical wavelet transform and adaptive dynamic least squares support vector machine is proposed for fault diagnosis of gear pump in this article. Sparsity empirical wavelet transform is used to obtain the features of the vibrational signal of gear pump, the sparsity function is potential to make empirical wavelet transform adaptive, and adaptive dynamic least squares support vector machine is used to recognize the state of gear pump. The experimental results show that the diagnosis accuracies of sparsity empirical wavelet transform and adaptive dynamic least squares support vector machine are better than those of the empirical wavelet transform and adaptive dynamic least squares support vector machine method or the empirical wavelet transform and least squares support vector machine method.

An Energy Graph-Based Approach to Fault Diagnosis of a Transcritical CO2 Heat Pump

The objective of this paper is to describe an energy-based approach to visualize, identify, and monitor faults that may occur in a water-to-water transcritical CO 2 heat pump system. A representation using energy attributes allows the abstraction of all physical phenomena present during operation into a compact and easily interpretable form. The use of a linear graph representation, with heat pump components represented as nodes and energy interactions as links, is investigated. Node signature matrices are used to present the energy information in a compact mathematical form. The resulting node signature matrix is referred to as an attributed graph and is populated in such a way as to retain the structural information, i.e., where the attribute points to in the physical system. To generate the energy and exergy information for the compilation of the attributed graphs, a descriptive thermal–fluid model of the heat pump system is developed. The thermal–fluid model is based on the specifications of and validated to the actual behavioral characteristics of a physical transcritical CO 2 heat pump test facility. As a first step to graph-matching, cost matrices are generated to represent a characteristic residual between a normal system node signature matrix and a faulty system node signature matrix. The variation in the eigenvalues and eigenvectors of the characteristic cost matrices from normal conditions to a fault condition was used for fault characterization. Three faults, namely refrigerant leakage, compressor failure and gas cooler fouling, were considered. The paper only aims to introduce an approach, with the scope limited to illustration at one operating point and considers only three relatively large faults. The results of the proposed method show promise and warrant further work to evaluate its sensitivity and robustness for small faults.

Research on fault diagnosis and state assessment of vacuum pump based on acoustic emission sensors.

A vacuum pump is a widely used vacuum device and a key component of the space environment simulator. Aiming at the problem of fault diagnosis and state assessment of the vacuum pump, this paper proposes a complete set of empirical mode decomposition [Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)] based on adaptive noise and support vector machine optimized by particle swarm optimization (PSO-SVM). The CEEMDAN method can adaptively decompose the acoustic emission signal of the vacuum pump to obtain several eigenmode functions [Intrinsic Mode Functions (IMFs)] and residuals. The normalized energy values of the IMF component are extracted as the eigenvector. The PSO algorithm is used to optimize the key parameters of the SVM, and the samples are used for training to establish a fault diagnosis model. The vacuum pump overload fault and vacuum pump with different working states are judged by experiments. The results show that the method has an accuracy of more than 97.0% and can effectively realize fault diagnosis and state assessment of vacuum pump equipment.

Fault Diagnosis of Sucker Rod Pump Based on Deep-Broad Learning Using Motor Data

Conventional fault diagnosis methods of sucker rod pump (SRP) mainly focus the operating status of oil well by identifying the dynamometer cards (DCs), which are limited by the sensor maintenance and calibration, battery replacement and safety hazards for staff. Motor power, as the most basic parameter providing the energy source for the oil well, is directly related to the real-time operation state of oil well. Therefore, a novel deep and broad learning system (DBLS) based on motor power data for fault diagnosis of sucker rod pump is proposed in this paper. Considering the key parameters such as mechanical wear and balance weight, the motor power data are labeled by the DCs with typical working conditions. Furthermore, CNN-based feature extractor is designed to make up for the lack of expert experience in motor power, which is obtained by merging the output of the CNNs with the manual features extracted based on mechanical analysis. And then the broad learning system is employed as the classifier to solve the problem of real-time update of system structure. Finally, a dataset containing six different working states collected from the oilfield by a self-developed device is employed to verify the proposed method experimentally and compared with other methods.