Hydraulic System Fault Research Articles

A fault diagnosis method for hydraulic system based on multi-branch neural networks

As a key part of construction machinery, the hydraulic system is widely used in industrial fields. Due to its complexity and high integration, the fault diagnosis of hydraulic systems has always been a challenging problem. However, existing methods or models mainly focus on individual hydraulic system components while overlooking the interactions between different components, which are limited to specific application scenarios. Therefore, this research aims to develop a multi-task network model to diagnose faults in multiple components of hydraulic systems. Firstly, the initial data was collected from the hydraulic system test bench, in which the extreme gradient boosting (XGBoost) was introduced to evaluate the importance of all features under different fault types. Then, reduction dimensionality is achieved by setting a threshold for feature selection. Subsequently, a novel multi-branch deep neural network (MBDNN), which utilizes multiple branches to extract different information and correlations in the input data, is proposed and established. The multi-level combination of residual block and multi-branch connections enables MBDNN to achieve multiple types of fault diagnosis simultaneously and compensate for the problem of insufficient information representation in single-branch neural networks. The results of multiple rounds of experimental indicate that the MBDNN has higher robustness and accuracy in hydraulic system fault diagnosis than the existing general methods, and the diagnostic accuracy of multi-type faults is improved to 98.5%.

Development of a Method for Diagnosing Faults in Hydraulic Systems Using Artificial Neural Networks with Deep Learning

Development of a Method for Diagnosing Faults in Hydraulic Systems Using Artificial Neural Networks with Deep Learning

Hydraulic system fault diagnosis decoupling method based on 2D time-series modeling and self-attention fusion

Hydraulic systems play a pivotal and extensive role in mechanics and energy. However, the performance of intelligent fault diagnosis models for multiple components is often hindered by the complexity, variability, strong hermeticity, intricate structures, and fault concealment in real-world conditions. This study proposes a new approach for hydraulic fault diagnosis that leverages 2D temporal modeling and attention mechanisms for decoupling compound faults and extracting features from multisample rate sensor data. Initially, to address the issue of oversampling in some high-frequency sensors within the dataset, variable frequency data sampling is employed during the data preprocessing stage to resample redundant data. Subsequently, two-dimensional convolution simultaneously captures both the instantaneous and long-term features of the sensor signals for the coupling signals of hydraulic system sensors. Lastly, to address the challenge of feature fusion with multisample rate sensor data, where direct merging of features through maximum or average pooling might dilute crucial information, a feature fusion and decoupling method based on a probabilistic sparse self-attention mechanism is designed, avoiding the issue of long-tail distribution in multisample rate sensor data. Experimental validation showed that the proposed model can effectively utilize samples to achieve accurate fault decoupling and classification for different components, achieving a diagnostic accuracy exceeding 97% and demonstrating robust performance in hydraulic system fault diagnosis under noise conditions.

Research on fault diagnosis method of the hydraulic system based on digital twin

Abstract As an advanced technology, digital-twin (DT) is an effective method to achieve information fusion and has been widely applied in fault diagnosis (FD). An integrated model, which consists of the DT model, consistency model, and FD network, is developed to detect faults in hydraulic systems (HS) by the dual effective information communication between the virtual model and real system. The consistency between the model and the real system can be guaranteed with the consistency model. A large number of labeled datasets generated by the DT model are used to train the FD network, which results in a significant reduction in the uncertainty of model parameters. A case study is done to investigate the performance of the DT method and the results show that the method can diagnose faults of HS effectively, efficiently, and accurately.

Real-Time Fault Diagnosis for Hydraulic System Based on Multi-Sensor Convolutional Neural Network.

This paper proposed a real-time fault diagnostic method for hydraulic systems using data collected from multiple sensors. The method is based on a proposed multi-sensor convolutional neural network (MS-CNN) that incorporates feature extraction, sensor selection, and fault diagnosis into an end-to-end model. Both the sensor selection process and fault diagnosis process are based on abstract fault-related features learned by a CNN deep learning model. Therefore, compared with the traditional sensor-and-feature selection method, the proposed MS-CNN can find the sensor channels containing higher-level fault-related features, which provides two advantages for diagnosis. First, the sensor selection can reduce the redundant information and improve the diagnostic performance of the model. Secondly, the reduced number of sensors simplifies the model, reducing communication burden and computational complexity. These two advantages make the MS-CNN suitable for real-time hydraulic system fault diagnosis, in which the multi-sensor feature extraction and the computation speed are both significant. The proposed MS-CNN approach is evaluated experimentally on an electric-hydraulic subsea control system test rig and an open-source dataset. The proposed method shows obvious superiority in terms of both diagnosis accuracy and computational speed when compared with traditional CNN models and other state-of-the-art multi-sensor diagnostic methods.

Fault diagnosis of power-shift system in continuously variable transmission tractors based on improved echo state network

For better reliability of tractors with continuously variable transmission, reported here is fault diagnosis of their power-shift systems. First, four hydraulic system faults are analyzed, i.e., pipe leakage, pipe blockage, a stuck solenoid valve spool, and a stuck clutch piston, and it is shown that these lead to clutch energy loss during power shifting and possibly even clutch burn-out. Second, fault simulations give more than 30000 groups of test data, and an improved dynamic time warping algorithm is used to segment the data samples automatically. Third, an improved echo state network is proposed to classify the above fault samples, and its performance is compared with those of traditional algorithms. Finally, the robustness of the proposed algorithm is tested using samples with noise superimposed. The results show that the accuracy, precision, recall, and F1-score of the improved echo state network are 96.87%, 96.31%, 97.11%, and 96.71%, respectively, making it equivalent in performance to a convolutional neural network with long short-term memory. However, the training speed of the former is significantly better than that of the latter, and even under the influence of strong noise, the proposed algorithm can still achieve an accuracy of 91.32%, which is better than the latter’s 86.62%. This is significantly better than traditional algorithms, thus demonstrating the better generalization characteristics and robustness of the proposed approach.

Fault Classification for Cooling System of Hydraulic Machinery Using AI.

Hydraulic systems are used in all kinds of industries. Mills, manufacturing, robotics, and Ports require the use of Hydraulic Equipment. Many industries prefer to use hydraulic systems due to their numerous advantages over electrical and mechanical systems. Hence, the growth in demand for hydraulic systems has been increasing over time. Due to its vast variety of applications, the faults in hydraulic systems can cause a breakdown. Using Artificial-Intelligence (AI)-based approaches, faults can be classified and predicted to avoid downtime and ensure sustainable operations. This research work proposes a novel approach for the classification of the cooling behavior of a hydraulic test rig. Three fault conditions for the cooling system of the hydraulic test rig were used. The spectrograms were generated using the time series data for three fault conditions. The CNN variant, the Residual Network, was used for the classification of the fault conditions. Various features were extracted from the data including the F-score, precision, accuracy, and recall using a Confusion Matrix. The data contained 43,680 attributes and 2205 instances. After testing, validating, and training, the model accuracy of the ResNet-18 architecture was found to be close to 95%.

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.

Reliable composite fault diagnosis of hydraulic systems based on linear discriminant analysis and multi-output hybrid kernel extreme learning machine

With increasingly stringent in requirements on the reliability and safety of hydraulic systems, data-driven fault diagnosis has emerged as a popular area of research. Hydraulic systems may have multiple failure modes, and accurately diagnosing compound failures in multi-component systems is a daunting task. In this paper, a method of multi-output classification by combining linear discriminant analysis (LDA) with the hybrid kernel extreme learning machine (HKELM) is proposed to diagnose compound faults in hydraulic systems. Data selection based on LDA is used in place of expert knowledge to screen out sensitive channels of each component from multi-channel signals. The multi-output strategy is embedded into the HKELM, which can simultaneously output the fault status of multiple components to diagnose the health of the system. An improved Hamming loss is also proposed to evaluate the total error in the multi-output classification because it has greater applicative relevance than classification accuracy. The results of experiments show that the proposed method can diagnose composite faults in multi-component systems with an accuracy higher than 99.5% and an error of only 0.20% on a dataset of hydraulic systems. As a shallow feed-forward network model, it can be used for real-time fault diagnosis due to its efficiency.

Hydraulic system fault diagnosis of the chain jacks based on multi-source data fusion

In this study, a fault diagnosis approach for the hydraulic system of chain jacks based on multi-source sensor data fusion is proposed. We developed a hydraulic test rig for the chain jacks with a special measurement and control system to measure and collect real-time data on pressure, temperature and flow in different operating conditions. The proposed approach integrates convolutional neural networks (CNN) and long and short-term memory (LSTM) at the network level to extract the spatial–temporal features of the time-series data measured by sensors. Compared with the artificial neural network (ANN), the accuracy of the CNN-LSTM hierarchical diagnosis model was 96.4%, which improved the diagnosis accuracy by 4.4% and enhanced the generalization ability and stability. This study provides a hierarchical monitoring approach for the service status of marine spread mooring systems and chain jack equipment, which is essential for the safe operation of marine equipment.

Research on the Analysis of Wear Mechanism and Improvement of the Two-way Limit Valve

Aiming at the pollution fault of aircraft hydraulic system caused by the abnormal wear of the two-way limit valve, the method combining theoretical analysis and numerical simulation is used for fault location analysis based on the structural characteristics of the two-way limit valve, and the effect of the main affecting factors, such as the fitting size between parts and the position deviation of the filter holes, on the wear is studied. The results show that the wear of the two-way limit valve is mainly caused by axial assembly gap inside the product, and assembly gap greater than 0.019mm will cause abnormal wear. To solve the problem, it is suggested that no axial assembly gap will be controlled for subsequent product assembly. The two-way limit valve after 100 times of bidirectional alternating pressure supply test has normal performance and no wear phenomenon occurs.

A bagging-strategy based heterogeneous ensemble deep neural networks approach for the multiple components fault diagnosis of hydraulic systems

Hydraulic systems faults have the characteristics of being highly concealed and unclear. Due to the characteristics of the complex vibration transmission mechanism and strong nonlinear time-varying signals in hydraulic systems, it is extremely difficult to achieve fault diagnosis for hydraulic systems. Different components of the system can fail individually or simultaneously. Signal processing faces the problem of coupling between multi-component faults, which makes it more difficult to realise multi-component fault diagnosis. On the one hand, existing techniques rely on hand-designed features and only use a traditional single shallow machine model as the base classifier, and these do not have the ability to self-learn meaningful features. On the other hand, the diagnostic performance of a single base classifier sometimes does not meet engineering requirements. To handle the above problems, a bagging strategy based heterogeneous ensemble deep neural networks (DNNs) approach is proposed for the multiple components fault diagnosis of hydraulic systems. First, Pearson correlation coefficient and neighbourhood component analysis are developed for data channel selection and feature dimensionality reduction. Second, two distinct DNNs are constructed as base learners: a stacked sparse autoencoder and a deep hierarchical extreme-learning machine. Finally, a bagging strategy is adopted to integrate different DNNs to obtain robust diagnostic results. The results from this experiment demonstrate that the proposed method can precisely diagnose hydraulic system faults compared with comparative methods.

Operation reliability monitoring towards fault diagnosis of airplane hydraulic system using Quick Access Recorder flight data

Hydraulic system operation reliability (HSOR) can evaluate time series state reliability for hydraulic system fault diagnosis and provide condition based maintenance decisions. The quick access recorder (QAR) flight data and normal values of the hydraulic system are utilized to analyze time series HSOR by calculating the operation reliability index. Considering the relationship of the hydraulic subsystem among the components, hydraulic components Bayesian Network is constructed to analyze time series HSOR. Furthermore, the sensitivity of HSOR features to fault location is assessed using categorical boosting (CatBoost) and Shapley Additive ex-Planations values. Through the analysis of two flights hydraulic system QAR datasets, it is revealed that (a) HSOR can accurately monitor the time series operating states of the hydraulic system; and (b) with demonstrating two illustrative case, the HSOR values and features sensitivity analysis can be a useful reference for the fault diagnosis and location of the airplane hydraulic system. The study intends to develop a practical reference approach for hydraulic system fault diagnosis and location using QAR data.

An EMD-LSTM Deep Learning Method for Aircraft Hydraulic System Fault Diagnosis under Different Environmental Noises

Aircraft hydraulic fault diagnosis is an important technique in aircraft systems, as the hydraulic system is one of the key components of an aircraft. In aircraft hydraulic system fault diagnosis, complex environmental noises will lead to inaccurate results. To address the above problem, hydraulic system fault detection methods should be capable of noise resistance. Previous research has mainly focused on noise-free conditions and many effective approaches have been proposed; however, in real-world aircraft flying conditions, the aircraft hydraulic system often has strong and complex noises. The methods proposed may not have good fault detection results in such a noisy environment. According to the situation, this work focuses on aircraft hydraulic system fault classification under the influence of a hydraulic working environment with Gaussian white noise. In order to eliminate the noise interference and adapt to the actual noisy environment, a new aircraft hydraulic fault diagnostic method based on empirical mode deposition (EMD) and long short-term memory (LSTM) is presented. First, the hydraulic system is constructed by AMESIM. One normal state and five fault states are considered in this paper. Eight-channel signals of different states are collected for network training and testing. Second, the EMD method is used to obtain the different intrinsic mode functions (IMFs) of the signals. Third, principal component analysis (PCA) is used to obtain the main component of the IMFs. Fourth, three different LSTM methods are chosen to compare and the best structure that is chosen is the gate recurrent unit (GRU). After that, the network parameters are optimized. The results under different noise environments are given. Then, a comparison between the EMD-GRU with several different machine learning methods is considered, and the result shows that the method in this paper has a better anti-noise effect. Therefore, the proposed method is demonstrated to have a strong ability of fault diagnosis and classification under the working noises based on the simulation results.

Pressure Sensor Fault Diagnosis by Using Adaptive Robust Observers

The model uncertaint of hydraulic systems is a key issue in observer-based fault diagnosis. In this research, for diagnosing pressure sensor faults in hydraulic systems, we present the application of adaptive robust observers design. For reducing model uncertainty and increasing the feasibility of the fault diagnosis scheme, this paper uses a robust filter and controlled parameter adaption to deal with model uncertainty. Then, according to the fault system model, the fault magnitude of the pressure sensor fault is estimated and the residual variance can be generated for diagnosing the sensor fault. By experiments, the fault diagnosis scheme is proven efficient, which is based on adaptive robust observers. Then, the fault magnitude can be estimated.

Influence of high pressure pulsating excitation on pipe joint sealing based on multiscale contact analyses

Most of the leakage faults in hydraulic pipeline systems occur in the pipe joint. The research on the high-pressure sealing and reliability of the pipe joint is very important in the application of civil aircraft parts. On the basis of studying the characteristics of pressure pulsating fluid, this paper explores the influence of a high pressure pulsating fluid on the sealing performance of flameless extruded pipe joints and analyzes the influence of the internal fluid pulsating amplitude and fluid under different pressures on the contact stress distribution of the sealing surface of the pipe joint. The results show that with the increase in flow pulsation amplitude from 0.5% to 5%, the amplitude of contact stress on the sealing surface decreases with creep time, which makes the sealing performance better. However, the effective working time of the joint as a whole decreases from 2000 to 194 h. The high-stress area of the contact surface expands as the fluid pressure increases, and the high internal fluid pressure obviously improves the sealing effectiveness and reliability of the pipe joint. In addition, the influence of fluid pulsation amplitude on the leakage rate of the joint is much greater than that of the fluid pressure.

Fault Diagnosis for Aircraft Hydraulic Systems via One-Dimensional Multichannel Convolution Neural Network

Detecting the faults in hydraulic systems in advance is difficult owing to the complexity associated with such systems. Hence, it is necessary to investigate the different fault modes and analyze the system reliability in order to establish a method for improving the reliability and security of hydraulic systems. To this end, this paper proposes a novel one-dimensional multichannel convolution neural network (1DMCCNN) for diagnosing fault modes. In this work, a landing gear hydraulic system was constructed with a normal model and a fault model; five types of faults were considered. Pressure signals were extracted from this hydraulic system, and the extracted signals were subsequently input into the convolution neural network (CNN) as multichannel data. Thereafter, the data were subjected to a one-dimensional convolution filter. The differences between channels were used to enhance features. The features obtained in this manner were compared for fault diagnoses. Furthermore, this proposed method was verified via simulations; the simulation results indicated that the precision of the 1DMCCNN was considerably higher than that of conventional machine learning algorithms.

Research on Fault Diagnosis of Hydraulic System of Fast Erecting Device Based on Fuzzy Neural Network

In order to improve the fault diagnosis effectiveness of hydraulic system in erecting devices, the fuzzy neural neural network is applied to carry out fault diagnosis of hydraulic system. Firstly, the main faults of hydraulic system of erecting mechanism are summarized. The main faults of hydraulic system of erecting devices concludes abnormal noise, high temperature of hydraulic oil of hydraulic system, leakage of hydraulic system, low operating speed of hydraulic system, and the characteristics of different faults are analyzed. Secondly, basic theory of fuzzy neural network is studied, and the framework of fuzzy neural network is designed. The inputting layer, fuzzy layer, fuzzy relation layer, relationship layer after fuzzy operation and outputting layer of fuzzy neural network are designed, and the corresponding mathematical models are confirmed. The analysis procedure of fuzzy neural network is established. Thirdly, simulation analysis is carried out for a hydraulic system in erecting device, the BP neural network reaches convergence after 600 times iterations, and the fuzzy neural network reaches convergence after 400 times iterations, fuzzy neural network can obtain higher accuracy than BP neural network, and running time of fuzzy neural network is less than that of BP neural network, therefore, simulation results show that the fuzzy neural network can effectively improve the fault diagnosis efficiency and precision. Therefore, the fuzzy neural network is reliable for fault diagnosis of hydraulic system in erecting devices, which has higher fault diagnosis effect, which can provide the theory basis for healthy detection of hydraulic system in erecting devices.

Improved Fault Diagnosis in Hydraulic Systems with Gated Convolutional Autoencoder and Partially Simulated Data.

This paper examines the effectiveness of neural network algorithms for hydraulic system fault detection and a novel neural network architecture is suggested. The proposed gated convolutional autoencoder was trained on a simulated training set augmented with just 0.2% data from the real test bench, dramatically reducing the time needed to spend with the actual hardware to build a high-quality fault detection model. Our fault detection model was validated on a test bench and showed accuracy of more than 99% of correctly recognized hydraulic system states with a 10-s sampling window. This model can be also leveraged to examine the decision boundaries of the classifier in the two-dimensional embedding space.

Sensor and Component Fault Detection and Diagnosis for Hydraulic Machinery Integrating LSTM Autoencoder Detector and Diagnostic Classifiers.

Anomaly occurrences in hydraulic machinery might lead to massive system shut down, jeopardizing the safety of the machinery and its surrounding human operator(s) and environment, and the severe economic implications following the faults and their associated damage. Hydraulics are mostly placed in ruthless environments, where they are consistently vulnerable to many faults. Hence, not only are the machines and their components prone to anomalies, but also the sensors attached to them, which monitor and report their health and behavioral changes. In this work, a comprehensive applicational analysis of anomalies in hydraulic systems extracted from a hydraulic test rig was thoroughly achieved. First, we provided a combination of a new architecture of LSTM autoencoders and supervised machine and deep learning methodologies, to perform two separate stages of fault detection and diagnosis. The two phases were condensed by—the detection phase using the LSTM autoencoder. Followed by the fault diagnosis phase represented by the classification schema. The previously mentioned framework was applied to both component and sensor faults in hydraulic systems, deployed in the form of two in-depth applicational experiments. Moreover, a thorough literature review of related work from the past decade, for autoencoders related fault detection and diagnosis in hydraulic systems, was successfully conducted in this study.