Fault Diagnosis Of Hydraulic System Research Articles

Intelligent fault diagnosis methods for hydraulic components based on information fusion: review and prospects

Abstract Hydraulic component faults have the characteristics of nonlinear time-varying signal, strong concealment, and difficult feature extraction, etc. Timely and accurately fault diagnosis of hydraulic components is helpful to curb economic losses and accidents, so researches have carried out a lot of research on hydraulic components. Information fusion technology can combine multi-source data from multiple dimensions to mine fault data characteristics, which effectively improves the accuracy and reliability of fault diagnosis results. However, there is currently a lack of a comprehensive and systematic review in this domain. Therefore, in this paper, the hydraulic components information fusion fault diagnosis technologies are summarized and analyzed, including the main process information fusion fault diagnosis and the research status of information fusion fault diagnosis of hydraulic system. The methods and techniques involved in the fusion process, data source and fusion method of fault diagnosis of hydraulic components information fusion are described and summarized. The problems of information fusion in fault diagnosis of hydraulic components are analyzed, the solutions are discussed, and the research ideas of improving information fusion fault diagnosis are put forward. Finally, digital twin technology is introduced, and the advantages and research status of intelligent fault diagnosis based on digital twin are summarized. On this basis, the intelligent fault diagnosis of hydraulic components based on information fusion is summarized, and the challenges and future research ideas of applying information fusion and digital twin to intelligent fault diagnosis of hydraulic components are put forward and analyzed comprehensively.

Fault Diagnosis of Hydraulic Components Based on Multi-Sensor Information Fusion Using Improved TSO-CNN-BiLSTM.

In order to realize the accurate and reliable fault diagnosis of hydraulic systems, a diagnostic model based on improved tuna swarm optimization (ITSO), optimized convolutional neural networks (CNNs), and bi-directional long short-term memory (BiLSTM) networks is proposed. Firstly, sensor selection is implemented using the random forest algorithm to select useful signals from six kinds of physical or virtual sensors including pressure, temperature, flow rate, vibration, motor power, and motor efficiency coefficient. After that, fused features are extracted by CNN, and then, BiLSTM is applied to learn the forward and backward information contained in the data. The ITSO algorithm is adopted to adaptively optimize the learning rate, regularization coefficient, and node number to obtain the optimal CNN-BiLSTM network. Improved Chebyshev chaotic mapping and the nonlinear reduction strategy are adopted to improve population initialization and individual position updating, further promoting the optimization effect of TSO. The experimental results show that the proposed method can automatically extract fusion features and effectively utilize multi-sensor information. The diagnostic accuracies of the plunger pump, cooler, throttle valve, and accumulator are 99.07%, 99.4%, 98.81%, and 98.51%, respectively. The diagnostic results of noisy data with 0 dB, 5 dB, and 10 dB signal-to-noise ratios (SNRs) show that the ITSO-CNN-BiLSTM model has good robustness to noise interference.

Preface

This Proceedings arises from 2023 6th International Conference on Power Electronics and Control Engineering (ICPECE 2023), which was held via virtual form in Nanning, China from November 10th to 12th, 2023. Meetings in the ICPECE series were and will be held every year, and ICPECE 2023 marks the sixth time that this series of conference had been held successfully.The scientific and social program for the Conference ran over three days with more than 50 delegates from about 10 countries gathering on November 10th and departing on November 12th, including keynote speeches, oral presentations, poster presentations, free discussion, academic investigation, etc.In all there were four keynote speeches by renowned professors during the meeting. Professor Xingjian Wang from Beihang University, China addressed his research to us on Health Status Assessment and Fault-tolerance Control for Aircraft Control System. According to the control command of the pilot or the autopilot, the aircraft control system drives the aircraft rudder through the servo actuator to realize the aircraft attitude and flight trajectory control. In order to realize the highly reliable and healthy service of the aircraft control system, the health status assessment and remaining useful life prediction of the key components of the aircraft control system, the key technology of fault diagnosis for the airborne hydraulic system, and the high safety hierarchical active fault-tolerance control strategy were carried out to effectively improve the healthy service capability of the aircraft control system.Topics that were the focus of invited presentations and contributed papers at ICPECE 2023 included: Motor Drive and Frequency Converter, Drive Motor and Control Technology, Power and Fluid Machinery Equipment, Automatic Control Technology, Computer Integrated Manufacturing System, etc. All submissions were gone through a rigorous review process by performing double blind review to check their quality of content, level of innovation, significance, originality and legibility.The Conference could not have occurred without the generous support of the sponsors and the hard work of all Committee members. We are most grateful for the staff of Journal of Physics: Conference Series for their help and kindness in the preparation of this volume. Finally, we would like to thank all the attendees at ICPECE 2023 for their scientific contributions to the Conference, and for their warm spirit of engagement which characterized the scientific discussions and social occasions.The Committee of ICPECE 2023List of Committee Member is available in this pdf.

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 hydraulic system based on D-S evidence theory and SVM

Fault diagnosis of hydraulic system based on D-S evidence theory and SVM

Preface

The International Conference on Mechatronics, Automation and Intelligent Control (MAIC) has confirmed once again this year the enthusiasm and determination of students and young researchers to play a major role in the scientific progress. Since 2021, we have regularly gathered to discuss the most recent developments and achievement on mechatronics, automation and intelligent control, and we firmly convince that sharing our expertise and experience is the foundation of research activity.The 2023 3rd International Conference on Mechatronics, Automation and Intelligent Control (MAIC 2023) took place from September 15th to 17th, 2023 in Guilin, China (virtual event), attracting about 80 leading delegates from around the world. The format we chose is a formal meeting primarily aimed at graduate students and leading researchers, who were encouraged to present their work through oral or poster presentations that provide genuine engagement of the audience and cross-pollination of ideas. One of the main purposes of the Conference was to create an international network of researchers, both experimentalists and theorists, and fruitful collaborations across different branches of mechatronics, automation and intelligent control.After previous two sessions that strengthened it, the International Conference on Mechatronics, Automation and Intelligent Control was held for the third time. It was a three-day Conference. The success of this year’s event was testified by an increased number of participants and institutions all around the world. This resulted in an extremely rich and interactive keynote speech, oral and poster session that covered several areas related to mechatronics, automation and intelligent control.With the intent of broadening the contents and stimuli adopting academic communication, the keynote speech part was held. This choice reflected the growing importance of the activity performed by scientists and researchers, especially at the earliest stages of their career, as a way of increasing their expertise and developing soft skills. During the meeting, Prof. Xingjian Wang from Beihang University, China shared with us his research on Fault Diagnosis and Fault-tolerance Control for Aircraft Control System. In order to realize the highly reliable and healthy service of the aircraft control system, the health status assessment and remaining useful life prediction of the key components of the aircraft control system, the key technology of fault diagnosis for the airborne hydraulic system, and the high safety hierarchical active fault-tolerance control strategy were carried out to effectively improve the healthy service capability of the aircraft control system.This paper volume cover topics on Industrial Robots and Automatic Production Line, Automatic Control Technology, Intelligent Fault Detection and Identification, Man-Machine Interactions, etc. Given the recent experimental achievements in mechatronics, automation and intelligent control, several contributions were focused on the latest results obtained in these fields, presenting the impact of experiments to the community of young researchers and forecasting the future goals in these areas of research.The organizers of MAIC 2023 would like to thank all the keynote speakers, authors, peer reviewers, and everyone who contributed to the Conference. We owe gratitude to all the Committee members for providing necessary help and support throughout the meeting. The publication of the Proceedings of this Conference is supported by Journal of Physics: Conference Series.The Committee of MAIC 2023List of Committee Member is available in this pdf.

Fault Diagnosis of Hydraulic System based on SOM Neural Network

The working principle of hydraulic system is to use the flow and pressure of liquid in the system for energy transfer and conversion. Hydraulic system realizes various work tasks such as pushing, grasping, lifting, rotating, etc. by controlling the action of hydraulic actuators. Its advantages are high power density, good stability, fast response time, smooth power output, etc. The disadvantage is that the hydraulic system is easy to lose control of more points. In this paper, taking the horizontal outrigger hydraulic circuit as an example, the hydraulic pump leakage, etc. is used as a fault sample, and the SOM neural network is used for fault diagnosis to make timely and accurate diagnosis of the abnormal or fault state of the hydraulic system, give guidance on the operation of the hydraulic system, improve the reliability and safety of operation, and reduce the fault loss to a minimum.

Fault Analysis and Diagnosis of Downhole Traction Robot Hydraulic System

Fault diagnosis of downhole traction robot has characteristics of special mechanical structure, complicated working condition and lack of research data. In this paper, a method of fault analysis and diagnosis of tractor hydraulic system based on model is presented. The failure mode and mechanism of key components in telescopic hydraulic system are analyzed. The fault diagnosis of hydraulic system is proposed based on the mathematical model of hydraulic component. Based on AMESim, the fault model of hydraulic system is established, and the diagnosis of single fault and multi-fault are simulated, which can collect the fault data and the specific diagnosis process. To carry forward the engineering application, the simulated internal leakage system in cylinder is set up and schemes of fault diagnosis are carried out, which prove that the diagnosis is consistent with theoretical analysis and simulation. The research on fault analysis and diagnosis of the downhole traction robot hydraulic system not only lays theoretical basis for the tractor fault detection, but also provides guidance for the construction and implementation of the diagnostic system.

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.

Ensemble Multiple Distinct ResNet Networks With Channel-Attention Mechanism for Multisensor Fault Diagnosis of Hydraulic Systems

Hydraulic systems are widely used in modern industries, and the study of efficient and accurate fault diagnosis techniques for hydraulic systems can help prevent accidents and reduce economic losses. Most of the existing deep learning-based intelligent diagnosis methods use only single-channel signals for diagnosis, which may ignore some important fault information. In addition, the existing deep learning algorithms cannot distinguish the sensitivity of inter-channel features, and the important features of the channel domain cannot be emphasized. To solve the above problems, this paper proposes a multi-channel data-driven framework integrated distinct ResNet networks with channel-attention mechanism (CM-ResNet) for fault diagnosis of hydraulic systems. Firstly, the multi-channel signals are converted into 2-dimensional feature maps by continuous wavelet transform. Then, multiple CM-ResNet are trained as base-learners based on the signals of each channel. Third, the category probabilities of each base-learner are concatenated into new feature vectors. Finally, the new feature vectors are used to train the meta-learner and perform fault diagnosis. The meta-learner can capture the complex nonlinear relationships between the base-learners and obtain a stronger learner. Experimental results show that the performance of our proposed method outperforms the comparison method and the already existing methods.

Fault diagnosis of complex hydraulic system based on fast Mahalanobis classification system with high-dimensional imbalanced data

Due to the complexity of hydraulic systems, the data generated is prone to be high-dimensional and imbalanced, posing a challenge for fault diagnosis. This paper proposes a fast Mahalanobis classification system (FMCS). At the dimensionality reduction stage, smymetrical uncertainty and Mahalanobis kernel principal component analysis are used to construct a two-stage fast dimensionality reduction method. At the weight distribution stage, Mahalanobis distance is used to measure the similarity between the main feature and other features to determine the feature weights. At the fault diagnosis stage, Mahalanobis-Taguchi system is improved in threshold determination by atom search optimization to achieve fault diagnosis. Experiments show that FMCS has better classification performance (accuracy = 0.92, F1 = 0.94, G-mean = 0.91) and shorter computing time (4.9045 s) compared with 24 baseline algorithms. FMCS promotes the development of intelligent fault diagnosis for complex hydraulic systems.

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.

Research on Fault Diagnosis of HMCVT Shift Hydraulic System Based on Optimized BPNN and CNN

There are some problems in the shifting process of hydraulic CVT, such as irregularity, low stability and high failure rate. In this paper, the BP neural network and convolutional neural network are used for fault diagnosis of the HMCVT hydraulic system. Firstly, through experiments, 120 groups of pressure and flow data under normal and four typical fault modes were obtained and preprocessed; they were divided into 80 groups of training samples and 40 groups of test samples via random extraction, using the BP neural network model and convolutional neural network model for fault classification. The results show that compared with BP, PSO-BP and other models, the fault diagnosis rate of the BAS-BP neural network model can reach 92.5%, and the average diagnosis accuracy rate of the convolutional neural network can reach 97.5%, which can be effectively applied to the fault diagnosis of the HMCVT hydraulic system and provide some reference for the shifting reliability of hydraulic CVT.

A Fault Diagnosis Method under Data Imbalance Based on Generative Adversarial Network and Long Short-Term Memory Algorithms for Aircraft Hydraulic System

Safe and stable operation of the aircraft hydraulic system is of great significance to the flight safety of an aircraft. Any fault may be a threat to flight safety and may lead to enormous economic losses and even human casualties. Hence, the normal status of the aircraft hydraulic system is large, but very few data samples relate to the fault status. This causes a data imbalance in the fault diagnosis of the aircraft hydraulic system, which directly affects the accuracy of aircraft fault diagnosis. To solve the data imbalance problem in the fault diagnosis of the aircraft hydraulic system, this paper proposes an improved GAN-LSTM algorithm by using the improved GAN method, which can stably and accurately generate high-quality simulated fault samples using a small number of fault data. First, the model of the aircraft hydraulic system was built using AMESim software, and the imbalanced fault data and normal status data were acquired. Then, the imbalanced data were used to train the GAN model until the system reached a Nash equilibrium. By comparing the time domain and frequency signal, it was found that the quality of the generated sample was highly similar to the real sample. Moreover, LSTM (long short-term memory) and some other data-driven intelligent fault diagnosis methods were used as classifiers. The accuracy of these fault diagnosis methods increased steadily when the number of fault samples was gradually increased until it reached a balance with the normal sample. Meanwhile, three different sample generation methods were compared and analyzed to find the method with the best data generation ability. Finally, the anti-noise performance of the LSTM-GAN method was analyzed; this model has superior noise immunity.

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.

Fault diagnosis of hydraulic system based on D-S evidence theory and SVM

Fault diagnosis of hydraulic system based on D-S evidence theory and SVM

A New Method for Fault Diagnosis of Hydraulic System Based on Improved Empirical Wavelet Transform and Kernel Extreme Learning Machine

A New Method for Fault Diagnosis of Hydraulic System Based on Improved Empirical Wavelet Transform and Kernel Extreme Learning Machine