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Machine learning for fault diagnosis of high-speed train traction systems: A review

《工程管理前沿(英文)》 2024年 第11卷 第1期   页码 62-78 doi: 10.1007/s42524-023-0256-2

摘要: High-speed trains (HSTs) have the advantages of comfort, efficiency, and convenience and have gradually become the mainstream means of transportation. As the operating scale of HSTs continues to increase, ensuring their safety and reliability has become more imperative. As the core component of HST, the reliability of the traction system has a substantially influence on the train. During the long-term operation of HSTs, the core components of the traction system will inevitably experience different degrees of performance degradation and cause various failures, thus threatening the running safety of the train. Therefore, performing fault monitoring and diagnosis on the traction system of the HST is necessary. In recent years, machine learning has been widely used in various pattern recognition tasks and has demonstrated an excellent performance in traction system fault diagnosis. Machine learning has made considerably advancements in traction system fault diagnosis; however, a comprehensive systematic review is still lacking in this field. This paper primarily aims to review the research and application of machine learning in the field of traction system fault diagnosis and assumes the future development blueprint. First, the structure and function of the HST traction system are briefly introduced. Then, the research and application of machine learning in traction system fault diagnosis are comprehensively and systematically reviewed. Finally, the challenges for accurate fault diagnosis under actual operating conditions are revealed, and the future research trends of machine learning in traction systems are discussed.

关键词: high-speed train     traction systems     machine learning     fault diagnosis    

Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical faultdiagnosis of bearings

《机械工程前沿(英文)》 2021年 第16卷 第4期   页码 814-828 doi: 10.1007/s11465-021-0650-6

摘要: The fault diagnosis of bearings is crucial in ensuring the reliability of rotating machinery. Deep neural networks have provided unprecedented opportunities to condition monitoring from a new perspective due to the powerful ability in learning fault-related knowledge. However, the inexplicability and low generalization ability of fault diagnosis models still bar them from the application. To address this issue, this paper explores a decision-tree-structured neural network, that is, the deep convolutional tree-inspired network (DCTN), for the hierarchical fault diagnosis of bearings. The proposed model effectively integrates the advantages of convolutional neural network (CNN) and decision tree methods by rebuilding the output decision layer of CNN according to the hierarchical structural characteristics of the decision tree, which is by no means a simple combination of the two models. The proposed DCTN model has unique advantages in 1) the hierarchical structure that can support more accuracy and comprehensive fault diagnosis, 2) the better interpretability of the model output with hierarchical decision making, and 3) more powerful generalization capabilities for the samples across fault severities. The multiclass fault diagnosis case and cross-severity fault diagnosis case are executed on a multicondition aeronautical bearing test rig. Experimental results can fully demonstrate the feasibility and superiority of the proposed method.

关键词: bearing     cross-severity fault diagnosis     hierarchical fault diagnosis     convolutional neural network     decision tree    

Gear fault diagnosis using gear meshing stiffness identified by gearbox housing vibration signals

《机械工程前沿(英文)》 2022年 第17卷 第4期 doi: 10.1007/s11465-022-0713-3

摘要: Gearbox fault diagnosis based on vibration sensing has drawn much attention for a long time. For highly integrated complicated mechanical systems, the intercoupling of structure transfer paths results in a great reduction or even change of signal characteristics during the process of original vibration transmission. Therefore, using gearbox housing vibration signal to identify gear meshing excitation signal is of great significance to eliminate the influence of structure transfer paths, but accompanied by huge scientific challenges. This paper establishes an analytical mathematical description of the whole transfer process from gear meshing excitation to housing vibration. The gear meshing stiffness (GMS) identification approach is proposed by using housing vibration signals for two stages of inversion based on the mathematical description. Specifically, the linear system equations of transfer path analysis are first inverted to identify the bearing dynamic forces. Then the dynamic differential equations are inverted to identify the GMS. Numerical simulation and experimental results demonstrate the proposed method can realize gear fault diagnosis better than the original housing vibration signal and has the potential to be generalized to other speeds and loads. Some interesting properties are discovered in the identified GMS spectra, and the results also validate the rationality of using meshing stiffness to describe the actual gear meshing process. The identified GMS has a clear physical meaning and is thus very useful for fault diagnosis of the complicated equipment.

关键词: gearbox fault diagnosis     meshing stiffness     identification     transfer path     signal processing    

Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

《机械工程前沿(英文)》 2021年 第16卷 第4期   页码 829-839 doi: 10.1007/s11465-021-0652-4

摘要: Existing fault diagnosis methods usually assume that there are balanced training data for every machine health state. However, the collection of fault signals is very difficult and expensive, resulting in the problem of imbalanced training dataset. It will degrade the performance of fault diagnosis methods significantly. To address this problem, an imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning is proposed in this paper. Unsupervised autoencoder is firstly used to compress every monitoring signal into a low-dimensional vector as the node attribute in the SuperGraph. And the edge connections in the graph depend on the relationship between signals. On the basis, graph convolution is performed on the constructed SuperGraph to achieve imbalanced training dataset fault diagnosis for rotating machinery. Comprehensive experiments are conducted on a benchmarking publicized dataset and a practical experimental platform, and the results show that the proposed method can effectively achieve rotating machinery fault diagnosis towards imbalanced training dataset through graph feature learning.

关键词: imbalanced fault diagnosis     graph feature learning     rotating machinery     autoencoder    

New method of fault diagnosis of rotating machinery based on distance of information entropy

Houjun SU, Tielin SHI, Fei CHEN, Shuhong HUANG

《机械工程前沿(英文)》 2011年 第6卷 第2期   页码 249-253 doi: 10.1007/s11465-011-0124-3

摘要:

This paper introduces the basic conception of information fusion and some fusion diagnosis methods commonly used nowadays in rotating machinery. From the thought of the information fusion, a new quantitative feature index monitoring and diagnosing the vibration fault of rotating machinery, which is called distance of information entropy, is put forward on the basis of the singular spectrum entropy in time domain, power spectrum entropy in frequency domain, wavelet energy spectrum entropy, and wavelet space feature entropy in time-frequency domain. The mathematic deduction suggests that the conception of distance of information entropy is accordant with the maximum subordination principle in the fuzzy theory. Through calculation it has been proved that this method can effectively distinguish different fault types. Then, the accuracy of rotor fault diagnosis can be improved through the curve chart of the distance of information entropy at multi-speed.

关键词: rotating machinery     information fusion     fault diagnosis     Information entropy     distance of the information entropy    

A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

《机械工程前沿(英文)》 2022年 第17卷 第2期 doi: 10.1007/s11465-022-0673-7

摘要: Convolutional neural network (CNN) has achieved remarkable applications in fault diagnosis. However, the tuning aiming at obtaining the well-trained CNN model is mainly manual search. Tuning requires considerable experiences on the knowledge on CNN training and fault diagnosis, and is always time consuming and labor intensive, making the automatic hyper parameter optimization (HPO) of CNN models essential. To solve this problem, this paper proposes a novel automatic CNN (ACNN) for fault diagnosis, which can automatically tune its three key hyper parameters, namely, learning rate, batch size, and L2-regulation. First, a new deep reinforcement learning (DRL) is developed, and it constructs an agent aiming at controlling these three hyper parameters along with the training of CNN models online. Second, a new structure of DRL is designed by combining deep deterministic policy gradient and long short-term memory, which takes the training loss of CNN models as its input and can output the adjustment on these three hyper parameters. Third, a new training method for ACNN is designed to enhance its stability. Two famous bearing datasets are selected to evaluate the performance of ACNN. It is compared with four commonly used HPO methods, namely, random search, Bayesian optimization, tree Parzen estimator, and sequential model-based algorithm configuration. ACNN is also compared with other published machine learning (ML) and deep learning (DL) methods. The results show that ACNN outperforms these HPO and ML/DL methods, validating its potential in fault diagnosis.

关键词: deep reinforcement learning     hyper parameter optimization     convolutional neural network     fault diagnosis    

Fault diagnosis of axial piston pumps with multi-sensor data and convolutional neural network

《机械工程前沿(英文)》 2022年 第17卷 第3期 doi: 10.1007/s11465-022-0692-4

摘要: Axial piston pumps have wide applications in hydraulic systems for power transmission. Their condition monitoring and fault diagnosis are essential in ensuring the safety and reliability of the entire hydraulic system. Vibration and discharge pressure signals are two common signals used for the fault diagnosis of axial piston pumps because of their sensitivity to pump health conditions. However, most of the previous fault diagnosis methods only used vibration or pressure signal, and literatures related to multi-sensor data fusion for the pump fault diagnosis are limited. This paper presents an end-to-end multi-sensor data fusion method for the fault diagnosis of axial piston pumps. The vibration and pressure signals under different pump health conditions are fused into RGB images and then recognized by a convolutional neural network. Experiments were performed on an axial piston pump to confirm the effectiveness of the proposed method. Results show that the proposed multi-sensor data fusion method greatly improves the fault diagnosis of axial piston pumps in terms of accuracy and robustness and has better diagnostic performance than other existing diagnosis methods.

关键词: axial piston pump     fault diagnosis     convolutional neural network     multi-sensor data fusion    

大型火电机组的振动故障诊断

于文虎,宋斌

《中国工程科学》 2001年 第3卷 第1期   页码 44-50

摘要:

文章回顾总结了国内外火电机组振动故障诊断的发展状况,指出了目前振动故障诊断研究存在的问题及确定故障诊断知识范围、进行机组轴系传递特性研究的重要性,对大型火电机组振动及性能远程诊断系统进行了介绍,最后指出了振动故障诊断的发展方向。

关键词: 汽轮机组     故障诊断     振动     性能诊断    

Iterative HOEO fusion strategy: a promising tool for enhancing bearing fault feature

《机械工程前沿(英文)》 2023年 第18卷 第1期 doi: 10.1007/s11465-022-0725-z

摘要: As parameter independent yet simple techniques, the energy operator (EO) and its variants have received considerable attention in the field of bearing fault feature detection. However, the performances of these improved EO techniques are subjected to the limited number of EOs, and they cannot reflect the non-linearity of the machinery dynamic systems and affect the noise reduction. As a result, the fault-related transients strengthened by these improved EO techniques are still subject to contamination of strong noises. To address these issues, this paper presents a novel EO fusion strategy for enhancing the bearing fault feature nonlinearly and effectively. Specifically, the proposed strategy is conducted through the following three steps. First, a multi-dimensional information matrix (MDIM) is constructed by performing the higher order energy operator (HOEO) on the analysis signal iteratively. MDIM is regarded as the fusion source of the proposed strategy with the properties of improving the signal-to-interference ratio and suppressing the noise in the low-frequency region. Second, an enhanced manifold learning algorithm is performed on the normalized MDIM to extract the intrinsic manifolds correlated with the fault-related impulses. Third, the intrinsic manifolds are weighted to recover the fault-related transients. Simulation studies and experimental verifications confirm that the proposed strategy is more effective for enhancing the bearing fault feature than the existing methods, including HOEOs, the weighting HOEO fusion, the fast Kurtogram, and the empirical mode decomposition.

关键词: higher order energy operator     fault diagnosis     manifold learning     rolling element bearing     information fusion    

Multi-model ensemble deep learning method for intelligent fault diagnosis with high-dimensional samples

Xin ZHANG, Tao HUANG, Bo WU, Youmin HU, Shuai HUANG, Quan ZHOU, Xi ZHANG

《机械工程前沿(英文)》 2021年 第16卷 第2期   页码 340-352 doi: 10.1007/s11465-021-0629-3

摘要: Deep learning has achieved much success in mechanical intelligent fault diagnosis in recent years. However, many deep learning methods cannot fully extract fault information to recognize mechanical health states when processing high-dimensional samples. Therefore, a multi-model ensemble deep learning method based on deep convolutional neural network (DCNN) is proposed in this study to accomplish fault recognition of high-dimensional samples. First, several 1D DCNN models with different activation functions are trained through dimension reduction learning to obtain different fault features from high-dimensional samples. Second, the obtained features are constructed into 2D images with multiple channels through a conversion method. The integrated 2D feature images can effectively represent the fault characteristic contained in raw high-dimension vibration signals. Lastly, a 2D DCNN model with multi-layer convolution and pooling is used to automatically learn features from the 2D images and identify the fault mode of the mechanical equipment by adopting a softmax classifier. The proposed method, which is validated using the bearing public dataset of Case Western Reserve University, USA and a one-stage reduction gearbox dataset, has high recognition accuracy. Compared with other classical deep learning methods, the proposed fault diagnosis method has considerable improvements.

关键词: fault intelligent diagnosis     deep learning     deep convolutional neural network     high-dimensional samples    

Method for solving the nonlinear inverse problem in gas face seal diagnosis based on surrogate models

《机械工程前沿(英文)》 2022年 第17卷 第3期 doi: 10.1007/s11465-022-0689-z

摘要: Physical models carry quantitative and explainable expert knowledge. However, they have not been introduced into gas face seal diagnosis tasks because of the unacceptable computational cost of inferring the input fault parameters for the observed output or solving the inverse problem of the physical model. The presented work develops a surrogate-model-assisted method for solving the nonlinear inverse problem in limited physical model evaluations. The method prepares a small initial database on sites generated with a Latin hypercube design and then performs an iterative routine that benefits from the rapidity of the surrogate models and the reliability of the physical model. The method is validated on simulated and experimental cases. Results demonstrate that the method can effectively identify the parameters that induce the abnormal signal output with limited physical model evaluations. The presented work provides a quantitative, explainable, and feasible approach for identifying the cause of gas face seal contact. It is also applicable to mechanical devices that face similar difficulties.

关键词: surrogate model     gas face seal     fault diagnosis     nonlinear dynamics     tribology    

Basic research on machinery fault diagnostics: Past, present, and future trends

Xuefeng CHEN, Shibin WANG, Baijie QIAO, Qiang CHEN

《机械工程前沿(英文)》 2018年 第13卷 第2期   页码 264-291 doi: 10.1007/s11465-018-0472-3

摘要:

Machinery fault diagnosis has progressed over the past decades with the evolution of machineries in terms of complexity and scale. High-value machineries require condition monitoring and fault diagnosis to guarantee their designed functions and performance throughout their lifetime. Research on machinery Fault diagnostics has grown rapidly in recent years. This paper attempts to summarize and review the recent R&D trends in the basic research field of machinery fault diagnosis in terms of four main aspects: Fault mechanism, sensor technique and signal acquisition, signal processing, and intelligent diagnostics. The review discusses the special contributions of Chinese scholars to machinery fault diagnostics. On the basis of the review of basic theory of machinery fault diagnosis and its practical applications in engineering, the paper concludes with a brief discussion on the future trends and challenges in machinery fault diagnosis.

关键词: fault diagnosis     fault mechanism     feature extraction     signal processing     intelligent diagnostics    

基于知识的卫星故障诊断与预测方法

杨天社,杨开忠,李怀祖

《中国工程科学》 2003年 第5卷 第6期   页码 63-67

摘要:

卫星结构的复杂性、运行环境的独特性和诱发故障的多源性,使得卫星故障的诊断与预测较一般设备困难。通常,一种形式的推理只能诊断和预测卫星的一类故障。文章提出了同时应用多种形式推理进行卫星故障诊断和预测的新方法,此方法已成功地应用于基于知识的卫星故障诊断与恢复系统的开发,并取得了显著的效果。

关键词: 卫星     故障     诊断     预测     多形式推理    

不确定性推理理论在卫星故障检测和诊断中的应用

杨天社,李怀祖,曹雨平

《中国工程科学》 2003年 第5卷 第2期   页码 68-74

摘要:

推理理论一般分为确定性推理理论和不确定性推理理论。传统的卫星故障检测和诊断应用的是确定性推理。然而,在卫星故障检测和诊断的实践中,仅使用确定性推理是很难对某些故障进行检测和诊断的,因为这时需要合情推理和容错能力。不确定性推理理论可以满足此要求。目前,航天领域的许多专家和实际工作者正致力于应用不确定性推理理论检测和诊断那些用确定性推理无法检测和诊断的故障。不确定性推理理论包括诸如包含度理论、粗糙集理论、证据推理理论、概率推理理论、模糊推理理论等。笔者研究的卫星故障检测和诊断的三种新方法,分别应用了包含度理论、粗糙集理论和证据推理理论。

关键词: 卫星     故障     检测     诊断     不确定性推理    

Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition

Diego CABRERA,Fernando SANCHO,René-Vinicio SÁNCHEZ,Grover ZURITA,Mariela CERRADA,Chuan LI,Rafael E. VÁSQUEZ

《机械工程前沿(英文)》 2015年 第10卷 第3期   页码 277-286 doi: 10.1007/s11465-015-0348-8

摘要:

This paper addresses the development of a random forest classifier for the multi-class fault diagnosis in spur gearboxes. The vibration signal’s condition parameters are first extracted by applying the wavelet packet decomposition with multiple mother wavelets, and the coefficients’ energy content for terminal nodes is used as the input feature for the classification problem. Then, a study through the parameters’ space to find the best values for the number of trees and the number of random features is performed. In this way, the best set of mother wavelets for the application is identified and the best features are selected through the internal ranking of the random forest classifier. The results show that the proposed method reached 98.68% in classification accuracy, and high efficiency and robustness in the models.

关键词: fault diagnosis     spur gearbox     wavelet packet decomposition     random forest    

标题 作者 时间 类型 操作

Machine learning for fault diagnosis of high-speed train traction systems: A review

期刊论文

Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical faultdiagnosis of bearings

期刊论文

Gear fault diagnosis using gear meshing stiffness identified by gearbox housing vibration signals

期刊论文

Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

期刊论文

New method of fault diagnosis of rotating machinery based on distance of information entropy

Houjun SU, Tielin SHI, Fei CHEN, Shuhong HUANG

期刊论文

A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

期刊论文

Fault diagnosis of axial piston pumps with multi-sensor data and convolutional neural network

期刊论文

大型火电机组的振动故障诊断

于文虎,宋斌

期刊论文

Iterative HOEO fusion strategy: a promising tool for enhancing bearing fault feature

期刊论文

Multi-model ensemble deep learning method for intelligent fault diagnosis with high-dimensional samples

Xin ZHANG, Tao HUANG, Bo WU, Youmin HU, Shuai HUANG, Quan ZHOU, Xi ZHANG

期刊论文

Method for solving the nonlinear inverse problem in gas face seal diagnosis based on surrogate models

期刊论文

Basic research on machinery fault diagnostics: Past, present, and future trends

Xuefeng CHEN, Shibin WANG, Baijie QIAO, Qiang CHEN

期刊论文

基于知识的卫星故障诊断与预测方法

杨天社,杨开忠,李怀祖

期刊论文

不确定性推理理论在卫星故障检测和诊断中的应用

杨天社,李怀祖,曹雨平

期刊论文

Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition

Diego CABRERA,Fernando SANCHO,René-Vinicio SÁNCHEZ,Grover ZURITA,Mariela CERRADA,Chuan LI,Rafael E. VÁSQUEZ

期刊论文