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An approach for mechanical fault classification based on generalized discriminant analysis

LI Wei-hua, SHI Tie-lin, YANG Shu-zi

《机械工程前沿（英文）》 2006年 第1卷 第3期   页码 292-298 doi: 10.1007/s11465-006-0022-2

摘要： To deal with pattern classification of complicated mechanical faults, an approach to multi-faults classification based on generalized discriminant analysis is presented. Compared with linear discriminant analysis (LDA), generalized discriminant analysis (GDA), one of nonlinear discriminant analysis methods, is more suitable for classifying the linear non-separable problem. The connection and difference between KPCA (Kernel Principal Component Analysis) and GDA is discussed. KPCA is good at detection of machine abnormality while GDA performs well in multi-faults classification based on the collection of historical faults symptoms. When the proposed method is applied to air compressor condition classification and gear fault classification, an excellent performance in complicated multi-faults classification is presented.

关键词： generalized discriminant     non-separable     abnormality     classification     multi-faults classification    

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Fault classification and reconfiguration of distribution systems using equivalent capacity margin method

K. Sathish KUMAR, T. JAYABARATHI

《能源前沿（英文）》 2012年 第6卷 第4期   页码 394-402 doi: 10.1007/s11708-012-0211-0

摘要： This paper investigates the capability of support vector machines (SVM) for prediction of fault classification and the use of the concept of equivalent capacity margin (ECM) for restoration of the power system. The SVM, as a novel type of machine learning based on statistical learning theory, achieves good generalization ability by adopting a structural risk minimization (SRM) induction principle aimed at minimizing a bound on the generalization error of a model rather than the minimization of the error on the training data only. Here, the SVM has been used as a classification. The inputs of the SVM model are power and voltage values. An equation has been developed for the prediction of the fault in the power system based on the developed SVM model. The next steps of this paper are the restoration and reconfiguration by using the ECM concept, the development of a code, and the testing of the results with various load outages, which have been executed for a 12 load system.

关键词： support vector machines (SVM)     structural risk minimization (SRM)     equivalent capacity margin (ECM)     restoration     fault classification    

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Unknown fault detection for EGT multi-temperature signals based on self-supervised feature learning andunary classification

《能源前沿（英文）》 2023年 第17卷 第4期   页码 527-544 doi: 10.1007/s11708-023-0880-x

摘要： Intelligent power systems can improve operational efficiency by installing a large number of sensors. Data-based methods of supervised learning have gained popularity because of available Big Data and computing resources. However, the common paradigm of the loss function in supervised learning requires large amounts of labeled data and cannot process unlabeled data. The scarcity of fault data and a large amount of normal data in practical use pose great challenges to fault detection algorithms. Moreover, sensor data faults in power systems are dynamically changing and pose another challenge. Therefore, a fault detection method based on self-supervised feature learning was proposed to address the above two challenges. First, self-supervised learning was employed to extract features under various working conditions only using large amounts of normal data. The self-supervised representation learning uses a sequence-based Triplet Loss. The extracted features of large amounts of normal data are then fed into a unary classifier. The proposed method is validated on exhaust gas temperatures (EGTs) of a real-world 9F gas turbine with sudden, progressive, and hybrid faults. A comprehensive comparison study was also conducted with various feature extractors and unary classifiers. The results show that the proposed method can achieve a relatively high recall for all kinds of typical faults. The model can detect progressive faults very quickly and achieve improved results for comparison without feature extractors in terms of F1 score.

关键词： fault detection     unary classification     self-supervised representation learning     multivariate nonlinear time series    

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针对工业故障分类系统的单变量攻击及其防御 Article

卓越, Yuri A.W. Shardt, 葛志强

《工程（英文）》 2022年 第19卷 第12期   页码 240-251 doi: 10.1016/j.eng.2021.07.033

摘要：

近年来，工业过程故障分类系统主要是由数据驱动的，得益于大量的数据模式，基于深度神经网络的模型显著地提高了故障分类的准确性。但是，这些数据驱动模型容易受到对抗攻击，因此，在样本上的微小扰动会导致模型提供错误的故障预测。最近的研究已经证明了机器学习模型的脆弱性以及对抗样本的广泛存在。本文针对安全、关键的工业故障分类系统提出了一种具有极端约束的黑盒攻击方法：只扰动一个变量来制作对抗样本。此外，为了将对抗样本隐藏在可视化空间中，本文使用了雅可比矩阵来引导扰动变量的选择，使降维空间中的对抗样本对人眼不可见。利用单变量攻击（OVA）方法，文本探究了不同工业变量和故障类别的脆弱性，有助于理解故障分类系统的几何特征。基于攻击方法，文本还提出了相应的对抗训练防御方法，该方法能够有效地防御单变量攻击，并提高分类器的预测精度。在实验中，将本文所提出的方法在田纳西-伊士曼过程（TEP）和钢板（SP）故障数据集上进行了测试。本文探索了变量和故障类别的脆弱相关性，并验证了各种分类器和数据集的单变量攻击和防御方法的有效性。对于工业故障分类系统，单变量攻击方法的攻击成功率接近（在TEP上）甚至高于（在SP 上）目前最有效的一阶白盒攻击方法（该方法需要对所有变量进行扰动）。

关键词： 对抗样本     黑盒攻击     工业数据安全     故障分类系统    

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集成增强主动学习混合判别分析模型及其在半监督故障分类中的应用 Research Article

王伟俊1,王云2,王君1,方信昀3,何雨辰1

《信息与电子工程前沿（英文）》 2022年 第23卷 第12期   页码 1814-1827 doi: 10.1631/FITEE.2200053

摘要： 故障分类作为过程监控中不可缺少的部分，其性能高度依赖于过程知识的充分性。然而，由于采样条件有限及实验室分析昂贵，数据标签总是难以获取，这可能导致分类性能下降。为了解决这个难题，本文提出一种新的半监督故障分类方法，其中每个未标记样本相对于特定标记数据集的价值采用增强的主动学习来评估。具有高价值的未标记样本将作为训练数据集的补充信息。此外，引入了几个合理的指标和准则大大降低了人工标注的干扰。最后，通过数值例子和田纳西伊士曼过程（TEP）评估了该方法的故障分类有效性。

关键词： 半监督；主动学习；集成学习；混合判别分析；故障分类    

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Development of a new method for RMR and Q classification method to optimize support system in tunneling

Asghar RAHMATI,Lohrasb FARAMARZI,Manouchehr SANEI

《结构与土木工程前沿（英文）》 2014年 第8卷 第4期   页码 448-455 doi: 10.1007/s11709-014-0262-x

摘要： Rock mass classification system is very suitable for various engineering design and stability analysis. classification method is confirmed by Japan Highway Public Corporation that this method can figure out either strength or deformability of rock mass, further appropriating the amount of rock bolts, thickness of shotcrete, and size of pitch of steel ribs just after the blasting procedure. Based on these advantages of method, in this study, according to data of five deep and long tunnels in Iran, two equations for estimating the value of method from and classification systems were developed. These equations as a new method were able to optimize the support system for and classification systems. From classification and its application in these case studies, it is pointed out that the method for the design of support systems in underground working is more reliable than the and classification systems.

关键词： JH classification     Q and RMR classification     new method    

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Molecular classification and precision therapy of cancer: immune checkpoint inhibitors

null

《医学前沿（英文）》 2018年 第12卷 第2期   页码 229-235 doi: 10.1007/s11684-017-0581-0

摘要：

On May 23, 2017, the US Food and Drug Administration (FDA) approved a treatment for cancer patients with positive microsatellite instability-high (MSI-H) markers or mismatch repair deficient (dMMR) markers. This approach is the first approved tumor treatment using a common biomarker rather than specified tumor locations in the body. FDA previously approved Keytruda for treatment of several types of malignancies, such as metastatic melanoma, metastatic non-small-cell lung cancer, recurrent or metastatic head and neck cancer, refractory Hodgkin lymphoma, and urothelial carcinoma, all of which carry positive programmed death-1/programmed death-ligand 1 biomarkers. Therefore, indications of Keytruda significantly expanded. Several types of malignancies are disclosed by MSI-H status due to dMMR and characterized by increased neoantigen load, which elicits intense host immune response in tumor microenvironment, including portions of colorectal and gastric carcinomas. Currently, biomarker-based patient selection remains a challenge. Pathologists play important roles in evaluating histology and biomarker results and establishing detection methods. Taking gastric cancer as an example, its molecular classification is built on genome abnormalities, but it lacks acceptable clinical characteristics. Pathologists are expected to act as “genetic interpreters” or “genetic translators” and build a link between molecular subtypes with tumor histological features. Subsequently, by using their findings, oncologists will carry out targeted therapy based on molecular classification.

关键词： molecular classification     precision medicine     pembrolizumab     PD-1/PD-L1     MSI-H    

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Three-dimensional finite difference analysis of shallow sprayed concrete tunnels crossing a reverse faultor a normal fault: A parametric study

Masoud RANJBARNIA, Milad ZAHERI, Daniel DIAS

《结构与土木工程前沿（英文）》 2020年 第14卷 第4期   页码 998-1011 doi: 10.1007/s11709-020-0621-8

摘要： Urban tunnels crossing faults are always at the risk of severe damages. In this paper, the effects of a reverse and a normal fault movement on a transversely crossing shallow shotcreted tunnel are investigated by 3D finite difference analysis. After verifying the accuracy of the numerical simulation predictions with the centrifuge physical model results, a parametric study is then conducted. That is, the effects of various parameters such as the sprayed concrete thickness, the geo-mechanical properties of soil, the tunnel depth, and the fault plane dip angle are studied on the displacements of the ground surface and the tunnel structure, and on the plastic strains of the soil mass around tunnel. The results of each case of reverse and normal faulting are independently discussed and then compared with each other. It is obtained that deeper tunnels show greater displacements for both types of faulting.

关键词： urban tunnel     sprayed concrete     reverse fault     normal fault     finite difference analysis    

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EAI-oriented information classification code system in manufacturing enterprises

WANG Junbiao, DENG Hu, JIANG Jianjun, YANG Binghong, WANG Bailing

《机械工程前沿（英文）》 2008年 第3卷 第1期   页码 81-85 doi: 10.1007/s11465-008-0011-8

摘要： Although the traditional information classification coding system in manufacturing enterprises (MEs) emphasizes the construction of code standards, it lacks the management of the code creation, code data transmission and so on. According to the demands of enterprise application integration (EAI) in manufacturing enterprises, an enterprise application integration oriented information classification code system (EAIO-ICCS) is proposed. EAIO-ICCS expands the connotation of the information classification code system and assures the identity of the codes in manufacturing enterprises with unified management of codes at the view of its lifecycle.

关键词： EAI     EAIO-ICCS     management     classification     connotation    

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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    

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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）》 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    

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Acoustic fault signal extraction via the line-defect phononic crystals

《机械工程前沿（英文）》 2022年 第17卷 第1期   页码 10-10 doi: 10.1007/s11465-021-0666-y

摘要： Rotating machine fault signal extraction becomes increasingly important in practical engineering applications. However, fault signals with low signal-to-noise ratios (SNRs) are difficult to extract, especially at the early stage of fault diagnosis. In this paper, 2D line-defect phononic crystals (PCs) consisting of periodic acrylic tubes with slit are proposed for weak signal detection. The defect band, namely, the formed resonance band of line-defect PCs enables the incident acoustic wave at the resonance frequency to be trapped and enhanced at the resonance cavity. The noise can be filtered by the band gap. As a result, fault signals with high SNRs can be obtained for fault feature extraction. The effectiveness of weak harmonic and periodic impulse signal detection via line-defect PCs are investigated in numerical and experimental studies. All the numerical and experimental results indicate that line-defect PCs can be well used for extracting weak harmonic and periodic impulse signals. This work will provide potential for extracting weak signals in many practical engineering applications.

关键词： phononic crystals     line-defect     fault signal extraction     acoustic enhancement    

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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    

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Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical fault

《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    

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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    

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标题 作者 时间 类型 操作

An approach for mechanical fault classification based on generalized discriminant analysis

LI Wei-hua, SHI Tie-lin, YANG Shu-zi

期刊论文

Fault classification and reconfiguration of distribution systems using equivalent capacity margin method

K. Sathish KUMAR, T. JAYABARATHI

期刊论文

Unknown fault detection for EGT multi-temperature signals based on self-supervised feature learning andunary classification

期刊论文

针对工业故障分类系统的单变量攻击及其防御

卓越, Yuri A.W. Shardt, 葛志强

期刊论文

集成增强主动学习混合判别分析模型及其在半监督故障分类中的应用

王伟俊1,王云2,王君1,方信昀3,何雨辰1

期刊论文

Development of a new method for RMR and Q classification method to optimize support system in tunneling

Asghar RAHMATI,Lohrasb FARAMARZI,Manouchehr SANEI

期刊论文

Molecular classification and precision therapy of cancer: immune checkpoint inhibitors

null

期刊论文

Three-dimensional finite difference analysis of shallow sprayed concrete tunnels crossing a reverse faultor a normal fault: A parametric study

Masoud RANJBARNIA, Milad ZAHERI, Daniel DIAS

期刊论文

EAI-oriented information classification code system in manufacturing enterprises

WANG Junbiao, DENG Hu, JIANG Jianjun, YANG Binghong, WANG Bailing

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

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

期刊论文

Acoustic fault signal extraction via the line-defect phononic crystals

期刊论文

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

期刊论文

Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical fault

期刊论文

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

期刊论文