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fault diagnosis of HVAC system with imbalanced data using multi-scale convolution composite neural network

Building Simulation 2024年第3期17卷 371-386页

作者： Rouhui Wu Yizhu Ren Mengying Tan Lei Nie School of Mechanical Engineering Hubei University of TechnologyWuhan430068China Industrial Research Institute of Xiangyang Hubei University of Technology Xiangyang441100China

Accurate fault diagnosis of heating,ventilation,and air conditioning(HVAC)systems is of significant importance for maintaining normal operation,reducing energy consumption,and minimizing maintenance costs.However,in practical applications,it is challenging to obtain sufficient fault data for HVAC systems,leading to imbalanced data,where the number of fault samples is much smaller than that of normal samples.Moreover,most existing HVAC system fault diagnosis methods heavily rely on balanced training sets to achieve high fault diagnosis accuracy.Therefore,to address this issue,a composite neural network fault diagnosis model is proposed,which combines SMOTETomek,multi-scale one-dimensional convolutional neural networks(M1DCNN),and support vector machine(SVM).This method first utilizes SMOTETomek to augment the minority class samples in the imbalanced dataset,achieving a balanced number of faulty and normal data.Then,it employs the M1DCNN model to extract feature information from the augmented dataset.Finally,it replaces the original Softmax classifier with an SVM classifier for classification,thus enhancing the fault diagnosis accuracy.Using the SMOTETomek-M1DCNN-SVM method,we conducted fault diagnosis validation on both the ASHRAE RP-1043 dataset and experimental dataset with an imbalance ratio of 1:10.The results demonstrate the superiority of this approach,providing a novel and promising solution for intelligent building management,with accuracy and F1 scores of 98.45%and 100%for the RP-1043 dataset and experimental dataset,respectively.

关键词： fault diagnosis chiller imbalanced data SMOTETomek multi-scale neural networks

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fault diagnosis Method of Rolling Bearing Based on ESGMD-CC and AFSA-ELM

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Structural Durability & Health Monitoring 2024年第1期18卷 37-54页

作者： Jiajie He Fuzheng Liu Xiangyi Geng Xifeng Liang Faye Zhang Mingshun Jiang School of Traffic&Transportation Engineering Central South UniversityChangsha410075China Project Management Department CRRC Advanced Composites Co.Ltd.Qingdao266108China School of Control Science and Engineering Shandong UniversityJinan250061China

Incomplete fault signal characteristics and ease of noise contamination are issues with the current rolling bearing early fault diagnostic methods,making it challenging to ensure the fault diagnosis accuracy and reliability.A novel approach integrating enhanced Symplectic geometry mode decomposition with cosine difference limitation and calculus operator(ESGMD-CC)and artificial fish swarm algorithm(AFSA)optimized extreme learning machine(ELM)is proposed in this paper to enhance the extraction capability of fault features and thus improve the accuracy of fault diagnosis.Firstly,SGMD decomposes the raw vibration signal into multiple Symplectic geometry components(SGCs).Secondly,the iterations are reset by the cosine difference limitation to effectively separate the redundant components from the representative components.Additionally,the calculus operator is performed to strengthen weak fault features and make them easier to extract,and the singular value decomposition(SVD)weighted by power spectrum entropy(PSE)can be utilized as the sample feature representation.Finally,AFSA iteratively optimized ELM is adopted as the optimized classifier for fault identification.The superior performance of the proposed method has been validated by various experiments.

关键词： Symplectic geometry mode decomposition calculus operator cosine difference limitation fault diagnosis AFSAELM model

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Intelligent fault diagnosis methods toward gas turbine: A review

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Chinese Journal of Aeronautics 2024年第4期37卷 93-120页

作者： Xiaofeng LIU Yingjie CHEN Liuqi XIONG Jianhua WANG Chenshuang LUO Liming ZHANG Kehuan WANG School of Transportation Science and Engineering Beihang UniversityBeijing 100191China Systems Engineering Research Institute China State Shipbuilding CorporationBeijing 100094China

fault diagnosis plays a significant role in conducting condition-based maintenance and health management for gas turbines(GTs) to improve reliability and reduce costs. Various diagnosis methods developed by modeling engine systems or certain components implement faults detection and diagnosis based on the measurement of systemic parameters deviations. However, these conventional model-based methods are hindered by limitations of inability to handle the nonlinear nature, measurement uncertainty, fault coupling and other implementing problems. Recently, the development of artificial intelligence algorithms has provided an effective solution to the above problems, triggering broad researches for data-driven fault diagnosis methods with better accuracy,dynamic performance, and universality. This paper presents a systematic review of recently proposed intelligent fault diagnosis methods for GT engines, according to the classification of shallow learning methods, deep learning methods and hybrid intelligent methods. Moreover, the principle of typical algorithms, the evolution of enhanced methods, and the assessment of pros and cons are summarized to conclude the present status and look forward to the future in the field of GT fault diagnosis. Possible directions for development in method validation, information fusion, and interpretability of intelligent diagnosis methods are concluded in the end to provide insightful concepts for scholars in related fields.

关键词： fault diagnosis Health management Gas turbine Artificial intelligence Intelligent diagnosis method

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Complementary-Label Adversarial Domain Adaptation fault diagnosis Network under Time-Varying Rotational Speed and Weakly-Supervised Conditions

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Computers, Materials & Continua 2024年第4期79卷 761-777页

作者： Siyuan Liu Jinying Huang Jiancheng Ma Licheng Jing Yuxuan Wang School of Data Science and Technology North University of ChinaTaiyuan030051China School of Mechanical Engineering North University of ChinaTaiyuan030051China

Recent research in cross-domain intelligence fault diagnosis of machinery still has some problems,such as relatively ideal speed conditions and sample conditions.In engineering practice,the rotational speed of the machine is often transient and time-varying,which makes the sample annotation increasingly expensive.Meanwhile,the number of samples collected from different health states is often unbalanced.To deal with the above challenges,a complementary-label(CL)adversarial domain adaptation fault diagnosis network(CLADAN)is proposed under time-varying rotational speed and weakly-supervised conditions.In the weakly supervised learning condition,machine prior information is used for sample annotation via cost-friendly complementary label learning.A diagnosticmodel learning strategywith discretized category probabilities is designed to avoidmulti-peak distribution of prediction results.In adversarial training process,we developed virtual adversarial regularization(VAR)strategy,which further enhances the robustness of the model by adding adversarial perturbations in the target domain.Comparative experiments on two case studies validated the superior performance of the proposed method.

关键词： Time-varying rotational speed weakly-supervised fault diagnosis domain adaptation

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Vibration-Based fault diagnosis for Railway Point Machines Using VMD and Multiscale Fluctuation-Based Dispersion Entropy

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Chinese Journal of Electronics 2024年第3期33卷 803-813页

作者： Yongkui SUN Yuan CAO Peng LI Guo XIE Tao WEN Shuai SU National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University School of Electronic and Information Engineering Beijing Jiaotong University Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University

As one of the most important railway signaling equipment,railway point machines undertake the major task of ensuring train operation safety.Thus fault diagnosis for railway point machines becomes a hot topic.Considering the advantage of the anti-interference characteristics of vibration signals,this paper proposes an novel intelligent fault diagnosis method for railway point machines based on vibration signals.A feature extraction method combining variational mode decomposition(VMD) and multiscale fluctuation-based dispersion entropy is developed,which is verified a more effective tool for feature selection.Then,a two-stage feature selection method based on Fisher discrimination and ReliefF is proposed,which is validated more powerful than single feature selection methods.Finally,support vector machine is utilized for fault diagnosis.Experiment comparisons show that the proposed method performs best.The diagnosis accuracies of normal-reverse and reverse-normal switching processes reach 100% and 96.57% respectively.Especially,it is a try to use new means for fault diagnosis on railway point machines,which can also provide references for similar fields.

关键词： fault diagnosis Vibrations Support vector machines Diversity reception Switches Feature extraction Rail transportation

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Multi-Time-Scale Variational Mode Decomposition-Based Robust fault diagnosis of Railway Point Machines Under Multiple Noises

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Chinese Journal of Electronics 2024年第3期33卷 814-822页

作者： Junqi LIU Tao WEN Guo XIE Yuan CAO Clive Roberts Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology The School of Electronic and Information Engineering Beijing Jiaotong University Birmingham Centre for Railway Research and Education University of Birmingham

The fault diagnosis of railway point machines(RPMs) has attracted the attention of engineers and researchers.Seldom have studies considered diverse noises along the track.To fulfill this aspect,a multi-time-scale variational mode decomposition(MTSVMD) is proposed in this paper to realize the accurate and robust fault diagnosis of RPMs under multiple noises.MTSVMD decomposes condition monitoring signals after coarse-grained processing in varying degrees.In this manner,the information contained in the signal components at multiple time scales can construct a more abundant feature space than at a single scale.In the experimental validation,a random position,random type,random number,and random length(4R) noise-adding algorithm helps to verify the robustness of the approach.The adequate experimental results demoristrate the superiority of the proposed MTSVMD-based fault diagnosis.

关键词： fault diagnosis Condition monitoring Noise Signal processing algorithms Aerospace electronics Rail transportation Robustness

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Intelligent fault diagnosis Method of Rolling Bearings Based on Transfer Residual Swin Transformer with Shifted Windows

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Structural Durability & Health Monitoring 2024年第2期18卷 91-110页

作者： Haomiao Wang Jinxi Wang Qingmei Sui Faye Zhang Yibin Li Mingshun Jiang Phanasindh Paitekul The Institute of Marine Science and Technology Shandong UniversityQingdao26623China The School of Control Sciences and Engineering Shandong UniversityJinan250061China Thailand Institute of Scientific and Technological Research Amphoe Khlong LuangPathum Thani12120Thailand

Due to their robust learning and expression ability for complex features,the deep learning(DL)model plays a vital role in bearing fault diagnosis.However,since there are fewer labeled samples in fault diagnosis,the depth of DL models in fault diagnosis is generally shallower than that of DL models in other fields,which limits the diagnostic performance.To solve this problem,a novel transfer residual Swin Transformer(RST)is proposed for rolling bearings in this paper.RST has 24 residual self-attention layers,which use the hierarchical design and the shifted window-based residual self-attention.Combined with transfer learning techniques,the transfer RST model uses pre-trained parameters from ImageNet.A new end-to-end method for fault diagnosis based on deep transfer RST is proposed.Firstly,wavelet transform transforms the vibration signal into a wavelet time-frequency diagram.The signal’s time-frequency domain representation can be represented simultaneously.Secondly,the wavelet time-frequency diagram is the input of the RST model to obtain the fault type.Finally,our method is verified on public and self-built datasets.Experimental results show the superior performance of our method by comparing it with a shallow neural network.

关键词： Rolling bearing fault diagnosis transformer self-attention mechanism

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

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Frontiers of Engineering Management 2024年第1期11卷 62-78页

作者： Huan WANG Yan-Fu LI Jianliang REN Department of Industrial Engineering Tsinghua UniversityBeijing 100084China Zhibo Lucchini Railway Equipment Co. Ltd.Taiyuan 030032China

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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A fault diagnosis Method for Power Systems Based on Temporal Tissue-like P Systems

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Protection and Control of Modern Power Systems 2024年第2期9卷 101-114页

作者： Kequan Zhou Tao Wang Xiaotian Chen Quanlin Leng Panzhihua University PanzhihuaChina School of Electrical Engineering and Electronic Information Xihua UniversityChengdu 610039China Key Laboratory of Fluid and Power Machinery Ministry of EducationXihua UniversityChengdu 610039China Sichuan University of Science&Engineering YibinChina Xihua University ChengduChina

To quickly and accurately identify faulty components based on the alarm information is critical for the fault diagnosis of power grids.To address this chal-lenge,this paper proposes a novel fault diagnosis method based on temporal tissue-like P system(TTPS).In the proposed method,suspected faulty components are iden-tiﬁedﬁrst via a network topology analysis method.An TTPS-based fault diagnosis model is then built for each suspected faulty component to perform fault reasoning,so as to accurately detect the faulty components.To take full advantage of the action signals and temporal information of protection devices,TTPS and its forward temporal reasoning algorithm are proposed.TTPS can synchro-nously model the action and temporal logics of protection devices in an intuitive and graphical way,while the rea-soning algorithm can process the fault alarm information in parallel.To demonstrate the effectiveness and superi-ority of the proposed method,simulations are carried out on the IEEE 14-bus and 118-bus systems,while the results are compared to other two widely adopted methods.Index Terms—Alarm signal,fault diagnosis,membrane computing,power system,tissue-like P system.

关键词： Alarm signal fault diagnosis membrane computing power system tissue-like P system

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Bearing fault diagnosis Based on Deep Discriminative Adversarial Domain Adaptation Neural Networks

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 2619-2640页

作者： Jinxi Guo Kai Chen Jiehui Liu Yuhao Ma Jie Wu Yaochun Wu Xiaofeng Xue Jianshen Li School of Mechanical and Equipment Engineering Hebei University of EngineeringHandan056000China College of Mechanical Engineering Anyang Institute of TechnologyAnyang455000China Department of Mechanical and Electrical Engineering Yuncheng UniversityYuncheng044000China

Intelligent diagnosis driven by big data for mechanical fault is an important means to ensure the safe operation ofequipment. In these methods, deep learning-based machinery fault diagnosis approaches have received increasingattention and achieved some results. It might lead to insufficient performance for using transfer learning alone andcause misclassification of target samples for domain bias when building deep models to learn domain-invariantfeatures. To address the above problems, a deep discriminative adversarial domain adaptation neural networkfor the bearing fault diagnosis model is proposed (DDADAN). In this method, the raw vibration data are firstlyconverted into frequency domain data by Fast Fourier Transform, and an improved deep convolutional neuralnetwork with wide first-layer kernels is used as a feature extractor to extract deep fault features. Then, domaininvariant features are learned from the fault data with correlation alignment-based domain adversarial training.Furthermore, to enhance the discriminative property of features, discriminative feature learning is embeddedinto this network to make the features compact, as well as separable between classes within the class. Finally, theperformance and anti-noise capability of the proposedmethod are evaluated using two sets of bearing fault datasets.The results demonstrate that the proposed method is capable of handling domain offset caused by differentworkingconditions and maintaining more than 97.53% accuracy on various transfer tasks. Furthermore, the proposedmethod can achieve high diagnostic accuracy under varying noise levels.

关键词： fault diagnosis transfer learning domain adaptation discriminative feature learning correlation alignment

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