The accurate identification and diagnosis of chemical process faults are crucial for ensuring the safe and stable operation of production plants.The current hot topic in industrial process fault diagnosis research is ...The accurate identification and diagnosis of chemical process faults are crucial for ensuring the safe and stable operation of production plants.The current hot topic in industrial process fault diagnosis research is data-driven methods.Most of the existing fault diagnosis methods focus on a single shallow or deep learning model.This paper proposes a novel hybrid fault diagnosis method to fully utilize various features to improve the accuracy of fault diagnosis.Furthermore,the method addresses the issue of incomplete data,which has been largely overlooked in the majority of existing research.Firstly,the variable data is effectively fitted using orthogonal non-negative matrix tri-factorization,and the missing data in the matrix is solved to construct a complete production condition relationship.Next,the support vector machine model and the deep residual contraction network model are trained in parallel to prediagnose process faults by mining linear and non-linear interaction features.Finally,a novel mapping relationship is established between the result and model levels using the multi-layer perceptron algorithm to complete the final diagnosis and evaluation of the fault.To demonstrate the effectiveness of the proposed method,we conducted extensive comparative experiments on the Tennessee Eastman dataset and the ethylene plant cracking unit dataset.The experimental results show that the method has advantages in different evaluation metrics.展开更多
The Erlian fault basin group, a typical Basin and Range type fault basin group, was formed during Late Jurassic to Early Cretaceous, in which there are rich coal, oil and gas resources. In the present paper the abund...The Erlian fault basin group, a typical Basin and Range type fault basin group, was formed during Late Jurassic to Early Cretaceous, in which there are rich coal, oil and gas resources. In the present paper the abundant geological and petroleum information accumulated in process of industry oil and gas exploration and development of the Erlian basin group is comprehensively analyzed, the structures related to formation of basin are systematically studied, and the complete extensional tectonic system of this basin under conditions of wide rift setting and low extensional ratio is revealed by contrasting study with Basin and Range Province of the western America. Based on the above studies and achievements of the former workers, the deep background of the basin development is treated.展开更多
Distillation is the most widely used operation for liquid mixture separation in the chemical industry. It is of great importance to detect and diagnose faults in distillation process. Due to the strong feedback and co...Distillation is the most widely used operation for liquid mixture separation in the chemical industry. It is of great importance to detect and diagnose faults in distillation process. Due to the strong feedback and coupling of processes in a distillation column, it is difficult to use deep auto-encoders(DAEs) alone to achieve good results in detecting and diagnosing faults, in terms of accuracy and efficiency. This paper proposes a hybrid fault-diagnosis model based on convolutional neural networks(CNNs) and DAEs, by integrating the powerful capability of CNN in feature extraction and of DAE in classification. A case study was carried out with the distillation process of depropanization. It is shown that the proposed hybrid model is of good performance compared to other models, in terms of the accuracy of fault detection in such a process. Also, with the increase of structural layers of the CNN–DAE model, the diagnostic accuracy will be improved, with an optimal accuracy of 92.2%.展开更多
Deep learning(DL) is progressively popular as a viable alternative to traditional signal processing(SP) based methods for fault diagnosis. However, the lack of explainability makes DL-based fault diagnosis methods dif...Deep learning(DL) is progressively popular as a viable alternative to traditional signal processing(SP) based methods for fault diagnosis. However, the lack of explainability makes DL-based fault diagnosis methods difficult to be trusted and understood by industrial users. In addition, the extraction of weak fault features from signals with heavy noise is imperative in industrial applications. To address these limitations, inspired by the Filterbank-Feature-Decision methodology, we propose a new Signal Processing Informed Neural Network(SPINN) framework by embedding SP knowledge into the DL model. As one of the practical implementations for SPINN, a denoising fault-aware wavelet network(DFAWNet) is developed, which consists of fused wavelet convolution(FWConv), dynamic hard thresholding(DHT),index-based soft filtering(ISF), and a classifier. Taking advantage of wavelet transform, FWConv extracts multiscale features while learning wavelet scales and selecting important wavelet bases automatically;DHT dynamically eliminates noise-related components via point-wise hard thresholding;inspired by index-based filtering, ISF optimizes and selects optimal filters for diagnostic feature extraction. It’s worth noting that SPINN may be readily applied to different deep learning networks by simply adding filterbank and feature modules in front. Experiments results demonstrate a significant diagnostic performance improvement over other explainable or denoising deep learning networks. The corresponding code is available at https://github. com/alber tszg/DFAWn et.展开更多
For the tunnel crossing active fault,the damage induced by fault movement is always serious.To solve such a problem,a detailed anti-faulting tunnel design process for Urumqi subway line 2 was introduced,and seven thre...For the tunnel crossing active fault,the damage induced by fault movement is always serious.To solve such a problem,a detailed anti-faulting tunnel design process for Urumqi subway line 2 was introduced,and seven three-dimensional elastic-plastic finite element models were established.The anti-faulting design process included three steps.First,the damage of tunnel lining from different locations of fault rupture surfaces was analyzed.Then,the analysis of the effect on tunnel buried depth was given.Finally,the effect of the disaster mitigation method on the flexible joint was verified and the location of the flexible joint was discussed.The results show that when the properties of surrounding rock at the tunnel bottom grows soft,the tunnel deformation curve is smoother and tunnel damage induced by fault movement is less serious.The vertical displacement change ratio of secondary linings along the tunnel axis may be the main factor to cause shear damage to the tunnel.The interface between the hanging wall and fracture zone is defined as the most adverse fault rupture surface.The tunnel damage was reduced with the decrease in the tunnel buried depth as more energy was dissipated by overburden soil and the differential uplift zone of soil became more diffuse.The method of the flexible joint can reduce the tunnel damage significantly and the disaster mitigation effect of different locations on the flexible joint is different.The tunnel damage is reduced by the greatest degree when the flexible joint is located on the fault rupture surface.展开更多
Identification of abnormal conditions is essential in the chemical process.With the rapid development of artificial intelligence technology,deep learning has attracted a lot of attention as a promising fault identific...Identification of abnormal conditions is essential in the chemical process.With the rapid development of artificial intelligence technology,deep learning has attracted a lot of attention as a promising fault identification method in chemical process recently.In the high-dimensional data identification using deep neural networks,problems such as insufficient data and missing data,measurement noise,redundant variables,and high coupling of data are often encountered.To tackle these problems,a feature based deep belief networks(DBN)method is proposed in this paper.First,a generative adversarial network(GAN)is used to reconstruct the random and non-random missing data of chemical process.Second,the feature variables are selected by Spearman’s rank correlation coefficient(SRCC)from high-dimensional data to eliminate the noise and redundant variables and,as a consequence,compress data dimension of chemical process.Finally,the feature filtered data is deeply abstracted,learned and tuned by DBN for multi-case fault identification.The application in the Tennessee Eastman(TE)process demonstrates the fast convergence and high accuracy of this proposal in identifying abnormal conditions for chemical process,compared with the traditional fault identification algorithms.展开更多
This paper proposes a novel locally linear backpropagation based contribution(LLBBC) for nonlinear process fault diagnosis. As a method based on the deep learning model of auto-encoder(AE), LLBBC can deal with the fau...This paper proposes a novel locally linear backpropagation based contribution(LLBBC) for nonlinear process fault diagnosis. As a method based on the deep learning model of auto-encoder(AE), LLBBC can deal with the fault diagnosis problem through extracting nonlinear features. When the on-line fault diagnosis task is in progress, a locally linear model is firstly built at the current fault sample. According to the basic idea of reconstruction based contribution(RBC), the propagation of fault information is described by using back-propagation(BP) algorithm. Then, a contribution index is established to measure the correlation between the variable and the fault, and the final diagnosis result is obtained by searching variables with large contributions. The smearing effect, which is an important factor affecting the performance of fault diagnosis, can be suppressed as well,and the theoretical analysis reveals that the correct diagnosis can be guaranteed by LLBBC. Finally, the feasibility and effectiveness of the proposed method are verified through a nonlinear numerical example and the Tennessee Eastman benchmark process.展开更多
Owing to the 4.0 industrial revolution condition monitoring maintenance is widely accepted as a useful approach to avoiding plant disturbances and shutdown.Recently,Motor Current Signature Analysis(MCSA)is widely repo...Owing to the 4.0 industrial revolution condition monitoring maintenance is widely accepted as a useful approach to avoiding plant disturbances and shutdown.Recently,Motor Current Signature Analysis(MCSA)is widely reported as a condition monitoring technique in the detection and identification of individual andmultiple Induction Motor(IM)faults.However,checking the fault detection and classification with deep learning models and its comparison among them selves or conventional approaches is rarely reported in the literature.Therefore,in this work,wepresent the detection and identification of induction motor faults with MCSA and three Deep Learning(DL)models namely MLP,LSTM,and 1D-CNN.Initially,we have developed the model of Squirrel Cage induction motor in MATLAB and simulated it for single phasing and stator winding faults(SWF)using Fast Fourier Transform(FFT),Short Time Fourier Transform(STFT),and Continuous Wavelet Transform(CWT)to detect and identify the healthy and unhealthy conditions with phase to ground,single phasing and in multiple fault conditions using Motor Current Signature Analysis.The faults impact on stator current is presented in the time and frequency domain(i.e.,power spectrum).The simulation results show that the scalogram has shown good results in time-frequency analysis for fault and showing its impact on the energy of current during individual fault and multiple fault conditions.This is further investigated with three deep learning models(i.e.,MLP,LSTM,and 1D-CNN)for checking the fault detection and identification(i.e.,classification)improvement in a three-phase induction motor.By simulating the three-phase induction motor in various healthy and unhealthy conditions in MATLAB,we have collected current signature data in the time domain,labeled them accordingly and created the 50 thousand samples dataset for DL models.All the DL models are trained and validated with a suitable number of architecture layers.By simulation,the multiclass confusion matrix,precision,recall,and F1-score are obtained in several conditions.The result shows that the stator current signature of the motor can be used to detect individual and multiple faults.Moreover,deep learning models can efficiently classify the induction motor faults based on time-domain data of the stator current signature.In deep learning(DL)models,the LSTM has shown better accuracy among all other three models.These results show that employing deep learning in fault detection and identification of induction motors can be very useful in predictive maintenance to avoid shutdown and production cycle stoppage in the industry.展开更多
As modern power systems grow in complexity,accurate and efficient fault detection has become increasingly important.While many existing reviews focus on a single modality,this paper presents a comprehensive survey fro...As modern power systems grow in complexity,accurate and efficient fault detection has become increasingly important.While many existing reviews focus on a single modality,this paper presents a comprehensive survey from a dual-modality perspective-infrared imaging and voiceprint analysis-two complementary,non-contact techniques that capture different fault characteristics.Infrared imaging excels at detecting thermal anomalies,while voiceprint signals provide insight into mechanical vibrations and internal discharge phenomena.We review both traditional signal processing and deep learning-based approaches for each modality,categorized by key processing stages such as feature extraction and classification.The paper highlights how these modalities address distinct fault types and how they may be fused to improve robustness and accuracy.Representative datasets are summarized,and practical challenges such as noise interference,limited fault samples,and deployment constraints are discussed.By offering a cross-modal,comparative analysis,this work aims to bridge fragmented research and guide future development in intelligent fault detection systems.The review concludes with research trends including multimodal fusion,lightweight models,and self-supervised learning.展开更多
Fault diagnosis is important for maintaining the safety and effectiveness of chemical process.Considering the multivariate,nonlinear,and dynamic characteristic of chemical process,many time-series-based data-driven fa...Fault diagnosis is important for maintaining the safety and effectiveness of chemical process.Considering the multivariate,nonlinear,and dynamic characteristic of chemical process,many time-series-based data-driven fault diagnosis methods have been developed in recent years.However,the existing methods have the problem of long-term dependency and are difficult to train due to the sequential way of training.To overcome these problems,a novel fault diagnosis method based on time-series and the hierarchical multihead self-attention(HMSAN)is proposed for chemical process.First,a sliding window strategy is adopted to construct the normalized time-series dataset.Second,the HMSAN is developed to extract the time-relevant features from the time-series process data.It improves the basic self-attention model in both width and depth.With the multihead structure,the HMSAN can pay attention to different aspects of the complicated chemical process and obtain the global dynamic features.However,the multiple heads in parallel lead to redundant information,which cannot improve the diagnosis performance.With the hierarchical structure,the redundant information is reduced and the deep local time-related features are further extracted.Besides,a novel many-to-one training strategy is introduced for HMSAN to simplify the training procedure and capture the long-term dependency.Finally,the effectiveness of the proposed method is demonstrated by two chemical cases.The experimental results show that the proposed method achieves a great performance on time-series industrial data and outperforms the state-of-the-art approaches.展开更多
This paper examines the progression and advancements in fault detection techniques for photovoltaic (PV) panels, a target for optimizing the efficiency and longevity of solar energy systems. As the adoption of PV tech...This paper examines the progression and advancements in fault detection techniques for photovoltaic (PV) panels, a target for optimizing the efficiency and longevity of solar energy systems. As the adoption of PV technology grows, the need for effective fault detection strategies becomes increasingly paramount to maximize energy output and minimize operational downtimes of solar power systems. These approaches include the use of machine learning and deep learning methodologies to be able to detect the identified faults in PV technology. Here, we delve into how machine learning models, specifically kernel-based extreme learning machines and support vector machines, trained on current-voltage characteristic (I-V curve) data, provide information on fault identification. We explore deep learning approaches by taking models like EfficientNet-B0, which looks at infrared images of solar panels to detect subtle defects not visible to the human eye. We highlight the utilization of advanced image processing techniques and algorithms to exploit aerial imagery data, from Unmanned Aerial Vehicles (UAVs), for inspecting large solar installations. Some other techniques like DeepLabV3 , Feature Pyramid Networks (FPN), and U-Net will be detailed as such tools enable effective segmentation and anomaly detection in aerial panel images. Finally, we discuss implications of these technologies on labor costs, fault detection precision, and sustainability of PV installations.展开更多
钻井顶部驱动装置结构复杂、故障类型多样,现有的故障树分析法和专家系统难以有效应对复杂多变的现场情况。为此,利用知识图谱在结构化与非结构化信息融合、故障模式关联分析以及先验知识传递方面的优势,提出了一种基于知识图谱的钻井...钻井顶部驱动装置结构复杂、故障类型多样,现有的故障树分析法和专家系统难以有效应对复杂多变的现场情况。为此,利用知识图谱在结构化与非结构化信息融合、故障模式关联分析以及先验知识传递方面的优势,提出了一种基于知识图谱的钻井顶部驱动装置故障诊断方法,利用以Transformer为基础的双向编码器模型(Bidirectional Encoder Representations from Transformers,BERT)构建了混合神经网络模型BERT-BiLSTM-CRF与BERT-BiLSTM-Attention,分别实现了顶驱故障文本数据的命名实体识别和关系抽取,并通过相似度计算,实现了故障知识的有效融合和智能问答,最终构建了顶部驱动装置故障诊断方法。研究结果表明:①在故障实体识别任务上,BERT-BiLSTM-CRF模型的精确度达到95.49%,能够有效识别故障文本中的信息实体;②在故障关系抽取上,BERT-BiLSTM-Attention模型的精确度达到93.61%,实现了知识图谱关系边的正确建立;③开发的问答系统实现了知识图谱的智能应用,其在多个不同类型问题上的回答准确率超过了90%,能够满足现场使用需求。结论认为,基于知识图谱的故障诊断方法能够有效利用顶部驱动装置的先验知识,实现故障的快速定位与智能诊断,具备良好的应用前景。展开更多
文摘The accurate identification and diagnosis of chemical process faults are crucial for ensuring the safe and stable operation of production plants.The current hot topic in industrial process fault diagnosis research is data-driven methods.Most of the existing fault diagnosis methods focus on a single shallow or deep learning model.This paper proposes a novel hybrid fault diagnosis method to fully utilize various features to improve the accuracy of fault diagnosis.Furthermore,the method addresses the issue of incomplete data,which has been largely overlooked in the majority of existing research.Firstly,the variable data is effectively fitted using orthogonal non-negative matrix tri-factorization,and the missing data in the matrix is solved to construct a complete production condition relationship.Next,the support vector machine model and the deep residual contraction network model are trained in parallel to prediagnose process faults by mining linear and non-linear interaction features.Finally,a novel mapping relationship is established between the result and model levels using the multi-layer perceptron algorithm to complete the final diagnosis and evaluation of the fault.To demonstrate the effectiveness of the proposed method,we conducted extensive comparative experiments on the Tennessee Eastman dataset and the ethylene plant cracking unit dataset.The experimental results show that the method has advantages in different evaluation metrics.
文摘The Erlian fault basin group, a typical Basin and Range type fault basin group, was formed during Late Jurassic to Early Cretaceous, in which there are rich coal, oil and gas resources. In the present paper the abundant geological and petroleum information accumulated in process of industry oil and gas exploration and development of the Erlian basin group is comprehensively analyzed, the structures related to formation of basin are systematically studied, and the complete extensional tectonic system of this basin under conditions of wide rift setting and low extensional ratio is revealed by contrasting study with Basin and Range Province of the western America. Based on the above studies and achievements of the former workers, the deep background of the basin development is treated.
基金Supported by the National Natural Science Foundation of China(21706291,61751305)
文摘Distillation is the most widely used operation for liquid mixture separation in the chemical industry. It is of great importance to detect and diagnose faults in distillation process. Due to the strong feedback and coupling of processes in a distillation column, it is difficult to use deep auto-encoders(DAEs) alone to achieve good results in detecting and diagnosing faults, in terms of accuracy and efficiency. This paper proposes a hybrid fault-diagnosis model based on convolutional neural networks(CNNs) and DAEs, by integrating the powerful capability of CNN in feature extraction and of DAE in classification. A case study was carried out with the distillation process of depropanization. It is shown that the proposed hybrid model is of good performance compared to other models, in terms of the accuracy of fault detection in such a process. Also, with the increase of structural layers of the CNN–DAE model, the diagnostic accuracy will be improved, with an optimal accuracy of 92.2%.
基金National Natural Science Foundation of China (Grant Nos. 51835009, 52105116)China Postdoctoral Science Foundation (Grant Nos. 2021M692557, 2021TQ0263)。
文摘Deep learning(DL) is progressively popular as a viable alternative to traditional signal processing(SP) based methods for fault diagnosis. However, the lack of explainability makes DL-based fault diagnosis methods difficult to be trusted and understood by industrial users. In addition, the extraction of weak fault features from signals with heavy noise is imperative in industrial applications. To address these limitations, inspired by the Filterbank-Feature-Decision methodology, we propose a new Signal Processing Informed Neural Network(SPINN) framework by embedding SP knowledge into the DL model. As one of the practical implementations for SPINN, a denoising fault-aware wavelet network(DFAWNet) is developed, which consists of fused wavelet convolution(FWConv), dynamic hard thresholding(DHT),index-based soft filtering(ISF), and a classifier. Taking advantage of wavelet transform, FWConv extracts multiscale features while learning wavelet scales and selecting important wavelet bases automatically;DHT dynamically eliminates noise-related components via point-wise hard thresholding;inspired by index-based filtering, ISF optimizes and selects optimal filters for diagnostic feature extraction. It’s worth noting that SPINN may be readily applied to different deep learning networks by simply adding filterbank and feature modules in front. Experiments results demonstrate a significant diagnostic performance improvement over other explainable or denoising deep learning networks. The corresponding code is available at https://github. com/alber tszg/DFAWn et.
基金The National Natural Science Foundation of China(No.41572276)the National Key Research and Development Program of China(No.2017YFC0805400).
文摘For the tunnel crossing active fault,the damage induced by fault movement is always serious.To solve such a problem,a detailed anti-faulting tunnel design process for Urumqi subway line 2 was introduced,and seven three-dimensional elastic-plastic finite element models were established.The anti-faulting design process included three steps.First,the damage of tunnel lining from different locations of fault rupture surfaces was analyzed.Then,the analysis of the effect on tunnel buried depth was given.Finally,the effect of the disaster mitigation method on the flexible joint was verified and the location of the flexible joint was discussed.The results show that when the properties of surrounding rock at the tunnel bottom grows soft,the tunnel deformation curve is smoother and tunnel damage induced by fault movement is less serious.The vertical displacement change ratio of secondary linings along the tunnel axis may be the main factor to cause shear damage to the tunnel.The interface between the hanging wall and fracture zone is defined as the most adverse fault rupture surface.The tunnel damage was reduced with the decrease in the tunnel buried depth as more energy was dissipated by overburden soil and the differential uplift zone of soil became more diffuse.The method of the flexible joint can reduce the tunnel damage significantly and the disaster mitigation effect of different locations on the flexible joint is different.The tunnel damage is reduced by the greatest degree when the flexible joint is located on the fault rupture surface.
基金Financial support for carrying out this work was provided by the Shandong Provincial Key Research and Development Program(2018YFJH0802)。
文摘Identification of abnormal conditions is essential in the chemical process.With the rapid development of artificial intelligence technology,deep learning has attracted a lot of attention as a promising fault identification method in chemical process recently.In the high-dimensional data identification using deep neural networks,problems such as insufficient data and missing data,measurement noise,redundant variables,and high coupling of data are often encountered.To tackle these problems,a feature based deep belief networks(DBN)method is proposed in this paper.First,a generative adversarial network(GAN)is used to reconstruct the random and non-random missing data of chemical process.Second,the feature variables are selected by Spearman’s rank correlation coefficient(SRCC)from high-dimensional data to eliminate the noise and redundant variables and,as a consequence,compress data dimension of chemical process.Finally,the feature filtered data is deeply abstracted,learned and tuned by DBN for multi-case fault identification.The application in the Tennessee Eastman(TE)process demonstrates the fast convergence and high accuracy of this proposal in identifying abnormal conditions for chemical process,compared with the traditional fault identification algorithms.
基金supported by the Key Project of National Natural Science Foundation of China(61933013)Ningbo 13th Five-year Marine Economic Innovation and Development Demonstration Project(NBH Y-2017-Z1)。
文摘This paper proposes a novel locally linear backpropagation based contribution(LLBBC) for nonlinear process fault diagnosis. As a method based on the deep learning model of auto-encoder(AE), LLBBC can deal with the fault diagnosis problem through extracting nonlinear features. When the on-line fault diagnosis task is in progress, a locally linear model is firstly built at the current fault sample. According to the basic idea of reconstruction based contribution(RBC), the propagation of fault information is described by using back-propagation(BP) algorithm. Then, a contribution index is established to measure the correlation between the variable and the fault, and the final diagnosis result is obtained by searching variables with large contributions. The smearing effect, which is an important factor affecting the performance of fault diagnosis, can be suppressed as well,and the theoretical analysis reveals that the correct diagnosis can be guaranteed by LLBBC. Finally, the feasibility and effectiveness of the proposed method are verified through a nonlinear numerical example and the Tennessee Eastman benchmark process.
基金the support of the‘Haptics,Human Robotics,and Condition Monitoring Lab’Established in Mehran University of Engineering and Technology,Jamshoro,under the umbrella of the National Centre of Robotics and Automation.
文摘Owing to the 4.0 industrial revolution condition monitoring maintenance is widely accepted as a useful approach to avoiding plant disturbances and shutdown.Recently,Motor Current Signature Analysis(MCSA)is widely reported as a condition monitoring technique in the detection and identification of individual andmultiple Induction Motor(IM)faults.However,checking the fault detection and classification with deep learning models and its comparison among them selves or conventional approaches is rarely reported in the literature.Therefore,in this work,wepresent the detection and identification of induction motor faults with MCSA and three Deep Learning(DL)models namely MLP,LSTM,and 1D-CNN.Initially,we have developed the model of Squirrel Cage induction motor in MATLAB and simulated it for single phasing and stator winding faults(SWF)using Fast Fourier Transform(FFT),Short Time Fourier Transform(STFT),and Continuous Wavelet Transform(CWT)to detect and identify the healthy and unhealthy conditions with phase to ground,single phasing and in multiple fault conditions using Motor Current Signature Analysis.The faults impact on stator current is presented in the time and frequency domain(i.e.,power spectrum).The simulation results show that the scalogram has shown good results in time-frequency analysis for fault and showing its impact on the energy of current during individual fault and multiple fault conditions.This is further investigated with three deep learning models(i.e.,MLP,LSTM,and 1D-CNN)for checking the fault detection and identification(i.e.,classification)improvement in a three-phase induction motor.By simulating the three-phase induction motor in various healthy and unhealthy conditions in MATLAB,we have collected current signature data in the time domain,labeled them accordingly and created the 50 thousand samples dataset for DL models.All the DL models are trained and validated with a suitable number of architecture layers.By simulation,the multiclass confusion matrix,precision,recall,and F1-score are obtained in several conditions.The result shows that the stator current signature of the motor can be used to detect individual and multiple faults.Moreover,deep learning models can efficiently classify the induction motor faults based on time-domain data of the stator current signature.In deep learning(DL)models,the LSTM has shown better accuracy among all other three models.These results show that employing deep learning in fault detection and identification of induction motors can be very useful in predictive maintenance to avoid shutdown and production cycle stoppage in the industry.
基金supported by Science and Technology Project of State Grid Corporation of China(52094024003D).
文摘As modern power systems grow in complexity,accurate and efficient fault detection has become increasingly important.While many existing reviews focus on a single modality,this paper presents a comprehensive survey from a dual-modality perspective-infrared imaging and voiceprint analysis-two complementary,non-contact techniques that capture different fault characteristics.Infrared imaging excels at detecting thermal anomalies,while voiceprint signals provide insight into mechanical vibrations and internal discharge phenomena.We review both traditional signal processing and deep learning-based approaches for each modality,categorized by key processing stages such as feature extraction and classification.The paper highlights how these modalities address distinct fault types and how they may be fused to improve robustness and accuracy.Representative datasets are summarized,and practical challenges such as noise interference,limited fault samples,and deployment constraints are discussed.By offering a cross-modal,comparative analysis,this work aims to bridge fragmented research and guide future development in intelligent fault detection systems.The review concludes with research trends including multimodal fusion,lightweight models,and self-supervised learning.
基金supported by the National Natural Science Foundation of China(62073140,62073141)the Shanghai Rising-Star Program(21QA1401800).
文摘Fault diagnosis is important for maintaining the safety and effectiveness of chemical process.Considering the multivariate,nonlinear,and dynamic characteristic of chemical process,many time-series-based data-driven fault diagnosis methods have been developed in recent years.However,the existing methods have the problem of long-term dependency and are difficult to train due to the sequential way of training.To overcome these problems,a novel fault diagnosis method based on time-series and the hierarchical multihead self-attention(HMSAN)is proposed for chemical process.First,a sliding window strategy is adopted to construct the normalized time-series dataset.Second,the HMSAN is developed to extract the time-relevant features from the time-series process data.It improves the basic self-attention model in both width and depth.With the multihead structure,the HMSAN can pay attention to different aspects of the complicated chemical process and obtain the global dynamic features.However,the multiple heads in parallel lead to redundant information,which cannot improve the diagnosis performance.With the hierarchical structure,the redundant information is reduced and the deep local time-related features are further extracted.Besides,a novel many-to-one training strategy is introduced for HMSAN to simplify the training procedure and capture the long-term dependency.Finally,the effectiveness of the proposed method is demonstrated by two chemical cases.The experimental results show that the proposed method achieves a great performance on time-series industrial data and outperforms the state-of-the-art approaches.
文摘This paper examines the progression and advancements in fault detection techniques for photovoltaic (PV) panels, a target for optimizing the efficiency and longevity of solar energy systems. As the adoption of PV technology grows, the need for effective fault detection strategies becomes increasingly paramount to maximize energy output and minimize operational downtimes of solar power systems. These approaches include the use of machine learning and deep learning methodologies to be able to detect the identified faults in PV technology. Here, we delve into how machine learning models, specifically kernel-based extreme learning machines and support vector machines, trained on current-voltage characteristic (I-V curve) data, provide information on fault identification. We explore deep learning approaches by taking models like EfficientNet-B0, which looks at infrared images of solar panels to detect subtle defects not visible to the human eye. We highlight the utilization of advanced image processing techniques and algorithms to exploit aerial imagery data, from Unmanned Aerial Vehicles (UAVs), for inspecting large solar installations. Some other techniques like DeepLabV3 , Feature Pyramid Networks (FPN), and U-Net will be detailed as such tools enable effective segmentation and anomaly detection in aerial panel images. Finally, we discuss implications of these technologies on labor costs, fault detection precision, and sustainability of PV installations.
文摘钻井顶部驱动装置结构复杂、故障类型多样,现有的故障树分析法和专家系统难以有效应对复杂多变的现场情况。为此,利用知识图谱在结构化与非结构化信息融合、故障模式关联分析以及先验知识传递方面的优势,提出了一种基于知识图谱的钻井顶部驱动装置故障诊断方法,利用以Transformer为基础的双向编码器模型(Bidirectional Encoder Representations from Transformers,BERT)构建了混合神经网络模型BERT-BiLSTM-CRF与BERT-BiLSTM-Attention,分别实现了顶驱故障文本数据的命名实体识别和关系抽取,并通过相似度计算,实现了故障知识的有效融合和智能问答,最终构建了顶部驱动装置故障诊断方法。研究结果表明:①在故障实体识别任务上,BERT-BiLSTM-CRF模型的精确度达到95.49%,能够有效识别故障文本中的信息实体;②在故障关系抽取上,BERT-BiLSTM-Attention模型的精确度达到93.61%,实现了知识图谱关系边的正确建立;③开发的问答系统实现了知识图谱的智能应用,其在多个不同类型问题上的回答准确率超过了90%,能够满足现场使用需求。结论认为,基于知识图谱的故障诊断方法能够有效利用顶部驱动装置的先验知识,实现故障的快速定位与智能诊断,具备良好的应用前景。
