Fault Diagnosis of Multi-source Heterogeneous Information Fusion Based on Deep Learning

With the development of machine learning in recent years, the application of machine learning to machine fault diagnosis has become increasingly popular. Applying traditional feature extraction methods for complex systems will weaken the characterization capacity of features, which are not conducive to subsequent classification work. A reciprocating compressor is a complex system. In order to improve the fault diagnosis accuracy of complex systems, this paper does not use traditional fault diagnosis methods and applies deep convolutional neural networks (CNNs) to process this nonlinear and non-stationary fault signal. The valve fault data is obtained from the reciprocating compressor test bench of the Daqing Natural Gas Company. Firstly, the single-channel vibration signal is collected on the reciprocating compressor and the one-dimensional CNN (1-D CNN) is used for fault diagnosis and compared with the traditional model to verify the effectiveness of the 1-D CNN. Next, the collected eight channels signals (three channels of vibration signals, four channels of pressure signals, one channel key phase signal) are applied by 1-D CNN and 2-D CNN for fault diagnosis to verify the CNN that it is still suitable for multi-channel signal processing. Finally, further study on the influence of the input of different channel signal combinations on the model diagnosis accuracy is carried out. Experiments show that the seven-channel signal (three-channel vibration signal, four-channel pressure signal) with the key phase signal removed has the highest diagnostic accuracy in the 2-D CNN. Therefore, proper deletion of useless channels can not only speed up network operations but also improve diagnosis accuracy.

In order to fully utilize the features of multi-source heterogeneous data and effectively improve the accuracy and efficiency of fault diagnosis of rotating machinery, a multi-source heterogeneous data diagnosis method based on parameter collaborative optimization multi-scale convolutional autoencoder (MSCAE) is proposed. Firstly, multi-scale information learning is integrated into the convolutional autoencoder (CAE) to consider the temporal and spatial feature information of the diagnostic object simultaneously. To improve the training and diagnostic efficiency of MSCAE, a quantum particle swarm optimization (QPSO) module is used to perform hyperparameter optimization on it using chaos initialization and dynamic weight strategy (DWS). Besides, the sparse attention mechanism is introduced into the MSCAE model to improve the recognition rate of key fault features hidden in the original heterogeneous signals. Finally, the confusion matrix and visualization techniques are used to achieve fault classification. The experimental results demonstrate that after 100 experiments, the proposed method has an average diagnostic accuracy of 98.5 % and strong robustness to noise, providing a new method for rotating machinery fault diagnosis based on multi-source heterogeneous data.

With the rapid development of smart grid technology, a large amount of multi-source heterogeneous data has been generated in the power system, and its effective utilization is crucial for the optimization operation, demand prediction, and anomaly detection of the power system. However, the fusion processing of multi-source heterogeneous data faces many challenges, such as inconsistent data format, granularity, and quality, and direct fusion can easily lead to information redundancy and contradictions. A multi-source heterogeneous data fusion technology based on big data mining has been proposed to address the above issues. This method combines the advantages of convolutional neural networks and gated recurrent units to automatically extract features from image and sequence data and handle long-term dependency issues in time series data. Meanwhile, the K-means clustering algorithm is used to preprocess the data and train a specialized ConvGRU model. The results showed that in short-term load forecasting and abnormal electricity consumption behaviour detection tasks, the accuracy of this method reached 96.3% and 98.7%, respectively, with AUC values of 0.994 and 0.996. Compared to models that use only CNN or GRU, the performance is significantly improved. This method effectively solves the problem of integrating and processing multi-source heterogeneous power data, improves the accuracy and efficiency of power system data analysis, and provides strong support for the optimized operation of smart grids.

Comparative study on deep transfer learning strategies for cross-system and cross-operation-condition building energy systems fault diagnosis

Prosumers refer to the integration of production and consumption. Due to a large number of access to distributed power sources, electric vehicles, etc., which have a certain impact on power transformers, and increasing potential failures, transformers need to be monitored. In recent years, image recognition technology based on deep learning has been widely used in intelligent inspection image analysis. Aiming at the problem that the accuracy of appearance fault diagnosis in intelligent inspection images is limited by image quantity and quality, an image data set optimization method based on a seamless cloning algorithm and image cleaning is proposed. First, a sample generation method based on the seamless fusion algorithm is proposed, which seamlessly fuses the corroded texture of other power equipment into the transformer image to generate the rust transformer image. On this basis, an image quality evaluation and screening method based on the XGBoost algorithm is proposed to evaluate the image quality of the data set and clean the low-quality images. In addition, aiming at the limitation of a single diagnosis algorithm, an appearance diagnosis method based on heterogeneous model ensemble learning is proposed. By constructing multiple learning models and using a weighted voting strategy to fuse model outputs as final outputs, the accuracy of fault diagnosis is improved.

GeoAI in terrain analysis: Enabling multi-source deep learning and data fusion for natural feature detection

Rolling bearings are vital components in rotary machines where even minor faults can lead to the collapse of the entire mechanical system, resulting in significant economic losses. The condition monitoring and fault diagnosis of rolling bearings are crucial for safe operation of complex industrial systems. Traditional fault diagnosis methods mainly depend on expert experience, which leads to obvious deficiencies in diagnostic efficiency, especially in terms of accuracy. To address the issues of traditional bearing fault diagnosis relying on expert experience and low efficiency and accuracy of fault diagnosis, a bearing fault diagnosis model combining synchrosqueezed wavelet transform (SWT) and SE-ResNet is proposed. Firstly, the one-dimensional non-stationary bearing vibration signal is converted into a high-frequency two-dimensional time-frequency image through SWT, which serves as the input for the convolutional neural network; Secondly, an attention mechanism is introduced to construct an SE-ResNet model for extracting two-dimensional image features, which enhances the attention of important features and thus enhances the network's representation ability; Finally, the results of fault diagnosis are output through Softmax layer. To validate the capacity of the proposed model, experiments with the bearing dataset collected from Case Western Reserve University (CWRU) were conducted. The results show that the transfer learning accuracy of the CWRU dataset is 86.25% to 99%. This method has good feasibility, good convergence performance, and has high practical application value.

The study of the prediction of stock market volatility is of great significance to rationally control financial market risks and increase excessive investment returns and has received extensive attention from academic and commercial circles. However, as a dynamic and complex system, the stock market is affected by multiple factors and has a comprehensive capability to include complex financial data. Given that the explanatory variables of influencing factors are diverse, heterogeneous and complex, the existing intelligent algorithms have great limitations for the analysis and processing of multi-source heterogeneous data in the stock market. Therefore, this study adopts the edge weight and information transmission mechanism suitable for subgraph data to complete node screening, the gate recurrent unit (GRU) and long short-term memory (LSTM) to aggregate subgraph nodes. The compiled data contain the metapaths of three types of index data, and the introduction of the association relationship attention dimension effectively mines the implicit meanings of multi-source heterogeneous data. The metapath attention mechanism is combined with a graph neural network to complete the classification of multi-source heterogeneous graph data, by which the prediction of stock market volatility is realized. The results show that the above method is feasible for the fusion of heterogeneous stock market data and the mining of implicit semantic information of association relations. The accuracy of the proposed method for the prediction of stock market volatility in this study is 16.64% higher than that of the dimensional reduction index and 14.48% higher than that of other methods for the fusion and prediction of heterogeneous data using the same model.

This paper aims to build a new fault diagnosis system based on big data and artificial intelligence technology. This study first expounds the application value and prospect of big data technology and intelligent algorithm in the fault diagnosis of ship power system, and realizes the effective integration and mining of massive, multi-source and heterogeneous ship power system operation data through in-depth analysis of the characteristics of ship power system operation data. Secondly, in view of the nonlinear and time-varying characteristics of ship power system faults, this study designs and implements a fault diagnosis model that integrates intelligent algorithms such as machine learning and deep learning. The model can automatically extract key fault features, realize early warning and accurate identification of potential faults, and improve the accuracy and response speed of fault diagnosis through continuous learning and optimization. Finally, this study applies the big data intelligent system for fault diagnosis of computer-aided ship power system to a practical case to verify its effectiveness and practicability in improving fault diagnosis efficiency, reducing maintenance costs, and ensuring navigation safety. The research results have important theoretical significance and practical value for improving the level of ship operation and maintenance management and ensuring the stable operation of ship power system.

As the complexity of intelligent public transportation systems continues to increase and the degree of interconnection of equipment at all levels gradually deepens, building a digital twin system will generate a large amount of multi-source heterogeneous data. Effective processing and in-depth mining of data can provide real-time and intelligent decision-making optimization for the construction of digital twin systems, thereby efficiently handling complex emergencies. This paper conducts a systematic review on the processing methods and technologies of multi-source heterogeneous twin data in traffic dynamic scenarios. First, the content and classification of multi-source heterogeneous twin data in the operation process of intelligent bus systems are clarified; secondly, the multi-source The data processing methods and technologies applied in each stage of data collection, data storage, data fusion and data analysis in heterogeneous twin data processing are analyzed, and the advantages, disadvantages and applications of various methods and technologies are analyzed; finally, corresponding to the multiple This paper summarizes the methods and technologies for processing multi-source heterogeneous twin data, and points out the challenges and development trends faced by the current multi-source heterogeneous twin data processing methods and technologies.

Adaptive Convergent Visibility Graph Network: An interpretable method for intelligent rolling bearing diagnosis

Regarding the difficulty of extracting fault information in the faulty status of UAV (unmanned aerial vehicle) engines and the high time cost and large data requirement of the existing deep learning fault diagnosis algorithms with many training parameters, in this paper, a small-sample transfer learning fault diagnosis algorithm is proposed. First, vibration signals under the engine fault status are converted into a two-dimensional time-frequency map by multiple simultaneous squeezing S-transform (MSSST), which reduces the randomness of manually extracted features. Second, to address the problems of slow network model training and large data sample requirement, a transfer diagnosis strategy using the fine-tuned time-frequency map samples as the pre-training model of the ResNet-18 convolutional neural network is proposed. In addition, in order to improve the training effect of the network model, an agent model is introduced to optimize the hyperparameter network autonomously. Finally, experiments show that the algorithm proposed in this paper can obtain high classification accuracy in fault diagnosis of UAV engines compared to other commonly used methods, with a classification accuracy of faults as high as 97.1751%; in addition, we show that it maintains a very stable small-sample migratory learning capability under this condition.

PurposeThe purpose of this paper is to provide a shorter time cost, high-accuracy fault diagnosis method for water pumps. Water pumps are widely used in industrial equipment and their fault diagnosis is gaining increasing attention. Considering the time-consuming empirical mode decomposition (EMD) method and the more efficient classification provided by the convolutional neural network (CNN) method, a novel classification method based on incomplete empirical mode decomposition (IEMD) and dual-input dual-channel convolutional neural network (DDCNN) composite data is proposed and applied to the fault diagnosis of water pumps.Design/methodology/approachThis paper proposes a data preprocessing method using IEMD combined with mel-frequency cepstrum coefficient (MFCC) and a neural network model of DDCNN. First, the sound signal is decomposed by IEMD to get numerous intrinsic mode functions (IMFs) and a residual (RES). Several IMFs and one RES are then extracted by MFCC features. Ultimately, the obtained features are split into two channels (IMFs one channel; RES one channel) and input into DDCNN.FindingsThe Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection (MIMII dataset) is used to verify the practicability of the method. Experimental results show that decomposition into an IMF is optimal when taking into account the real-time and accuracy of the diagnosis. Compared with EMD, 51.52% of data preprocessing time, 67.25% of network training time and 63.7% of test time are saved and also improve accuracy.Research limitations/implicationsThis method can achieve higher accuracy in fault diagnosis with a shorter time cost. Therefore, the fault diagnosis of equipment based on the sound signal in the factory has certain feasibility and research importance.Originality/valueThis method provides a feasible method for mechanical fault diagnosis based on sound signals in industrial applications.

DGA (Dissolved Gas Analysis) is the traditional transformer fault diagnosis method, but it mainly depends on the experience of operators. In order to solve the limitations of traditional method, this paper introduces intelligent method for fault diagnosis of transformer. The intelligent method made fusion of various data, including SCADA data, oil dissolved gas sensor data, related electrical test data, operation maintenance records, and so on, employed space-time weighting fusion method based on BP neural network, and put forward the model of transformer fault diagnosis based on multi-source information fusion, which improved the accuracy of the transformer fault diagnosis dramatically.

With the development of hardware, deep learning, which based on high computation complexity, has become a popular tool for intelligent fault diagnosis. Compared with traditional methods, fault diagnosis method based on deep learning could approximate complex non-linear relationship and achieve very high precision under the same load. However, the performance of these deep learning-based fault diagnosis methods will be greatly reduced when the load changes, which are significant in fault diagnosis issues since the working load is changing all the time in real world industrial applications. Thus, a fault diagnosis method based on multi-scale convolutional neural network (CNN) is proposed to solve this problem. CNN is a type of neural network which can extract feature from the fault signal directly. In order to obtain sufficient fault information, the proposed method uses multi-scale convolution kernels to extract features of different scales. In addition, dropout is also added to the network to enhance the robustness and generalization capabilities of the model. The experimental results show that the proposed model not only performs well under the same load, but also achieves high fault diagnosis accuracy when the load changes.