A Method for Evaluating the Sensitivity of Signal Features in Pattern Recognition Based on Neural Network

Shock signal features must be extracted for use in pattern recognition or fault diagnosis. In this work, we proposed a method for the feature extraction of shock signals, which are vibration signals that change faster and have larger amplitude ranges than general signals. First, we proposed the concepts of amplitude density for monotonic functions and piecewise monotonic functions. On the basis of these concepts, we then proposed the concept of the upper limit of density integral (ULDI), which was adopted to obtain signal features. Then, we introduced two types of serious fault cracks to the latch sheet of an automatic gun mechanism that is used on warships. Next, we applied the proposed method to extract the features of shock signals from data acquired when the automatic gun mechanism fired with normal and two fault patterns. Finally, we verified the effectiveness of our proposed method by applying the features that it extracted to a support vector machine (SVM). Our proposed method provided good results and was superior to the traditional statistics-based feature extraction method when applied to a SVM for classification. In addition, the former method demonstrated better generalisation than the latter. Thus, our method is an efficient approach for extracting shock signal features in pattern recognition and fault diagnosis.

Traditional bearing sensory diagnostic include touching and hearing rely on personal experience, and for more complex system are unable to meet the needs of equipment fault diagnosis. The research on bearing fault diagnosis is developing significantly. Bearings are used in rotating machinery and most machinery failures are caused by bearing failures. The fault diagnosis of bearings is an important research area. The core of bearing fault diagnosis is the pattern recognition of fault features. The key of pattern recognition is to develop a reasonable classifier. Intelligent pattern recognition has been developed such as principal components, support vector machine, neural network. In this study, a bearing fault diagnosis based on exploring pattern recognition is proposed. The key to pattern recognition is to design a significant classifier. A number of features from bearing vibration of normal and fault bearing are extracted and processed using principal components of correlation matrix. Plot of principal components shows the visualization of normal and fault bearing and the classifier is chosen subjectively. The principal components exploration will be confirmed using least squares support vector machine. The parameter of support vector machine estimated using heuristic optimization particle swarm optimization. The proposed method can be applied in the detection of faults of bearing

In order to improve the accuracy of bearing fault diagnosis under a small sample, variable load, and noise conditions, a new fault diagnosis method based on an image information fusion and Vision Transformer (ViT) transfer learning model is proposed in this paper. Firstly, the method applies continuous wavelet transform (CWT), Gramian angular summation field (GASF), and Gramian angular difference field (GADF) to the time series data, and generates three grayscale images. Then, the generated three grayscale images are merged into an information fusion image (IFI) using image processing techniques. Finally, the obtained IFIs are fed into the advanced ViT model and trained based on transfer learning. In order to verify the effectiveness and superiority of the proposed method, the rolling bearing dataset from Case Western Reserve University (CWRU) is used to carry out experimental studies under different working conditions. Experimental results show that the method proposed in this paper is superior to other traditional methods in terms of accuracy, and the effect of ViT model based on transfer learning (TLViT) training is better than that of the Resnet50 model based on transfer learning training (TLResnet50) under variable loads and small sample conditions. In addition, the experimental results also prove that the IFI with multiple image information has better anti-noise ability than the single information image. Therefore, the method proposed in this paper can improve the accuracy of bearing fault diagnosis under small sample, variable load and noise conditions, and provide a new method for bearing fault diagnosis.

A new theoretical model of Pattern Recognition principles was proposed, which is based on matter cognition instead of matter classification in traditional statistical Pattern Recognition. This new model is closer to the function of human being, rather than traditional statistical Pattern Recognition using optimal separating as its main principle. So the new model of Pattern Recognition is called the Biomimetic Pattern Recognition (BPR). Its mathematical basis is placed on topological analysis of the sample set in the high dimensional feature space. Therefore, it is also called the Topological Pattern Recognition (TPR). The fundamental idea of this model is based on the fact of the continuity in the feature space of any one of the certain kinds of samples. We experimented with the Biomimetic Pattern Recognition (BPR) by using artificial neural networks, which act through covering the high dimensional geometrical distribution of the sample set in the feature space. Omnidirectionally cognitive tests were done on various kinds of animal and vehicle models of rather similar shapes. For the total 8800 tests, the correct recognition rate is 99.87%. The rejection rate is 0.13% and on the condition of zero error rates, the correct rate of BPR was much better than that of RBF-SVM.

The Sixth International Symposium on Neural Networks (ISNN 2009)

Multiprocessor systems for connectionist diagnosis of technical processes

Artificial neural networks (ANN) constitute a powerful class of nonlinear function approximate. ANN has been widely used in pattern recognition, prediction and classification. The application of wavelets in the fields of engineering has grown rapidly in the past few years. To improve the limitation of conventional applying traditional fault diagnosis method on gas turbine, a novel diagnosis approach integrating the wavelet transform with neural network is proposed. It can overcome the problems caused by local mimima of optimization. The model uses wavelets as the activation functions in neural networks. The wavelet basis function is assigned for each neuron of hidden layer and each weight is determined by learning. With respect to six kinds of typical and common fault based on thermo dynamic parameter of gas turbine, use these data as sample to train wavelet neural network, and then according the output of network to determine the type of fault. The experimental result show that the proposed gas turbine fault diagnostic model based on wavelet neural networks can diagnose the fault of gas turbine effectively. The method can be generalized to other fault diagnosis.

Floating wind turbine is the core of offshore floating power generation and an important way to develop offshore wind resources. In recent years, artificial intelligence technology has made great achievements in many fields, and equipment fault diagnosis is also very prominent. As the working environment of the floating fan unit is bad and far away from the land, once the floating fan unit generator fails, it may have very serious consequences. Based on the research results of artificial intelligence technology, this paper focuses on the application of artificial intelligence technology in the fault diagnosis of floating wind turbine generator, and proposes an intelligent fault diagnosis system framework. According to the research of expert system and artificial neural network, the fault diagnosis technology of floating fan generator is put forward, and the artificial neural network model and reasonable fault diagnosis reasoning flow are given.

Bearing fault diagnosis plays a pivotal role in the realm of condition-based maintenance, with vibration spectra analysis standing out as a highly effective method for discerning issues in rotating machinery. Various signal processing tools, including wavelet analysis, empirical mode decomposition, and Hilbert-Huang transform, have been employed to scrutinize vibration spectra. However, these methodologies often necessitate human expertise to ensure optimal outcomes. In the pursuit of automated fault diagnosis, machine learning tools such as Artificial Neural Networks (ANN) and support vector machines (SVM) have emerged as viable alternatives. Over recent decades, considerable research efforts have been devoted to exploring the viability of employing Artificial Neural Networks (ANN) for automatic fault diagnosis, resulting in predominantly positive findings. Nevertheless, the accuracy of Artificial Neural Networks (ANN) is intricately linked to factors such as the neural network structure, encompassing considerations like the number of nodes, hidden layers, and the choice of activation function. Addressing this challenge, the present study introduces an innovative hybrid algorithm for automated bearing fault diagnosis, integrating Artificial Neural Networks (ANN) with the Bayes’ Theorem (BT) theory. The hybrid algorithm exploits Bayes’ Theorem (BT) theory to augment fault diagnosis results derived from Artificial Neural Networks (ANN), specifically by alleviating conflicting outcomes generated within the neural network. The study focuses on characterizing four conditions of bearings, namely a healthy state and three distinct fault types: rolling element, inner race, and outer race faults. Through the proposed hybrid algorithm, working in tandem with artificial neural networks, a demonstrated superiority is established, surpassing the results obtained by Artificial Neural Networks (ANN) in isolation. This pioneering approach not only underscores the potential for heightened accuracy but also underscores the enhanced reliability achievable in automated bearing fault diagnosis.

The continuous development of power system is an important guarantee for the rapid development of economy and science and technology in China. The emergence of smart grid has accelerated the development of power big data and artificial intelligence in power field. In smart grid, the state of power equipment determines the state of power system and is the decisive factor for the stable operation of power grid. spark distributed parallel processing system can meet the storage and calculation of big data, and provides a new research idea for realizing fault classification and diagnosis of power equipment under big data. The concept of deep learning provides a platform for the analysis and prediction of power equipment status under big data. This paper introduces the basic contents of power equipment handover test, analyzes the common fault types of power equipment in handover test, introduces the existing fault diagnosis methods, puts forward the scheme of power equipment state diagnosis-based on spark platform, and classifies the faults of power equipment by using the naive Bayesian network in spark, Taking the test data as sample input, the fault diagnosis of power equipment can be carried out. On this basis, through the research of deep learning network, the feasibility of power equipment fault diagnosis is analyzed. The test shows that the method used in this paper can diagnose and predict the state of power equipment effectively.

Machine learning has demonstrated significant application potential in rolling bearing fault detection. However, traditional deep learning approaches typically depend on large-scale labeled datasets and are limited to identifying a single fault type, which poses challenges in meeting the complex fault diagnosis in real-world industrial scenarios, particularly under small sample conditions. To address these issues, this study proposes a multi-task attention fault diagnosis framework, convolutional attention neural network by neural discriminant dimension reduction (NDDR-CANN). The model employs multiple task-specific neural networks to extract distinct features such as fault type and severity, respectively, and achieves adaptive feature sharing across tasks by NDDR, thereby facilitating the transfer and complementarity of task-related knowledge and enhancing the model's generalization capability and predictive accuracy. Furthermore, by integrating a convolutional block attention module, the model can focus on salient features across channel and spatial dimensions, thereby improving feature discrimination. Experimental validation on the CWRU bearing dataset demonstrates that the proposed model exhibits robust effectiveness and adaptability under various fault conditions and small sample scenarios. Comparative experimental results further confirm that NDDR-CANN outperforms current mainstream few-shot fault diagnosis methodologies.

Ecological Applications of Fuzzy Logic.- Ecological Applications of Qualitative Reasoning.- Ecological Applications of Non-supervised Artificial Neural Networks.- Ecological Applications of Genetic Algorithms.- Ecological Applications of Evolutionary Computation.- Ecological Applications of Adaptive Agents.- Bio-Inspired Design of Computer Hardware by Self-Replicating Cellular Automata.- Prediction and Elucidation of Stream Ecosystems.- Development and Application of Predictive River Ecosystem Models Based on Classification Trees and Artificial Neural Networks.- Modelling Ecological Interrelations in Running Water Ecosystems with Artificial Neural Networks.- Non-linear Approach to Grouping, Dynamics and Organizational Informatics of Benthic Macroinvertebrate Communities in Streams by Artificial Neural Networks.- Elucidation of Hypothetical Relationships between Habitat Conditions and Macroinvertebrate Assemblages in Freshwater Streams by Artificial Neural Networks.- Prediction and Elucidation of River Ecosystems.- Prediction and Elucidation of Population Dynamics of the Blue-green Algae Microcystis aeruginosa and the Diatom Stephanodiscus hantzschii in the Nakdong River-Reservoir System (South Korea) by a Recurrent Artificial Neural Network.- An Evaluation of Methods for the Selection of Inputs for an Artificial Neural Network Based River Model.- Utility of Sensitivity Analysis by Artificial Neural Network Models to Study Patterns of Endemic Fish Species.- Prediction and Elucidation of Lake and Marine Ecosystems.- A Comparison between Neural Network Based and Multiple Regression Models for Chlorophyll-a Estimation.- Artificial Neural Network Approach to Unravel and Forecast Algal Population Dynamics of Two Lakes Different in Morphometry and Eutrophication.- Hybrid Evolutionary Algorithm for Rule Set Discovery in Time-Series Data to Forecast and Explain Algal Population Dynamics in Two Lakes Different in Morphometry and Eutrophication.- Multivariate Time Series Prediction of Marine Zooplankton by Artificial Neural Networks.- Classification of Fish Stock-Recruitment Relationships in Different Environmental Regimes by Fuzzy Logic with Bootstrap Re-sampling Approach.- Computational Assemblage of Ordinary Differential Equations for Chlorophyll-a Using a Lake Process Equation Library and Measured Data of Lake Kasumigaura.- Classification of Ecological Images at Micro and Macro Scale.- Identification of Marine Microalgae by Neural Network Analysis of Simple Descriptors of Flow Cytometric Pulse Shapes.- Age Estimation of Fish Using a Probabilistic Neural Network.- Pattern Recognition and Classification of Remotely Sensed Images by Artificial Neural Networks.

A fault diagnosis method based on meta-learning and an improved multi-channel convolutional neural network (MAML-MCCNN-ISENet) was proposed to solve the problems of insufficient feature extraction and low fault type identification accuracy of vibration signals at small sample sizes. The signal is first preprocessed using adaptive chirp mode decomposition (ACMD) methods. A multi-channel input structure is then employed to process the multidimensional signal information after preprocessing. The improved squeeze and excitation networks (ISENets) have been enhanced to concurrently enhance the network's adaptive perception of the significance of each channel feature. On this basis, a meta-learning strategy is introduced, the learning process of model initialization parameters is improved, the network is optimized by a multi-task learning mechanism, and the initial parameters of the diagnosis model are adaptively adjusted, so that the model can quickly adapt to new fault diagnosis tasks on limited datasets. Then, the overfitting problem under small sample conditions is alleviated, and the accuracy and robustness of fault identification are improved. Finally, the performance of the model is verified on the experimental data of the fault diagnosis of the laboratory plunger pump and the vibration dataset of the centrifugal pump of the Saint Longoval Institute of Engineering and Technology. The results show that the diagnostic accuracy of the proposed method for various diagnostic tasks can reach more than 90% on small samples.

A fault diagnosis and location method of artificial neural network (ANN) based on regularized radial basis function (RRBF) is proposed. The phase angle feature of fault voltage and current signal is analyzed. The proposed method adopts synchronized amplitude and phase angle feature for fault diagnosis based on RRBF neural network. The fault diagnosis and location for the distribution branch is researched in the IEEE 13-bus active distribution network (ADN) system. The diagnosis accuracy and location precision is analyzed considering the effect of different input signals, fault position, and fault resistance. The simulation result demonstrates that the location method based on phase angle feature shows higher accuracy. The RRBF fault diagnosis and location method aims to solve fault in ADN and lays the foundation to maintain ADN system stability.

With the development of big data, artificial intelligence and other technologies, data-driven aviation equipment fault diagnosis and prediction technology has gradually become a research hotspot in the aviation field. Many typical intelligent algorithm models have been applied to this field. However, limited by the airborne embedded computing environment, there are still some problems in the deployment of intelligent prediction models represented by deep neural networks on aircraft. This paper summarizes and analyzes the research and application of typical deep neural networks such as convolutional neural networks in the field of aircraft fault diagnosis and prediction. Facing the airborne embedded environment, the current difficulties in deploying the deep neural network algorithm model in the airborne environment are analyzed. The development direction of the application of fault prediction and diagnosis algorithms represented by neural networks in the future is discussed.