Enhanced Data-Driven Fault Diagnosis of Chemical Process via Information Fusion in Multiple Neural Networks and Andrews Plot

2021 ◽  
Author(s):  
Shengkai Wang ◽  
Jie Zhang
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Chemical Process ◽  
Data Driven ◽  
Multiple Neural Networks

Based on Information Fusion Integrated Wavelet Neural Network Fault Diagnosis

2011 ◽  
Vol 219-220 ◽  
pp. 1077-1080
Author(s):  
Dong Yan Cui ◽  
Zai Xing Xie
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Wavelet Neural Network ◽  
Wavelet Neural Networks ◽  
Effective Combination ◽  
Fault Diagnosis System ◽  
Network Fault ◽  
Fault Characteristic

In this paper, the integration of wavelet neural network fault diagnosis system is established based on information fusion technology. the effective combination of fault characteristic information proves that integration of wavelet neural networks make better use of a variety of characteristic information than the list of wavelet neural networks to solve difficulties and problems which are difficult to resolve by a single network.

Interpretability of deep convolutional neural networks on rolling bearing fault diagnosis

2021 ◽  
Author(s):  
Huixin Yang ◽  
Xiang Li ◽  
Wei Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Deep Neural Network ◽  
Rapid Development ◽  
Saliency Map ◽  
Data Driven ◽  
Experimental Investigations ◽  
Deep Convolutional Neural Networks

Abstract Despite the rapid development of deep learning-based intelligent fault diagnosis methods on rotating machinery, the data-driven approach generally remains a "black box" to researchers, and its internal mechanism has not been sufficiently understood. The weak interpretability significantly impedes further development and applications of the effective deep neural network-based methods. This paper contributes efforts to understanding the mechanical signal processing of deep learning on the fault diagnosis problems. The diagnostic knowledge learned by the deep neural network is visualized using the neuron activation maximization and the saliency map methods. The discriminative features of different machine health conditions are intuitively observed. The relationship between the data-driven methods and the well-established conventional fault diagnosis knowledge is confirmed by the experimental investigations on two datasets. The results of this study can benefit researchers on understanding the complex neural networks, and increase the reliability of the data-driven fault diagnosis model in the real engineering cases.

A Data-Driven Fault Diagnosis Method using Modified Health Index and Deep Neural Networks of a Rolling Bearing

2021 ◽  
pp. 1-20
Author(s):  
Muyangzi Lin ◽  
Miyuan Shan ◽  
Jie Zhou ◽  
Yunjie Pan
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Deep Neural Networks ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Data Driven ◽  
Maintenance Cost ◽  
Health Index ◽  
Diagnosis Accuracy ◽  
Fault Mode

Abstract To improve fault diagnosis accuracy, a data-driven fault diagnosis model based on the adjustment Mahalanobis-Taguchi system (AMTS) was proposed. This model can analyze and identify the characteristics of vibration signals by using degradation monitoring as the classifier to capture and recognize the faults of product more accurately. To achieve this goal, we firstly used the modified ensemble empirical mode decomposition (MEEMD) scalar index to capture the bearing condition; then, by using the key intrinsic mode function (IMF) extracted by AMTS as the input of classifier, the optimized properties of bearing is decomposed and extracted effectively. Next, in order to improve the accuracy of the fault diagnosis we tested different modes; employing the modified health index (MHI), which is designed to overcome the shortcomings of the proposed health index as a classifier in single fault mode, and the deep neural networks (DNN) as a classifier in multi-fault mode. To evaluate the effectiveness of our model, the Case Western Reserve University (CWRU) bearing data were used for verification. Results indicated a strong robustness with 99.16% and 1.09s, 99.86% and 6.61s fault diagnosis accuracy in different data modes respectively. Furthermore, we argue that this data-driven fault diagnosis obviously lowers the maintenance cost of complex systems by significantly reducing the inspection frequency and improves future safety and reliability.

A New Gas Path Fault Diagnostic Method of Gas Turbine Based on Support Vector Machine

2015 ◽  
Vol 137 (10) ◽  
Author(s):  
Dengji Zhou ◽  
Huisheng Zhang ◽  
Shilie Weng
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Gas Turbine ◽  
Small Sample ◽  
Diagnostic Methods ◽  
Data Driven ◽  
Support Vector ◽  
Artificial Neural ◽  
Gas Path Fault ◽  
Turbine Model

As a crucial section of gas turbine maintenance decision-making process, to date, gas path fault diagnostic has gained a lot of attention. However, model-based diagnostic methods, like nonlinear gas path analysis (GPA) and genetic algorithms, need an accurate gas turbine model, and diagnostic methods without gas turbine model, like expert system, need a knowledge database. Both are difficult to gain. Thus, data-driven approach for gas path diagnosis, like artificial neural network, is increasingly attractive. Support vector machine (SVM), a novel computational learning method, seems to be a good choice for data-driven gas path fault diagnosis of gas turbine. In this paper, SVM is employed to diagnose a deteriorated gas turbine. The effect of sample number, kernel function, and monitoring parameters on diagnostic accuracy are studied, respectively. Additionally, the diagnostic result of SVM is compared to the result of artificial neural networks. The comparing result confirms that SVM has an obvious advantage over artificial neural networks method based on a small sample of data and can be employed to gas path fault diagnosis of gas turbine. In addition, SVM with radial basis kernel function is the best choice for gas turbine gas path fault diagnosis based on small sample.

scholarly journals Sensor Data-Driven Bearing Fault Diagnosis Based on Deep Convolutional Neural Networks and S-Transform

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2750 ◽  
Author(s):  
Guoqiang Li ◽  
Chao Deng ◽  
Jun Wu ◽  
Xuebing Xu ◽  
Xinyu Shao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Convolutional Neural Networks ◽  
Data Driven ◽  
Sensor Data ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
S Transform ◽  
Diagnosis Method

Accurate and timely bearing fault diagnosis is crucial to decrease the probability of unexpected failures of rotating machinery and improve the efficiency of its scheduled maintenance. Since convolutional neural networks (CNN) have poor feature extraction capability for sensor data with 1D format, CNN combined with signal processing algorithm is often adopted for fault diagnosis. This increases manual conversion work and expertise dependence while reducing the feasibility and robustness of the corresponding fault diagnosis method. In this paper, a novel sensor data-driven fault diagnosis method is proposed by fusing S-transform (ST) algorithm and CNN, namely ST-CNN. First of all, a ST layer is designed based on S-transform algorithm. In the ST layer, sensor data is automatically converted into 2D time-frequency matrix without manual conversion work. Then, a new ST-CNN model is constructed, and the time-frequency coefficient matrixes are inputted into the constructed ST-CNN model. After the training process of the ST-CNN model is completed, the classification layer such as softmax performs the fault diagnosis. Finally, the diagnosis performance of the proposed method is evaluated by using two public available datasets of bearings. The experimental results show that the proposed method performs the higher and more robust diagnosis performance than other existing methods.

Sign in / Sign up

Export Citation Format

Share Document