scholarly journals A Lightweight Framework for Intelligent Fault Diagnosis Acceleration in Complex Industrial Systems

2021 ◽  
Author(s):  
Nanliang Shan
Keyword(s):  
Fault Diagnosis ◽  
Dimensional Space ◽  
Vibration Signal ◽  
Monitoring Data ◽  
Data Sampling ◽  
Intelligent Fault Diagnosis ◽  
Compressed Sampling ◽  
Lightweight Framework ◽  
Sampled Signal ◽  
Low Dimensional

<p>With the acquisition of massive condition monitoring data, how to realize real-time and efficient intelligent fault diagnosis is the focus of current research. Inspired by the ideas of compressed sensing (CS) and deep extreme learning machines (DELM), a data-driven lightweight framework is proposed to accelerate intelligent fault diagnosis. The integrated framework contains two modules: data sampling and fault diagnosis. Data sampling module projects the intensive original monitoring data into lightweight compressed sampling data non-linearly, which can effectively reduce the pressure of transmission, storage and calculation. Fault diagnosis module digs deeply into the inner connection between the compressed sampled signal and the fault types to realize accurate fault diagnosis. This work has three meaningful points. First, we believe that the bearing vibration signal is not strictly sparse in the transform domain. Second, we verified that the sparse signal after compressed sampling can be directly used for fault diagnosis without being reconstructed. Third, adding a kernel function to the DELM can perfectly map the low-dimensional inseparable features after compressed sampling to the high-dimensional space non-linearly to make it linearly separable and thus improve the classification accuracy</p>

scholarly journals A Lightweight Framework for Intelligent Fault Diagnosis Acceleration in Complex Industrial Systems

2021 ◽  
Author(s):  
Nanliang Shan
Keyword(s):  
Fault Diagnosis ◽  
Dimensional Space ◽  
Vibration Signal ◽  
Monitoring Data ◽  
Data Sampling ◽  
Intelligent Fault Diagnosis ◽  
Compressed Sampling ◽  
Lightweight Framework ◽  
Sampled Signal ◽  
Low Dimensional

<p>With the acquisition of massive condition monitoring data, how to realize real-time and efficient intelligent fault diagnosis is the focus of current research. Inspired by the ideas of compressed sensing (CS) and deep extreme learning machines (DELM), a data-driven lightweight framework is proposed to accelerate intelligent fault diagnosis. The integrated framework contains two modules: data sampling and fault diagnosis. Data sampling module projects the intensive original monitoring data into lightweight compressed sampling data non-linearly, which can effectively reduce the pressure of transmission, storage and calculation. Fault diagnosis module digs deeply into the inner connection between the compressed sampled signal and the fault types to realize accurate fault diagnosis. This work has three meaningful points. First, we believe that the bearing vibration signal is not strictly sparse in the transform domain. Second, we verified that the sparse signal after compressed sampling can be directly used for fault diagnosis without being reconstructed. Third, adding a kernel function to the DELM can perfectly map the low-dimensional inseparable features after compressed sampling to the high-dimensional space non-linearly to make it linearly separable and thus improve the classification accuracy</p>

scholarly journals Intelligent Fault Diagnosis and Forecast of Time-Varying Bearing Based on Deep Learning VMD-DenseNet

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7467
Author(s):  
Shih-Lin Lin
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Image Feature ◽  
Signal Acquisition ◽  
Time Varying ◽  
Intelligent Fault Diagnosis ◽  
Image Block ◽  
Wide Range

Rolling bearings are important in rotating machinery and equipment. This research proposes variational mode decomposition (VMD)-DenseNet to diagnose faults in bearings. The research feature involves analyzing the Hilbert spectrum through VMD whereby the vibration signal is converted into an image. Healthy and various faults show different characteristics on the image, thus there is no need to select features. Coupled with the lightweight network, DenseNet, for image classification and prediction. DenseNet is used to build a model of motor fault diagnosis; its structure is simple, and the calculation speed is fast. The method of using DenseNet for image feature learning can perform feature extraction on each image block of the image, providing full play to the advantages of deep learning to obtain accurate results. This research method is verified by the data of the time-varying bearing experimental device at the University of Ottawa. Through the four links of signal acquisition, feature extraction, fault identification, and prediction, a mechanical intelligent fault diagnosis system has established the state of bearing. The experimental results show that the method can accurately identify four common motor faults, with a VMD-DenseNet prediction accuracy rate of 92%. It provides a more effective method for bearing fault diagnosis and has a wide range of application prospects in fault diagnosis engineering. In the future, online and timely diagnosis can be achieved for intelligent fault diagnosis.

scholarly journals An Intelligent Fault Diagnosis Method for Reciprocating Compressors Based on LMD and SDAE

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1041 ◽  
Author(s):  
Yang Liu ◽  
Lixiang Duan ◽  
Zhuang Yuan ◽  
Ning Wang ◽  
Jianping Zhao
Keyword(s):  
Fault Diagnosis ◽  
Health Management ◽  
Vibration Signal ◽  
Intelligent Fault Diagnosis ◽  
Traditional Methods ◽  
Learning Abilities ◽  
Fault Features ◽  
Correlation Criterion ◽  
Long Time ◽  
Diagnosis Method

The effective fault diagnosis in the prognostic and health management of reciprocating compressors has been a research hotspot for a long time. The vibration signal of reciprocating compressors is nonlinear and non-stationary. However, the traditional methods applied to processing such signals have three issues, including separating the useful frequency bands from overlapped signals, extracting fault features with strong subjectivity, and processing the massive data with limited learning abilities. To address the above issues, this paper, which is based on the idea of deep learning, proposed an intelligent fault diagnosis method combining Local Mean Decomposition (LMD) and the Stack Denoising Autoencoder (SDAE). The vibration signal is firstly decomposed by LMD and reconstructed based on the cross-correlation criterion. The virtual noise channel is constructed to reduce the noise of the vibration signal. Then, the de-noised signal is input into the trained SDAE model to learn the fault features adaptively. Finally, the conditions of the reciprocating compressor valve are classified by the proposed method. The results show that classification accuracy is 92.72% under the condition of a low signal-noise ratio, which is 5 percentage points higher than that of the traditional methods. This shows the effectiveness and robustness of the proposed method.

scholarly journals A bearing fault diagnosis method based on the low-dimensional compressed vibration signal

2015 ◽  
Vol 7 (7) ◽  
pp. 168781401559344 ◽  
Author(s):  
Xinpeng Zhang ◽  
Niaoqing Hu ◽  
Lei Hu ◽  
Ling Chen ◽  
Zhe Cheng
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method ◽  
Low Dimensional

scholarly journals A New Bearing Fault Diagnosis Method Based on Capsule Network and Markov Transition Field/Gramian Angular Field

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7762
Author(s):  
Bin Han ◽  
Hui Zhang ◽  
Ming Sun ◽  
Fengtong Wu
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Vibration Sensor ◽  
Sensor Data ◽  
Intelligent Fault Diagnosis ◽  
Bearing Fault ◽  
Markov Transition ◽  
Transition Field ◽  
The Impact ◽  
Western Reserve

Compared to time-consuming and unreliable manual analysis, intelligent fault diagnosis techniques using deep learning models can improve the accuracy of intelligent fault diagnosis with their multi-layer nonlinear mapping capabilities. This paper proposes a model to perform fault diagnosis and classification by using a time series of vibration sensor data as the input. The model encodes the raw vibration signal into a two-dimensional image and performs feature extraction and classification by a deep convolutional neural network or improved capsule network. A fault diagnosis technique based on the Gramian Angular Field (GAF), the Markov Transition Field (MTF), and the Capsule Network is proposed. Experiments conducted on a bearing failure dataset from Case Western Reserve University investigated the impact of two coding methods and different network structures on the diagnosis accuracy. The results show that the GAF technique retains more complete fault characteristics, while the MTF technique contains a small number of fault characteristics but more dynamic characteristics. Therefore, the proposed method incorporates GAF images and MTF images as a dual-channel image input to the capsule network, enabling the network to obtain a more complete fault signature. Multiple sets of experiments were conducted on the bearing fault dataset at Case Western Reserve University, and the Capsule Network in the proposed model has an advantage over other convolutional neural networks and performs well in the comparison of fault diagnosis methods proposed by other researchers.

Intelligent fault diagnosis for rotating machinery using L1/2-SF under variable rotational speed

2020 ◽  
pp. 095440702096462
Author(s):  
Jinrui Wang ◽  
Shanshan Ji ◽  
Baokun Han ◽  
Huaiqian Bao
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Rotational Speed ◽  
Vibration Signal ◽  
Fault Classification ◽  
Mechanical Transmission ◽  
Intelligent Fault Diagnosis ◽  
Planetary Gearbox ◽  
Non Stationary Signal ◽  
Regularization Strategy

Sparse filtering (SF), as an effective feature extraction technique, has attracted considerable attention in the field of mechanical fault diagnosis. But the generalization ability of SF to handle non-stationary signal under variable rotational speed is still poor. When the rotating parts of mechanical transmission work at a constant speed, the collected vibration signal is strongly correlated with the fault type. However, the mappings will no longer be so simple under the condition of variable rotational speed, which brings a rigorous challenge to intelligent fault diagnosis. To overcome the aforementioned deficiency, a novel L1/2 regularized SF method ( L1/2-SF) is studied in this paper. Specifically, L1/2 regularization strategy is added to the cost function of SF, then the L1/2-SF is directly employed to extract sparse features from the raw vibration data under variable rotational speed condition. In order to understand the sparse feature extraction ability of the L1/2 regularization, a physical explanation of the sparse solution generated by the L1/2 regularization strategy is explored. Next, softmax regression is employed for fault classification connected with the output layer of L1/2-SF. The effectiveness of L1/2-SF method is verified using a planetary gearbox dataset and a bearing dataset, respectively. Experiment results show that L1/2-SF can deal well with the variable rotational speed problem and is superior to other methods.

scholarly journals Intelligent Fault Diagnosis Based on Vibration Signal Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-1 ◽  
Author(s):  
Minvydas Ragulskis ◽  
Lu Chen ◽  
Ganging Song ◽  
Ameen El Sinawi
Keyword(s):  
Fault Diagnosis ◽  
Signal Analysis ◽  
Vibration Signal ◽  
Intelligent Fault Diagnosis ◽  
Vibration Signal Analysis

scholarly journals Enhanced K-Nearest Neighbor for Intelligent Fault Diagnosis of Rotating Machinery

2021 ◽  
Vol 11 (3) ◽  
pp. 919
Author(s):  
Jiantao Lu ◽  
Weiwei Qian ◽  
Shunming Li ◽  
Rongqing Cui
Keyword(s):  
Fault Diagnosis ◽  
Nearest Neighbor ◽  
Vibration Signal ◽  
Reconstruction Algorithm ◽  
Nearest Neighbors ◽  
Health Condition ◽  
Rotating Machinery ◽  
Intelligent Fault Diagnosis ◽  
K Nearest Neighbor ◽  
Case Based

Case-based intelligent fault diagnosis methods of rotating machinery can deal with new faults effectively by adding them into the case library. However, case-based methods scarcely refer to automatic feature extraction, and k-nearest neighbor (KNN) commonly required by case-based methods is unable to determine the nearest neighbors for different testing samples adaptively. To solve these problems, a new intelligent fault diagnosis method of rotating machinery is proposed based on enhanced KNN (EKNN), which can take advantage of both parameter-based and case-based methods. First, EKNN is embedded with a dimension-reduction stage, which extracts the discriminative features of samples via sparse filtering (SF). Second, to locate the nearest neighbors for various testing samples adaptively, a case-based reconstruction algorithm is designed to obtain the correlation vectors between training samples and testing samples. Finally, according to the optimized correlation vector of each testing sample, its nearest neighbors can be adaptively selected to obtain its corresponding health condition label. Extensive experiments on vibration signal datasets of bearings are also conducted to verify the effectiveness of the proposed method.

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