A New Transferable Fault Diagnosis Approach of Rotating Machinery Based on Deep Autoencoder and Dominant Features Selection under Different Operating Conditions

In the actual industrial scenarios, most existing fault diagnosis approaches are faced with two challenges, insufficient labeled training data and distribution divergences between training and testing datasets. For the above issues, a new transferable fault diagnosis approach of rotating machinery based on deep autoencoder and dominant features selection is proposed in this article. First, maximal overlap discrete wavelet packet transform is applied for signals processing and mix-domains statistical feature extraction. Second, dominant features selection by importance score and differences between domains is proposed to select dominant features with high fault-discriminative ability and domain invariance. Then, selected dominant features are used for pretraining deep autoencoder (source model), which helps in enhancing the fault representative ability of deep features. The parameters of the source model are transferred to the target model, and normal state features from target domain are adopted for fine-tuning the target model. Finally, the target model is applied for fault patterns classification. Motor and bearing fault datasets are used for a series of experiments, and the results verify that the proposed methods have better cross-domain diagnosis performance than comparative models.

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An Analog Circuit Fault Diagnosis Approach Based on Wavelet-based fractal analysis and Multiple Kernel SVM

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813666201207154641 ◽

2020 ◽

Vol 13 ◽

Author(s):

Jianfeng Jiang

Keyword(s):

Fault Diagnosis ◽

Fractal Analysis ◽

Analog Circuit ◽

Training Data ◽

Support Vector ◽

Pass Filter ◽

Multiple Kernel ◽

Testing Data ◽

Circuit Fault Diagnosis ◽

Diagnosis Approach

Objective: In order to diagnose the analog circuit fault correctly, an analog circuit fault diagnosis approach on basis of wavelet-based fractal analysis and multiple kernel support vector machine (MKSVM) is presented in the paper. Methods: Time responses of the circuit under different faults are measured, and then wavelet-based fractal analysis is used to process the collected time responses for the purpose of generating features for the signals. Kernel principal component analysis (KPCA) is applied to reduce the features’ dimensionality. Afterwards, features are divided into training data and testing data. MKSVM with its multiple parameters optimized by chaos particle swarm optimization (CPSO) algorithm is utilized to construct an analog circuit fault diagnosis model based on the testing data. Results: The proposed analog diagnosis approach is revealed by a four opamp biquad high-pass filter fault diagnosis simulation. Conclusion: The approach outperforms other commonly used methods in the comparisons.

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Construction of a deep sparse filtering network for rotating machinery fault diagnosis

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/09544070211014852 ◽

2021 ◽

pp. 095440702110148

Author(s):

Chun Cheng ◽

Wei Zou ◽

Weiping Wang ◽

Michael Pecht

Keyword(s):

Fault Diagnosis ◽

Back Propagation ◽

Feature Learning ◽

Back Propagation Neural Network ◽

Rotating Machinery ◽

Fine Tuning ◽

Local Optimum ◽

Back Propagation Algorithm ◽

Intelligent Fault Diagnosis ◽

Sparse Filtering

Deep neural networks (DNNs) have shown potential in intelligent fault diagnosis of rotating machinery. However, traditional DNNs such as the back-propagation neural network are highly sensitive to the initial weights and easily fall into the local optimum, which restricts the feature learning capability and diagnostic performance. To overcome the above problems, a deep sparse filtering network (DSFN) constructed by stacked sparse filtering is developed in this paper and applied to fault diagnosis. The developed DSFN is pre-trained by sparse filtering in an unsupervised way. The back-propagation algorithm is employed to optimize the DSFN after pre-training. Then, the DSFN-based intelligent fault diagnosis method is validated using two experiments. The results show that pre-training with sparse filtering and fine-tuning can help the DSFN search for the optimal network parameters, and the DSFN can learn discriminative features adaptively from rotating machinery datasets. Compared with classical methods, the developed diagnostic method can diagnose rotating machinery faults with higher accuracy using fewer training samples.

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Fault Diagnosis Approach for Rotating Machinery Based on Feature Importance Ranking and Selection

Shock and Vibration ◽

10.1155/2021/8899188 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Zong Yuan ◽

Taotao Zhou ◽

Jie Liu ◽

Changhe Zhang ◽

Yong Liu

Keyword(s):

Fault Diagnosis ◽

Wavelet Packet ◽

Vibration Signal ◽

Rotating Machinery ◽

Ranking And Selection ◽

Fault Features ◽

Feature Importance ◽

Importance Ranking ◽

Common Signal ◽

Diagnosis Approach

The key to fault diagnosis of rotating machinery is to extract fault features effectively and select the appropriate classification algorithm. As a common signal decomposition method, the effect of wavelet packet decomposition (WPD) largely depends on the applicability of the wavelet basis function (WBF). In this paper, a novel fault diagnosis approach for rotating machinery based on feature importance ranking and selection is proposed. Firstly, a two-step principle is proposed to select the most suitable WBF for the vibration signal, based on which an optimized WPD (OWPD) method is proposed to decompose the vibration signal and extract the fault information in the frequency domain. Secondly, FE is utilized to extract fault features of the decomposed subsignals of OWPD. Thirdly, the categorical boosting (CatBoost) algorithm is introduced to rank the fault features by a certain strategy, and the optimal feature set is further utilized to identify and diagnose the fault types. A hybrid dataset of bearing and rotor faults and an actual dataset of the one-stage reduction gearbox are utilized for experimental verification. Experimental results indicate that the proposed approach can achieve higher fault diagnosis accuracy using fewer features under complex working conditions.

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Tensor Transfer Learning for Intelligence Fault Diagnosis of Bearing with Semisupervised Partial Label Learning

Journal of Sensors ◽

10.1155/2021/6205890 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Chaofan Hu ◽

Zhichao Zhou ◽

Biao Wang ◽

WeiGuang Zheng ◽

Shuilong He

Keyword(s):

Fault Diagnosis ◽

Domain Adaptation ◽

Subspace Learning ◽

Rotating Machinery ◽

Training Data ◽

Tensor Representation ◽

Vibration Signals ◽

Tensor Representations ◽

Partial Label Learning ◽

And Training

A new tensor transfer approach is proposed for rotating machinery intelligent fault diagnosis with semisupervised partial label learning in this paper. Firstly, the vibration signals are constructed as a three-way tensor via trial, condition, and channel. Secondly, for adapting the source and target domains tensor representations directly, without vectorization, the domain adaptation (DA) approach named tensor-aligned invariant subspace learning (TAISL) is first proposed for tensor representation when testing and training data are drawn from different distribution. Then, semisupervised partial label learning (SSPLL) is first introduced for tackling a problem that it is hard to label a large number of instances and there exists much data left to be unlabeled. Ultimately, the proposed method is used to identify faults. The effectiveness and feasibility of the proposed method has been thoroughly validated by transfer fault experiments. The experimental results show that the presented technique can achieve better performance.

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A Variational Stacked Autoencoder with Harmony Search Optimizer for Valve Train Fault Diagnosis of Diesel Engine

Sensors ◽

10.3390/s20010223 ◽

2019 ◽

Vol 20 (1) ◽

pp. 223 ◽

Cited By ~ 1

Author(s):

Kun Chen ◽

Zhiwei Mao ◽

Haipeng Zhao ◽

Zhinong Jiang ◽

Jinjie Zhang

Keyword(s):

Fault Diagnosis ◽

Diesel Engine ◽

Harmony Search ◽

Operating Conditions ◽

Valve Train ◽

Fine Tuning ◽

Stacked Autoencoder ◽

Adaptive Adjustment ◽

High Level ◽

Coupling Fault

Diesel engine fault diagnosis is vital due to enhanced reliability and economic efficiency requirements. The extracted features in traditional fault diagnosis are constructed manually, which is very cumbersome because of the requirement for lots of expertise. To handle this issue, this paper proposed a variational stacked autoencoder (VSAE) to adaptively extract features from angular domain signals. As an unsupervised algorithm, VSAE can extract high-level features with the help of multiple encoding layers. Layer-wise pre-training and fine-tuning are introduced to get a better network initialization value. Moreover, the dropout technique and the batch normalization technique are carried out to prevent over-fitting and implement fast convergence. Finally, the harmony search optimizer (HSO) algorithm is introduced to get an appropriate hyper-parameter setting in the VSAE model, as well as make adaptive adjustment of the network structure. In order to verify the proposed method, the valve train fault data is collected on the diesel engine test rig under twelve operating conditions. The results indicate that the proposed scheme can effectively diagnose different degrees of intake valve fault, exhaust valve fault, and coupling fault under various operating conditions. Furthermore, the classification accuracy improved from 94.10% to 98.85%VSAE compared with stacked autoencoder (SAE) and some other traditional fault diagnosis algorithms.

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A Hybrid Fault Diagnosis Approach for Rotating Machinery with the Fusion of Entropy-Based Feature Extraction and SVM Optimized by a Chaos Quantum Sine Cosine Algorithm

Entropy ◽

10.3390/e20090626 ◽

2018 ◽

Vol 20 (9) ◽

pp. 626 ◽

Cited By ~ 13

Author(s):

Wenlong Fu ◽

Jiawen Tan ◽

Chaoshun Li ◽

Zubing Zou ◽

Qiankun Li ◽

...

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Production Efficiency ◽

Economic Benefits ◽

Rotating Machinery ◽

Permutation Entropy ◽

Support Vector ◽

Industrial Manufacture ◽

Sine Cosine Algorithm ◽

Diagnosis Approach

As crucial equipment during industrial manufacture, the health status of rotating machinery affects the production efficiency and device safety. Hence, it is of great significance to diagnose rotating machinery faults, which can contribute to guarantee the running stability and plan for maintenance, thus promoting production efficiency and economic benefits. For this purpose, a hybrid fault diagnosis model with entropy-based feature extraction and SVM optimized by a chaos quantum sine cosine algorithm (CQSCA) is developed in this research. Firstly, the state-of-the-art variational mode decomposition (VMD) is utilized to decompose the vibration signals into sets of components, during which process the preset parameter K is confirmed with the central frequency observation method. Subsequently, the permutation entropy values of all components are computed to constitute the feature vectors corresponding to different kind of signals. Later, the newly developed sine cosine algorithm (SCA) is employed and improved with chaotic initialization by a Duffing system and quantum technique to optimize the support vector machine (SVM) model, with which the fault pattern is recognized. Additionally, the availability of the optimized SVM with CQSCA was revealed in pattern recognition experiments. Finally, the proposed hybrid fault diagnosis approach was employed for engineering applications as well as contrastive analysis. The comparative results show that the proposed method achieved the best training accuracy 99.5% and best testing accuracy 97.89%. Furthermore, it can be concluded from the boxplots of different diagnosis methods that the stability and precision of the proposed method is superior to those of others.

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Tackling challenges of neural purchase stage identification from imbalanced twitter data

Natural Language Engineering ◽

10.1017/s1351324919000433 ◽

2019 ◽

Vol 26 (4) ◽

pp. 383-411

Author(s):

Heike Adel ◽

Francine Chen ◽

Yan-Ying Chen

Keyword(s):

Domain Adaptation ◽

Source Model ◽

Training Data ◽

Fine Tuning ◽

Target Domain ◽

Product Domain ◽

Main Challenge ◽

Social Media Platforms ◽

Aida Model ◽

Label Distribution

AbstractTwitter and other social media platforms are often used for sharing interest in products. The identification of purchase decision stages, such as in the AIDA model (Awareness, Interest, Desire, and Action), can enable more personalized e-commerce services and a finer-grained targeting of advertisements than predicting purchase intent only. In this paper, we propose and analyze neural models for identifying the purchase stage of single tweets in a user’s tweet sequence. In particular, we identify three challenges of purchase stage identification: imbalanced label distribution with a high number of non-purchase-stage instances, limited amount of training data, and domain adaptation with no or only little target domain data. Our experiments reveal that the imbalanced label distribution is the main challenge for our models. We address it with ranking loss and perform detailed investigations of the performance of our models on the different output classes. In order to improve the generalization of the models and augment the limited amount of training data, we examine the use of sentiment analysis as a complementary, secondary task in a multitask framework. For applying our models to tweets from another product domain, we consider two scenarios: for the first scenario without any labeled data in the target product domain, we show that learning domain-invariant representations with adversarial training is most promising, while for the second scenario with a small number of labeled target examples, fine-tuning the source model weights performs best. Finally, we conduct several analyses, including extracting attention weights and representative phrases for the different purchase stages. The results suggest that the model is learning features indicative of purchase stages and that the confusion errors are sensible.

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Fault Diagnosis of Rotating Machinery Based on Convolutional Neural Network and Singular Value Decomposition

Shock and Vibration ◽

10.1155/2020/6542913 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Dong Liu ◽

Xu Lai ◽

Zhihuai Xiao ◽

Dong Liu ◽

Xiao Hu ◽

...

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Singular Value Decomposition ◽

Convolutional Neural Network ◽

Rotating Machinery ◽

Singular Value ◽

Support Vector ◽

Discrete Wavelet ◽

Comparison Results ◽

Value Decomposition

Vibration signal and shaft orbit are important features that reflect the operating state of rotating machinery. Fault diagnosis and feature extraction are critical to ensure the safety and reliable operation of rotating machinery. A novel method of fault diagnosis based on convolutional neural network (CNN), discrete wavelet transform (DWT), and singular value decomposition (SVD) is proposed in this paper. CNN is used to extract features of shaft orbit images, DWT is used to transform the denoised swing signal of rotating machinery, and the wavelet decomposition coefficients of each branch of the signal are obtained by the transformation. The SVD input matrix is formed after single branch reconstruction of the different branch coefficients, and the singular value is extracted to obtain the feature vector. The features extracted from both methods are combined and then classified by support vector machines (SVMs). The comparison results show that this hybrid method has a higher recognition rate than other methods.

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