An Early Classification Approach for Improving Structural Rotor Fault Diagnosis

Nath, Aneesh G; Sharma, Anshul; Udmale, Sandeep S.; Singh, Sanjay Kumar

doi:10.1109/tim.2020.3043959

Cited by 34 publications

(17 citation statements)

References 43 publications

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“…For example, merely several sets of bearing data with different damage degrees and different loads from the CWRU test bench are combined to construct a mixed variable working condition data set, and then divided into a training set and a test set according to a certain proportion to verify the model. This kind of method will face two main problems when it is applied in engineering: (1) The data to be diagnosed is not just homologous data from the same equipment. For example, although they are all rotor systems, they are likely to come from centrifugal compressors and flue gas turbines which are two completely different equipment.…”

Section: A Multi-source Domain Feature Space Constructionmentioning

confidence: 99%

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Research on a Multisource Domain Improved Fault Diagnosis Method of the Rotor System

Xiao

Wang

et al. 2022

IEEE Access

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In this paper, rotor systems of the rotating machinery such as steam turbines, centrifugal compressors and flue gas turbines are selected as the research objects. At present, most of the rotor system fault diagnosis methods based on artificial intelligence algorithms are in the laboratory research stage, and there is still a gap from the actual industrial application. Therefore, the multi-source domain improved fault diagnosis (MSDIFD) method for satisfying engineering applications is proposed in this paper. Firstly, typical labeled data are selected to construct a multi-source domain training feature space. Then, commonality fault features are extracted and screened by the improved adaptive variational mode decomposition (IAVMD), and the feature reconstruction signal is automatically output. Next, the reinforced semi-supervised transfer component analysis (RSSTCA) method based on enhanced kernel function is employed to narrow the disparity between feature vectors of cross domain data. Finally, typical failure case data and real-time monitoring data are used as the training data and test data of the model, respectively, and an ensemble fault recognition classifier is constructed to achieve failure mode identification of the rotor system. Using 40 groups of typical fault engineering cases under different equipment and different operating conditions, the proposed rotor fault identification method has been verified and compared with five published fault identification methods. The results indicate that the proposed method possesses more excellent fault diagnosis accuracy and domain generalization performance, and the MSDIFD method has good application and promotion value for solving cross equipment, cross working condition, and cross domain diagnostic tasks.INDEX TERMS Multi-source domain improved fault diagnosis, domain generalization, rotor system, reinforced semi-supervised transfer component analysis, improved adaptive variational mode decomposition

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Section: A Multi-source Domain Feature Space Constructionmentioning

confidence: 99%

“…Once it fails, it directly affects the working state of the entire rotating machinery. It might even cause shutdowns or equipment damage accidents [1]. Digital transformation of enterprises puts forward new requirements for predictive maintenance (PdM).…”

Section: Introductionmentioning

confidence: 99%

Research on a Multisource Domain Improved Fault Diagnosis Method of the Rotor System

Xiao

Wang

et al. 2022

IEEE Access

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“…To tackle this problem, some methods designed exiting rules to quit the classification process automatically. Sharma et al [ 7 , 17 ] first used the hybrid model of CNNs and RNNs to extract classification features from complete sequences, and then designed a cost function to learn the suitable exiting threshold automatically. Shekhar et al [ 18 ] calculated the difference between the early classification cost and the misclassification cost.…”

Section: Related Workmentioning

confidence: 99%

“…Due to the widespread application of sensors, a large amount of sequence data is generated in various real-world applications [ 1 , 2 , 3 , 4 ]. For some time-sensitive applications [ 5 , 6 , 7 , 8 , 9 , 10 , 11 ], such as disaster prediction, gas leakage detection and fault diagnosis, it is crucial to identify the classes of observable time series as accurately and quickly as possible [ 12 , 13 ]. Therefore, early time series classification (ETSC) has high research value.…”

Section: Introductionmentioning

confidence: 99%

Decoupled Early Time Series Classification Using Varied-Length Feature Augmentation and Gradient Projection Technique

Chen

Zhang

Tian

et al. 2022

Entropy

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Early time series classification (ETSC) is crucial for real-world time-sensitive applications. This task aims to classify time series data with least timestamps at the desired accuracy. Early methods used fixed-length time series to train the deep models, and then quit the classification process by setting specific exiting rules. However, these methods may not adapt to the length variation of flow data in ETSC. Recent advances have proposed end-to-end frameworks, which leveraged the Recurrent Neural Networks to handle the varied-length problems, and the exiting subnets for early quitting. Unfortunately, the conflict between the classification and early exiting objectives is not fully considered. To handle these problems, we decouple the ETSC task into the varied-length TSC task and the early exiting task. First, to enhance the adaptive capacity of classification subnets to the data length variation, a feature augmentation module based on random length truncation is proposed. Then, to handle the conflict between classification and early exiting, the gradients of these two tasks are projected into a unified direction. Experimental results on 12 public datasets demonstrate the promising performance of our proposed method.

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“…• Re-Sampling [48]: this is the straightforward technique to solve the class skew problem in tabular data. Here, we either under-sample or over-sample the available data to meet the requirement of equal class distribution.…”

Section: A Vae Evaluationmentioning

confidence: 99%

A New Deep Stacked Architecture for Multi-Fault Machinery Identification With Imbalanced Samples

et al. 2021

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Effective intelligent fault diagnosis of rotating machinery using its vibrational signals has a considerable influence on certain analysis factors such as the reliability, performance, and productivity of a variety of modern manufacturing machines. Traditional intelligent approaches lack generalization schemes and add the burden of extracting features from data-driven cases. On the other hand, the Deep Learning (DL) studies have reported capabilities higher than the expectations of the researchers' objectives. In this context, this paper proposes a new deep architecture based on Stacked Variant Autoencoders for multi-fault machinery identification with imbalanced samples. The proposed model starts with a Variational Autoencoder (VAE) for facilitating data augmentation of small and imbalanced data samples using Gaussian distribution. After the preparation of suitable samples based on quality and size, the preprocessed vibration signals obtained are injected into the deep framework. The proposed deep architecture contains two subsequent unsupervised Sparse Autoencoders (SAE) with a penalty term that helps in acquiring more abstract and essential features as well as avoiding redundancy. The output of the second SAE is integrated on a supervised Logistic Regression (LR) with 10 classes. This is utilized for the proposed classifier training to achieve accurate fault identification. Experimental results show the efficiency of the proposed model which achieved an accuracy of 93.2%. In addition, for extensive comparative analysis issue, the Generative Adversarial Network (GAN) and triNetwork Generative Adversarial Network (tnGAN) were both implemented on the vibrational signal data, where the proposed method reported better results in terms of training and testing time and overall accuracy.

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An Early Classification Approach for Improving Structural Rotor Fault Diagnosis

Cited by 34 publications

References 43 publications

Research on a Multisource Domain Improved Fault Diagnosis Method of the Rotor System

Research on a Multisource Domain Improved Fault Diagnosis Method of the Rotor System

Decoupled Early Time Series Classification Using Varied-Length Feature Augmentation and Gradient Projection Technique

A New Deep Stacked Architecture for Multi-Fault Machinery Identification With Imbalanced Samples

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