Nonlinear dynamic response of reciprocating compressor system with rub-impact fault caused by subsidence

Xiao, Shungen; Liu, Shulin; Jiang, Feng; Song, Mengmeng; Cheng, Shouguo

doi:10.1177/1077546319835281

Cited by 50 publications

(26 citation statements)

References 25 publications

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“…Research concerning the modeling of reciprocating compressors uses phase space trajectories, Poincaré maps, and large Lyapunov exponents for identifying the chaotic behavior of a reciprocating compressor system with a subsidence rub-impact fault [13,14]. Non-linear and chaotic dynamical systems can be analyzed by using tools like the phase space diagram and the Poincaré plot [15].…”

Section: Introductionmentioning

confidence: 99%

Reciprocating Compressor Multi-Fault Classification Using Symbolic Dynamics and Complex Correlation Measure

et al. 2020

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Prognostics and Health Management technologies are useful for early fault detection and optimization of reliability in mechanical systems. Reciprocating compressors units are commonly used in industry for gas pressurization and transportation, and the valves in compressors are considered vulnerable parts susceptible to failure. Then, early detection of faults is important for avoiding catastrophic accidents. A feasible approach for fault detection consists in measuring the vibration signal for extracting useful features enabling fault detection and classification. In this research, a test-bed composed by two-stage reciprocating compressor was used for simulating a set of 13 different conditions of combined faults in valves and roller bearings. Three accelerometers were used for collecting the vibration signals for extracting three different types of features. These features were analyzed furthermore by using two random forest models to classifying the different faults. The first set of features was obtained by applying the symbolic dynamics algorithm, which provides the histogram of a set of symbols. This set of symbols was obtained by subdividing a 2D Poincaré plot into angular regions and counting the intersection of the phase trajectories on each of regions. The second type of features corresponds to the complex correlation measure which is calculated as the addition of the areas of triangles belonging to a Poincaré plot. Additionally, a small set of classical statistical features was also used for comparing their classification abilities to the new set of proposed features. The three sets of features enable highly accurate classification of the set of faults when used with random forest classification models. Notably, the ensemble subspace k-Nearest Neighbors algorithm provides classification accuracies higher than 99%.

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Section: Introductionmentioning

confidence: 99%

Reciprocating Compressor Multi-Fault Classification Using Symbolic Dynamics and Complex Correlation Measure

et al. 2020

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“…The compressed sensing (CS) [4][5][6][7] based on sparse representation has attracted significant attention as a new sampling theory in recent years. It breaks the limitation of Nyquist's sampling theorem, compresses signal sampling simultaneously, saves a lot of time and storage space, and has become a new research direction in the field of signal processing [8][9][10]. CS theory has been widely used in many biomedical imaging systems and physical imaging systems, such as computed tomography, ultrasound medical imaging, and single-pixel camera imaging.…”

Section: Introductionmentioning

confidence: 99%

A Matching Pursuit Algorithm for Backtracking Regularization Based on Energy Sorting

2020

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The signal reconstruction quality has become a critical factor in compressed sensing at present. This paper proposes a matching pursuit algorithm for backtracking regularization based on energy sorting. This algorithm uses energy sorting for secondary atom screening to delete individual wrong atoms through the regularized orthogonal matching pursuit (ROMP) algorithm backtracking. The support set is continuously updated and expanded during each iteration. While the signal energy distribution is not uniform, or the energy distribution is in an extreme state, the reconstructive performance of the ROMP algorithm becomes unstable if the maximum energy is still taken as the selection criterion. The proposed method for the regularized orthogonal matching pursuit algorithm can be adopted to improve those drawbacks in signal reconstruction due to its high reconstruction efficiency. The experimental results show that the algorithm has a proper reconstruction.

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“…The above research has achieved certain results. But due to the complex structure of the reciprocating compressor, multisource nonlinear pulse signal [14], the existing feature extraction methods are difficult to capture the essential fault feature, which make the diagnostic effects not ideal. After the deep learning convolutional neural network (CNN) was proposed [13], the recognition accuracy was significantly improved in many traditional pattern recognition tasks.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Reciprocating Compressor Based on Convolutional Neural Networks with Multisource Raw Vibration Signals

Yang

Jiang-an

Chen

et al. 2019

Mathematical Problems in Engineering

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Reciprocating compressors are widely used in petroleum industry. Due to containing complex nonlinear signal, it is difficult to extract the fault features from its vibration signals. This paper proposes a new method named Convolutional Neural Network based on Multisource Raw vibration signals (MSRCNN). The proposed method uses multisource raw vibration signals collected by several sensors as input and uses the designed CNN to operate both the feature extraction and classification. The gas valve signals of reciprocating compressor in different states are used as the experimental data. In order to test the effectiveness of the proposed method, it is compared with the traditional BP (Back-Propagation) neural network fault diagnosis method based on power spectrum energy and wavelet packet energy. In order to further test the antinoise performance of the proposed method, some noisy signals with different signal-to-noise ratios were constructed by adding white noise into sampled signals for testing. The results show that the MSRCNN model has higher fault recognition rate than the traditional methods. This indicates that the MSRCNN method not only has good fault recognition effect, but also has certain antinoise performance.

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Nonlinear dynamic response of reciprocating compressor system with rub-impact fault caused by subsidence

Cited by 50 publications

References 25 publications

Reciprocating Compressor Multi-Fault Classification Using Symbolic Dynamics and Complex Correlation Measure

Reciprocating Compressor Multi-Fault Classification Using Symbolic Dynamics and Complex Correlation Measure

A Matching Pursuit Algorithm for Backtracking Regularization Based on Energy Sorting

Fault Diagnosis of Reciprocating Compressor Based on Convolutional Neural Networks with Multisource Raw Vibration Signals

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