Fault diagnosis of reciprocating compressor using a novel ensemble empirical mode decomposition-convolutional deep belief network

Zhang, Ying; Ji, Jinchen; Ma, Bo

doi:10.1016/j.measurement.2020.107619

Cited by 61 publications

(33 citation statements)

References 38 publications

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“…In 2019, for nonstationary vibration signals, Cheng et al used the ensemble empirical mode decomposition (EEMD) method [16]. In 2020, Zhang et al combined convolutional deep belief network with ensemble empirical mode decomposition for fault diagnosis of reciprocating compressor [17]. However,It has the problem of mode mixing and misclassified caused by fix band allocation.…”

Section: Introductionmentioning

confidence: 99%

An Improved Bearing Fault Diagnosis Scheme Based on Hierarchical Fuzzy Entropy and Alexnet Network

et al. 2021

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Bearings are important part of aerospace equipments. Therefore, bearings fault diagnosis and fault detection is necessary for the safe operation. Generally, the bearing are core component and runs in a complex and heavy background noise environment. Thence, the bearing signals will be overwhelmed by noise. At present, the diagnosis methods of bearings are based on the experience of experts which costs a lot of labor and time-consuming. Consequently, to address these inadequacies, a novel method based on variational mode decomposition (VMD) algorithms and Alexnet neural network has been presented. Firstly, it decomposes the nonstationary bearing signals into intrinsic mode functions adaptively. However, background noise will seriously affect the number of signal decompositions and reduce accuracy even over-decomposition. Thence, this paper proposed the hierarchical fuzzy entropy method to adaptively extract weak fault characteristics of bearings. Then Alexnet neural network has been utilized to learn the relationship of fault features and bearings health conditions. The architecture of Alexnet diagnosis model is convenient and efficient. Finally, two tentative dataset are adopted to verify the effectiveness and feasibility of the method proposed in this article. The results show that the presented method has a higher fault diagnosis accuracy rate than traditional convolutional neural networks.

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Section: Introductionmentioning

confidence: 99%

An Improved Bearing Fault Diagnosis Scheme Based on Hierarchical Fuzzy Entropy and Alexnet Network

et al. 2021

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“…Reference [14] and [15] pointed out that the components of the original signal and the spurious noise components of the same scale are achieved in the first few components of the decomposition. Aiming at these problems, the Improved CEEMDAN method is proposed.…”

Section: A Improved Ceemdan Algorithmmentioning

confidence: 99%

An Integrated Approach Based on Improved CEEMDAN and LSTM Deep Learning Neural Network for Fault Diagnosis of Reciprocating Pump

Bie

Lyu

et al. 2021

IEEE Access

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The reciprocating pump plays an important role in the petrochemical industry procedure, it is crucial in ensuring the systematic safety and stability. Since the useful feature information of the vibration signal from the reciprocating pump tends to be overwhelmed by the background ingredients, it is tough to realize the recognition on typical modes. Aiming at the extraction of reciprocating mechanical fault features and mode recognition, this paper proposes Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and LSTM (Long Short-Term Memory) deep neural network algorithm. Firstly, the IMF components are obtained by decomposing the vibration signals from the reciprocating pump with the Improved CEEMDAN algorithm, in which the key parameter β k is improved and redefined, for optimizing SNRs (Signal Noise Ratio) of the IMF (Intrinsic Mode Function) components. Then the corresponding singular spectral entropy is calculated and the feature vector is constructed. The classification modal based on LSTM deep network is developed in the data dividing-training and the final mode recognition process.The study shows that the proposed method can effectively extract the fault features of vibration signal of the reciprocating pump, and the testing modes could be accurately recognized with the developed classification model.

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“…For example, motor faults will cause changes in the motor temperature distribution. This feature has been widely used in motor fault diagnosis [ 16 , 17 ], according to the gas thermodynamic equation: PV = vRT where P represents gas pressure; V represents gas volume; v represents gas quality; T represents gas temperature; R is constant. The speed at which the compressor draws in gas will affect n, while changes in the compressor’s internal pressure will affect P, which will cause changes in the internal gas temperature.…”

Section: Experimental Facilitiesmentioning

confidence: 99%

“…Pichler et al [ 15 ] extracted the features of vibration signals from the difference of a time-frequency spectrogram between the normal and broken valves of different geometries and materials at various compressor loads, and performed fault diagnosis of reciprocating compressor through logistic regression and SVM methods. Zhang et al [ 16 ] combined a novel convolutional deep belief networks and multi-source information to improve the performance of fault diagnosis of reciprocating compressors. Cabrera et al [ 17 ] used a time series of vibration signals collected from the compressor to train a set of long short-term memory (LSTM) models, which is suitable for diagnosing the valve failure of reciprocating compressors.…”

Section: Introductionmentioning

confidence: 99%

Object-Based Thermal Image Segmentation for Fault Diagnosis of Reciprocating Compressors

Deng

Lin

Tang

et al. 2020

Sensors

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As an essential mechanical device in many industrial applications, reciprocating compressors have a high demand for operating efficiency and availability. Because the temperature of each part of a reciprocating compressor depends considerably on operating conditions, faults in any parts will cause the variation of the temperature distribution, which provides the possibility to distinguish the fault type of reciprocating compressors by differentiating the distribution using infrared thermal imaging. In this paper, three types of common fault are laboratory experimented in an uncontrolled temperature environment. The temperature distribution signals of a reciprocating compressor are captured by a non-contact infrared camera remotely in the form of heat maps during the experimental process. Based on the temperature distribution under baseline condition, temperature fields of six main components were selected via Hue-Saturation-Value (HSV) image as diagnostic features. During the experiment, the average grayscale values of each component were calculated to form 6-dimension vectors to represent the variation of the temperature distribution. A computational efficient multiclass support vector machine (SVM) model is then used for classifying the differences of the distributions, and the classification results demonstrate that the average temperatures of six main components aided by SVM is a promising technique to diagnose the faults of reciprocating compressors under various operating conditions with a classification accuracy of more than 99%.

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Fault diagnosis of reciprocating compressor using a novel ensemble empirical mode decomposition-convolutional deep belief network

Cited by 61 publications

References 38 publications

An Improved Bearing Fault Diagnosis Scheme Based on Hierarchical Fuzzy Entropy and Alexnet Network

An Improved Bearing Fault Diagnosis Scheme Based on Hierarchical Fuzzy Entropy and Alexnet Network

An Integrated Approach Based on Improved CEEMDAN and LSTM Deep Learning Neural Network for Fault Diagnosis of Reciprocating Pump

Object-Based Thermal Image Segmentation for Fault Diagnosis of Reciprocating Compressors

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