Fault Diagnosis of Reciprocating Compressor Valve Based on Transfer Learning Convolutional Neural Network

Guo, Fuyan; Zhang, Yan-chao; Wang, Yue; Ren, Pei-Jun; Wang, Ping

doi:10.1155/2021/8891424

Cited by 9 publications

(6 citation statements)

References 26 publications

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“…Other condition diagnosis approaches are presented in [213] and [214] on bearings and gearboxes, [215] on bearings and a crack rotating machinery dataset, and [216] on induction motors. Yang et al [179], Yang et al [217], and Guo et al [218] added an MMD term and a pseudo label term to the loss function. They applied their approaches to the condition diagnosis of bearings or reciprocating compressor valves.…”

Section: ) Other MMD Approachesmentioning

confidence: 99%

“…Reference [236] transferred knowledge between different sensor positions on electromechanical actuators. As previously mentioned, Gribbestad et al [129] used knowledge from aircraft engines on marine air compressors, and Guo et al [218] transferred knowledge between two valves installed in two different reciprocating compressor models (IODM-115-5-3-16 and IODM 70-5-4R). Propeller damage of similar quadrotor drones that differ, e.g., in the drone architecture, propeller size, or propeller rotation speed, was considered in [103].…”

Section: E Other Similar Systemsmentioning

confidence: 99%

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Using Data From Similar Systems for Data-Driven Condition Diagnosis and Prognosis of Engineering Systems: A Review and an Outline of Future Research Challenges

Braig

Zeiler

2023

IEEE Access

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Prognostics and health management (PHM) is an engineering approach dealing with the diagnosis, prognosis, and management of the health state of engineering systems. Methods that can analyze system behavior, fault conditions, and degradation are crucial for PHM applications, as they create the basis for determining, predicting, and monitoring the health of engineering systems. Data-driven methods have been proven to be suitable for automated diagnosis or prognosis due to their pattern recognition and anomaly detection abilities. Moreover, they do not require knowledge of the underlying degradation process. However, training data-driven methods usually requires a large amount of data, whose collection, cleansing, organization, and preparation are generally very time-consuming and costly. There are usually little or no run-to-failure data available at market launch, especially for new systems such as new machine generations. Nevertheless, related systems, hereinafter referred to as similar systems, often already exist, differing only in some technical characteristics. In this paper, the similar system problem is defined, and explanations of the difficulties that arise when using data from similar systems are presented. Furthermore, it is discussed why the usage of these data offers great potential for condition diagnosis and prognosis of engineering systems. An overview of data-driven methods that can be used to utilize data from similar systems is provided, and the methods that such systems already consider are highlighted. Two related research areas are identified, namely, fleet learning and transfer learning. In the paper, it is shown that similar system approaches will become an important branch of research in PHM. However, some difficulties must be overcome.INDEX TERMS Condition diagnosis, condition prognosis, data-driven methods, fleet learning, prognostics and health management (PHM), similar system approach, similar system problem, transfer learning.

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Section: ) Other MMD Approachesmentioning

confidence: 99%

Section: E Other Similar Systemsmentioning

confidence: 99%

Using Data From Similar Systems for Data-Driven Condition Diagnosis and Prognosis of Engineering Systems: A Review and an Outline of Future Research Challenges

Braig

Zeiler

2023

IEEE Access

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“…4 Varied methods were proposed to detect and diagnose 1 Department of Mechanical Engineering, National Central University, Taoyuan, Taiwan (ROC) 2 valve faults of RCs. [5][6][7][8][9] Sharma and Parey 10 applied the empirical mode decomposition and variational mode decomposition to process acquired vibration signals to diagnose faults in a reciprocating compressor under varying speed conditions. Tran et al 11 proposed using wavelet transformation to eliminate random noise, and applied the TKEO to estimate signal amplitude envelopes.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of reciprocating compressor using Teager-Kaiser energy operator and envelope spectral feature extraction

Hou,

Pan

2024

Advances in Mechanical Engineering

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This paper proposed and implemented the Teager-Kaiser energy operator (TKEO) and envelope spectral analysis techniques for the fault detection of discharge valves of a reciprocating compressor. Based on the extraction of fault features, the instantaneous frequency and amplitude of the signals due to the discharged valve based on energy identification can be effectively characterized by the TKEO that was used to identify the characteristic fault signals accurately. The synthesized signal is processed by envelope spectral analysis and TKEO, which can extract the characteristic signal and eliminate the noise. The experimental design is verified experimentally through different reciprocating compressor gas valve conditions. The simulation results verify the feasibility of the proposed method. The experimental verification is carried out through the measurement signals of the six-cylinder reciprocating compressor under different valve operating conditions. TKEO can remove background noise to obtain reciprocating compressor fault feature signals. Feature extraction is based on TKEO and envelope spectra for fault detection of reciprocating compressors. It is expected to reduce the errors produced by traditional manual fault diagnosis methods and improve the accuracy and efficiency of fault diagnosis. The research results of vibration fault feature extraction using TKEO can be used as the basis for fault diagnosis of the reciprocating compressor system.

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“…The multi-class support vector machine model was used to classify faults, with an accuracy of over 90%. Guo et al 17 proposed a fault diagnosis method for reciprocating compressor air valves based on transfer learning convolutional neural network. This method can use the valve data of laboratory compressors for network training, which solves the problem that there is not enough valve data of actual compressors for training.…”

Section: Introductionmentioning

confidence: 99%

A new method for reciprocating compressor fault diagnosis based on indicator diagram feature extraction

Zhu

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part A:

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The damage of vulnerable components inside the cylinder of reciprocating compressor, including the valve, piston ring, packing and piston ring, will cause the unexpected shutdown of the compressor unit. The indicator diagram which reflects the thermodynamic process in the cylinder is suitable for fault diagnosis of vulnerable components. However, most of the published fault diagnosis methods based on indicator diagram are aimed at the fault diagnosis of gas valve. In addition, the extracted features lack physical meaning in most fault diagnosis methods using machine learning algorithm, which is not conducive to be widely applied in practical engineering. In this study, features with definite physical meaning, including average suction pressure, average discharge pressure, area of indicator diagram and centroid coordinates of indicator diagram, are extracted from indicator diagram, and the threshold database of features under normal states and various fault states is established according to the contrast experiment. The results of the experiment show that the thresholds of the extracted parameters are obviously different under normal states and various fault states. During fault diagnosis, several groups of indicator diagrams of the compressor to be diagnosed are collected at first. After feature extraction, the extracted features are compared with the thresholds under different compressor states to obtain the average numbers of features within the threshold range under different compressor states to determine the compressor states. The accuracy of the method for judging whether the compressor is faulty or normal could reach 98.3%. Furthermore, the accuracy of identifying individual faulty components and multiple faulty components could reach 86.86%. The reason for the low overall diagnostic accuracy is that certain faults have similar effects on the features extracted from indicator diagram. The proposed method is believed as an excellent fault diagnosis method for the vulnerable components inside the cylinder of reciprocating compressor.

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Fault Diagnosis of Reciprocating Compressor Valve Based on Transfer Learning Convolutional Neural Network

Cited by 9 publications

References 26 publications

Using Data From Similar Systems for Data-Driven Condition Diagnosis and Prognosis of Engineering Systems: A Review and an Outline of Future Research Challenges

Using Data From Similar Systems for Data-Driven Condition Diagnosis and Prognosis of Engineering Systems: A Review and an Outline of Future Research Challenges

Fault diagnosis of reciprocating compressor using Teager-Kaiser energy operator and envelope spectral feature extraction

A new method for reciprocating compressor fault diagnosis based on indicator diagram feature extraction

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