Investigation on recognition method of acoustic emission signal of the compressor valve based on the deep learning method

Zhang, Yangyang; Yang, Guanglu; Zhang, Dehai; Wang, Tao

doi:10.1016/j.egyr.2021.10.053

Cited by 14 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(4) Moderating variable: the degree of digital transformation of enterprises (INTE) Digitization is a complex and dynamic process that is extremely difficult to quantify, so the degree of digitization is introduced to measure the relatively static level of implementation of digitization in a company [90]. By drawing on existing research, this paper decides to use the proportion of the year-end intangible asset line items disclosed in the notes to the company's financial report that relate to digital technology to total intangible assets as a yardstick to measure the degree of digital transformation of the company [91].…”

Section: ) Mediating Variable: Corporate Environmental Protection Inp...mentioning

confidence: 99%

Does Environmental Regulation Promote Corporate Green Innovation? Empirical Evidence from Chinese Carbon Capture Companies

et al. 2023

Self Cite

View full text Add to dashboard Cite

The proposal of the “double carbon” goal of “carbon peak, carbon neutralization” highlights the determination of China’s green and low-carbon development. Carbon capture is one of the essential ways to reduce carbon dioxide (CO2) emissions and cope with climate change. Then, how to improve the green innovation capability of organizations and promote the transformation and upgrading of enterprises with green development is a practical problem that needs to be dealt with quickly. This paper uses multiple linear regression to investigate the impact of environmental regulation on corporate green innovation and explores the mediating effect of corporate environmental investment and the moderating effect of corporate digital transformation. The analysis results show that government environmental regulation can effectively enhance the green innovation of enterprises and environmental investments play an intermediary role. However, the development of environmental regulation in China is still relatively backward, and its positive incentive role needs to be further played. As a result, the government should strengthen environmental legislation while also accelerating system development, increasing corporate investment in environmental protection, and raising protection awareness among companies using digital network technology.

show abstract

Section: ) Mediating Variable: Corporate Environmental Protection Inp...mentioning

confidence: 99%

Does Environmental Regulation Promote Corporate Green Innovation? Empirical Evidence from Chinese Carbon Capture Companies

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…Prateepasen 20 innovated acoustic emission monitoring equipment and proposed a theoretical model of suction and discharge valve leakage prediction based on acoustic emission signals. Zhang 21 used the Acoustic Emission (AE) technology for nondestructive measurement of compressor valve movement. Furthermore, the AE signal is analyzed to predict the valves dynamic characteristics of based on the deep learning method.…”

Section: Introductionmentioning

confidence: 99%

Application of state parameter learning for fault diagnosis on the large reciprocating compressor

Guo

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part E:

View full text Add to dashboard Cite

According to the statistical results of the reciprocating compressor maintenance in chemical enterprises, the probability of faults caused by wear or damage of vulnerable parts inside the cylinder is close to 80%. Now, fault diagnosis of vulnerable parts inside the cylinder is relied on vibration signal, acoustic emission signal, or thermal parameters. However, it is difficult to extract eigenvalues from vibration signals and acoustic emission signals, which can be disturbed by noise easily. Thermal parameters are relatively stable and less affected by noise. The measurement of thermal parameters requires drilling testing holes through the cylinder wall, which will decrease the strength of the cylinder. According to the working principle of the compressor, fault of the vulnerable parts inside the cylinder would change the pressure and temperature distribution at the suction and the discharge port of the cylinder, while these state parameters are monitored by the parameter monitoring system on most of the compressors. For these reasons, a fault prediction model based on state parameters learning is proposed in this paper aiming to fault diagnosis of vulnerable parts inside the cylinder, which could make fault prediction without adding new hardware cost, such as sensors. Optimized back propagation neural network method by genetic algorithm (GA-BP) is applied to establish the fault prediction model to describe normal working process of the compressor. When fault of vulnerable parts inside the cylinder occurs, monitored pressure and temperature would deviate from the predicted value by the fault prediction. The degree of the deviation is adopted to the fault diagnosis. Then, experiments simulating the fault condition were carried out, and it shows that the accuracy of fault diagnosis using this method could exceed 95%. Verification test shows that the proposed method could successfully predict the fault before the unplanned shutdown.

show abstract

“…Deep learning techniques such as CNN and LSTM are used for dynamic characteristic prediction [7,8], along with digital image processing and artificial neural networks for fault detection [9,10]. However, as a research focus, adaptive signal extraction has the advantages of data adaptation, mode independence, high accuracy and easy implementation and is the current research focus of signal fault feature extraction [11].…”

Section: Introductionmentioning

confidence: 99%

A Double Interpolation and Mutation Interval Reconstruction LMD and Its Application in Fault Diagnosis of Reciprocating Compressor

Zhao

Liu³

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

The accuracy and stability of the envelope estimation function are enduring issues throughout the research process of LMD. This paper presents double interpolation and mutation interval reconstruction local mean decomposition (DIMIRLMD) to improve the stability of the demodulation process and the accuracy of PF components. DIMIRLMD first proposes a mutation interval reconstruction envelope algorithm using extreme symmetry points to suppress the demodulation mutation phenomenon, which disturbs the stability of the demodulation process, and then selects the optimal PF component from a double interpolation PF component library based on the index of orthogonality (IO) for a better hierarchical property. DIMIRLMD was employed to analyze the simulation signal and vibration signal of a reciprocating compressor in an oversized bearing clearance state, and the results illustrate its performances are more excellent than those of three other LMD methods. Furthermore, the envelope frequency spectrum obtained from the proposed LMD presents a clear double rotation fault frequency and lower noise disturbance.

show abstract

Investigation on recognition method of acoustic emission signal of the compressor valve based on the deep learning method

Cited by 14 publications

References 23 publications

Does Environmental Regulation Promote Corporate Green Innovation? Empirical Evidence from Chinese Carbon Capture Companies

Does Environmental Regulation Promote Corporate Green Innovation? Empirical Evidence from Chinese Carbon Capture Companies

Application of state parameter learning for fault diagnosis on the large reciprocating compressor

A Double Interpolation and Mutation Interval Reconstruction LMD and Its Application in Fault Diagnosis of Reciprocating Compressor

Contact Info

Product

Resources

About