Ship engine detection based on wavelet neural network and FPGA image scanning

Jiang, Yanhua; Lan, Guanglin; Zhang, Zhiqing

doi:10.1016/j.aej.2021.02.028

Cited by 12 publications

(7 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The corresponding characteristic indexes mainly include amplitude, kurtosis, power spectral density, etc. Typical methods are spectral analysis, Fourier transforms [66], wavelet transform [67], Stransform, empirical modal decomposition [68], and Hilbert-Huang transform (HHT). In a study by Chung et al [69], Blockchain Network Based Topic Mining Process for Cognitive Manufacturing was investigated.…”

Section: Methods Based On Independent Feature Extractionmentioning

confidence: 99%

A Review of Real-Time Fault Diagnosis Methods for Industrial Smart Manufacturing

Yan

Jing

et al. 2023

Processes

View full text Add to dashboard Cite

In the era of Industry 4.0, highly complex production equipment is becoming increasingly integrated and intelligent, posing new challenges for data-driven process monitoring and fault diagnosis. Technologies such as IIoT, CPS, and AI are seeing increasing use in modern industrial smart manufacturing. Cloud computing and big data storage greatly facilitate the processing and management of industrial information flow, which helps the development of real-time fault diagnosis (RTFD) technology. This paper provides a comprehensive review of the latest RTFD technologies in the field of industrial process monitoring and machine condition monitoring. The RTFD process is introduced in detail, starting with the data acquisition process. The current RTFD methods are divided into methods based on independent feature extraction, methods based on “end-to-end” neural networks, and methods based on qualitative knowledge reasoning from a new perspective. In addition, this paper discusses the challenges and potential trends of RTFD in future development to provide a reference for researchers focusing on this field.

show abstract

Section: Methods Based On Independent Feature Extractionmentioning

confidence: 99%

A Review of Real-Time Fault Diagnosis Methods for Industrial Smart Manufacturing

Yan

Jing

et al. 2023

Processes

View full text Add to dashboard Cite

show abstract

“…The feature-level fusion was oriented to feature fusion after the selection of monitoring objects. Jiang et al applied the wavelet neural network to analyze the characteristic data extracted by Fourier and other methods and integrated the characteristic data of various equipment states, which further improves the accuracy of the actual fault state diagnosis of the ship [48]. For different types of ship monitoring data such as temperature, pressure, Wang et al apply the normalization method to reduce the amplitude difference between the data, then combined PCA and BP neural networks to identify diesel engine failure modes [49].…”

Section: Data Fusionmentioning

confidence: 99%

Marine Systems and Equipment Prognostics and Health Management: A Systematic Review from Health Condition Monitoring to Maintenance Strategy

et al. 2022

View full text Add to dashboard Cite

Prognostics and health management (PHM) is an essential means to optimize resource allocation and improve the intelligent operation and maintenance (O&M) efficiency of marine systems and equipment (MSAE). PHM generally consists of four technical processes, namely health condition motoring (HCM), fault diagnosis (FD), health prognosis (HP), and maintenance decision (MD). In recent years, a large amount of research has been implemented in each process. However, there is not any systematic review that covers the technical framework comprehensively. This article presents a review of the framework of PHM in the marine field to fill the gap. First, the essential HCM methods, which are widely observed in the academic literature, are introduced systematically. Then, the commonly used FD approaches and their applications in MSAE are summarized, and the implementation process of intelligent methods is systematically introduced. After that, the technologies of HP have been reviewed, including the construction of health indicator (HI), health stage (HS) division, and popular remaining useful life (RUL) prediction approaches. Afterwards, the evolution of maintenance strategy in the maritime field is reviewed. Finally, the challenges of implementing PHM for intelligent ships are put forward.

show abstract

“…The timefrequency window can expand the research scope in the time domain and frequency domain at the same time, which is convenient for better problem discovery. This property makes it applicable to nonlinear sciences such as differential equations, pattern recognition, and computer vision [9][10][11].…”

Section: Wnnmentioning

confidence: 99%

Analysis of Wavelet Neural Network Algorithm in Inverter Fault Diagnosis

2022

IJOMAM

View full text Add to dashboard Cite

The frequency conversion speed regulation system powered by inverter is widely used in various fields. It is particularly important that the inverter that is most prone to failure can quickly and accurately locate the fault location and analyze the cause of the fault. The Wavelet Neural Network (WNN) is used as the entry point, and the fault diagnosis of the inverter is studied. Firstly, the Back Propagation (BP) algorithm commonly used in WNN is analyzed. A variable learning rate factor is introduced, and a variable learning rate variable weighted recursive least squares algorithm is proposed. Then, the relevant parameters are set. The inverter fault diagnosis simulation experiment is designed. Experiments show that the improved Learning Rate and Variable Weight Recursive Least Squares (LWRLS) algorithm completes fitting 1200-1300 steps faster than the BP algorithm in terms of operation speed. In terms of network error, the error value of the WNN of the BP algorithm can only reach 104. The error of the WNN of the LWRLS algorithm reaches 106. Therefore, when the algorithm is improved, the network error of WNN is reduced a lot. In the fault diagnosis accuracy, the average accuracy of the BP algorithm is 98.37%, and the standard deviation is 1.2571. The average accuracy of the LWRLS algorithm is 99.56% with a standard deviation of 0.3920. The fault recognition rate after the improved algorithm is significantly better, and the stability is better. Theoretical analysis and simulation results show that the improved LWRLS algorithm in WNN has the advantages of fast operation speed, small network error and high diagnostic accuracy in the inverter fault diagnosis method. The proposed improved WNN algorithm provides a reference for the application of inverter fault diagnosis.

show abstract

Ship engine detection based on wavelet neural network and FPGA image scanning

Cited by 12 publications

References 20 publications

A Review of Real-Time Fault Diagnosis Methods for Industrial Smart Manufacturing

A Review of Real-Time Fault Diagnosis Methods for Industrial Smart Manufacturing

Marine Systems and Equipment Prognostics and Health Management: A Systematic Review from Health Condition Monitoring to Maintenance Strategy

Analysis of Wavelet Neural Network Algorithm in Inverter Fault Diagnosis

Contact Info

Product

Resources

About