Fault Detection of Reciprocating Compressor Valve Based on One-Dimensional Convolutional Neural Network

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

doi:10.1155/2020/8058723

Cited by 8 publications

(6 citation statements)

References 22 publications

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“…Four recognition modules, audio processing, feature extraction, pattern classification, and semantic representation, are proposed according to the pitch, melody, rhythm, tone, and beat of Huaer melody. BP neural network method [29][30][31][32][33] is proposed for feature recognition of Huaer music in this study, and the main steps of extracting Huaer music features by BP neural network algorithm are introduced as follows. BP neural network is a multilayer feedforward neural network.…”

Section: Huaer Recognition Pathmentioning

confidence: 99%

Intangible Cultural Heritage Management Using Machine Learning Model: A Case Study of Northwest Folk Song Huaer

Song

2022

Scientific Programming

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Machine learning is the core field of artificial intelligence. It provides a guarantee for the digital intangible cultural heritage management of artificial intelligence. However, the existing theoretical and practical research points out that there are still many gaps in this field. Huaer is a folk song popular in Qinghai, Gansu, Ningxia Hui Autonomous Region, and individual regions of Xinjiang. It is known as the soul of the northwest. It is a national human intangible cultural heritage. It was listed as human intangible cultural heritage by the United Nations in September 2009. With the rapid development of network technology and machine learning, it is very important to manage the network communication and deep mining of Huaer information. In this regard, use of machine learning natural language processing to mine the information of Huaer lyrics is proposed. By constructing the Huaer model of recurrent neural network (RNN), data mining of Huaer lyrics is carried out, and the built-in language module in Python is interconnected with dynamic Web pages. Four Huaer image segmentation methods and five deep learning algorithms are proposed, and the steps of image segmentation algorithm and BP neural network algorithm based on block technology are introduced. The results can provide new ideas for the protection and inheritance of music intangible cultural heritage and provide effective and high-quality information for Huaer art researchers and lovers.

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Section: Huaer Recognition Pathmentioning

confidence: 99%

Intangible Cultural Heritage Management Using Machine Learning Model: A Case Study of Northwest Folk Song Huaer

Song

2022

Scientific Programming

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“…CNN was first applied to image recognition technology. It has the features of local connection, weight sharing and down-sampling, which greatly reduces the scale of the network structure, and can make full use of the local features of the data itself to improve the computing efficiency [ 27 , 28 ]. A typical CNN includes a convolution layer, pooling layer, full connected layer, and output layer [ 29 ].…”

Section: One-dimensional Convolutional Neural Network (1-dcnn)mentioning

confidence: 99%

Acoustic Emission Signal Fault Diagnosis Based on Compressed Sensing for RV Reducer

Yang

Liu

et al. 2022

Sensors

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The rotate vector (RV) reducer has a complex structure and highly coupled internal components. Acoustic emission (AE) signal, which is more sensitive to a weak fault, is selected for fault diagnosis of the RV reducer. The high sampling frequency and big data are the challenges for AE signal store and analysis. This study combines compressed sensing (CS) and convolutional neural networks. As a result, data redundancy is significantly reduced while retaining most of the information, and the analysis efficiency is improved. Firstly, the time-domain AE signal was projected into the compression domain to obtain the compression signal; then, the wavelet packet decomposition in the compressed domain was performed to obtain the information of each frequency band. Next, the frequency band information was sent into the input layer of the multi-channel convolutional layer, and the energy pooling layer mines the energy characteristics of each frequency band. Finally, the softmax classifier was used to classify and predict different fault types of RV reducers. The self-fabricated RV reducer experimental platform was used to verify the proposed method. The experimental results show that the proposed method can effectively extract the fault features in the AE signal of the RV reducer, improve the efficiency of signal processing and analysis, and achieve the accurate classification of RV reducer faults.

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“…Among all these approaches, the CNN and DBN approaches prevail in CFD applications. For example, Guo et al [15] proposed a one-dimensional convolutional neural network (1DCNN) based compressor fault diagnosis model which took the differential pressure and temperature of each compressor stage as the input of 1DCNN. Using the characteristics of the CNN, the model automatically extracted features and classified various faults.…”

Section: Literature Reviewmentioning

confidence: 99%

Compressor Fault Diagnosis Knowledge: A Benchmark Dataset for Knowledge Extraction From Maintenance Log Sheets Based on Sequence Labeling

et al. 2021

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Compressor fault diagnosis requires expert knowledge. Using the sequence labeling technology, this expert knowledge can be automatically extracted from compressor maintenance log sheets. Previous studies indicate that sequence labeling methods often need a substantial amount of annotation data for knowledge extraction, Unfortunately, the annotation data are very scarce in the field of compressor fault diagnosis. In this paper, we introduce a benchmark dataset for extraction of knowledge suitable for air compressor fault diagnosis. First, we collected 11,418 pieces of information from air compressor maintenance log sheets. Fault description, service requests, causes and troubleshooting solutions were stored in a dataset for data preprocessing and masking. In addition, 6196 valid text pairs were developed after the "noises" in the raw dataset were cleaned. Second, five kinds of entities and sequences, such as equipment, faults, service requests, causes and troubleshooting solutions, were annotated by three subject experts. The annotation consistency was assessed with F1 scores. Furthermore, our proposed baseline model (or the BERT-BI-LSTM-CRF model) was compared against other five sequence labeling models (BI-LSTM-CRF, Lattice LSTM, BERT NER, ZEN, and ERNIE). The BERT-BI-LSTM-CRF model gives superior performance in extracting expert knowledge from the subject dataset. Although the baseline model is not the most cuttingedge model in the sequence labeling and named entity recognition fields, it indeed presents a great potential for compressor fault diagnosis. The dataset is available at https://github.com/chentao1999/CFDK.

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Fault Detection of Reciprocating Compressor Valve Based on One-Dimensional Convolutional Neural Network

Cited by 8 publications

References 22 publications

Intangible Cultural Heritage Management Using Machine Learning Model: A Case Study of Northwest Folk Song Huaer

Intangible Cultural Heritage Management Using Machine Learning Model: A Case Study of Northwest Folk Song Huaer

Acoustic Emission Signal Fault Diagnosis Based on Compressed Sensing for RV Reducer

Compressor Fault Diagnosis Knowledge: A Benchmark Dataset for Knowledge Extraction From Maintenance Log Sheets Based on Sequence Labeling

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