Detection of Spike-type Stall of Axial Compressors Based on Dilated Causal Convolutional Neural Networks Article Swipe
Hongyang Zhao
,
Fuxiang Quan
,
Xia Weiguo
,
Xi‐Ming Sun
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/1693/1/012028
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/1693/1/012028
An aerodynamic instability inception and short-length-scale periodic anomaly prior to stall onset known as spike-type stall inception in axial compressors is observed in aero-engine. In this paper, a deep dilated causal convolutional neural network(CNN) named as WaveNet is applied to spike-type stall inception detection and prediction in time-series data of axial compressors. WaveNet can implement fast anomaly detection and spike-type stall prediction in long-time term series data. Furthermore, a single WaveNet can be trained to capture and learn the time-domain statistical characteristics of different spike-type stall inception training data with equal fidelity. The trained WaveNet model can rapidly detect the occurrence of anomaly point and predict the probability of rotating stall and surge in axial compressors as an early warning signal. By comparing with the time domain analysis, the calculation results are represented with experimental data to show the effectiveness and feasibility of spike-type stall detection approach based on WaveNet.
Related Topics
Concepts
Stall (fluid mechanics)
Axial compressor
Computer science
Spike (software development)
Time domain
Gas compressor
Convolutional neural network
Anomaly detection
Pattern recognition (psychology)
Artificial intelligence
Control theory (sociology)
Engineering
Computer vision
Aerospace engineering
Software engineering
Control (management)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1742-6596/1693/1/012028
- OA Status
- diamond
- Cited By
- 5
- References
- 12
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3115960478
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3115960478Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/1742-6596/1693/1/012028Digital Object Identifier
- Title
-
Detection of Spike-type Stall of Axial Compressors Based on Dilated Causal Convolutional Neural NetworksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-12-01Full publication date if available
- Authors
-
Hongyang Zhao, Fuxiang Quan, Xia Weiguo, Xi‐Ming SunList of authors in order
- Landing page
-
https://doi.org/10.1088/1742-6596/1693/1/012028Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1088/1742-6596/1693/1/012028Direct OA link when available
- Concepts
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Stall (fluid mechanics), Axial compressor, Computer science, Spike (software development), Time domain, Gas compressor, Convolutional neural network, Anomaly detection, Pattern recognition (psychology), Artificial intelligence, Control theory (sociology), Engineering, Computer vision, Aerospace engineering, Software engineering, Control (management)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 2, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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12Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.of | 50, 83, 102, 109, 143 |
| abstract_inverted_index.on | 149 |
| abstract_inverted_index.to | 10, 40, 75, 137 |
| abstract_inverted_index.The | 93 |
| abstract_inverted_index.and | 5, 45, 59, 77, 105, 112, 141 |
| abstract_inverted_index.are | 132 |
| abstract_inverted_index.can | 54, 72, 97 |
| abstract_inverted_index.the | 79, 100, 107, 125, 129, 139 |
| abstract_inverted_index.data | 49, 89, 136 |
| abstract_inverted_index.deep | 29 |
| abstract_inverted_index.fast | 56 |
| abstract_inverted_index.show | 138 |
| abstract_inverted_index.term | 65 |
| abstract_inverted_index.this | 26 |
| abstract_inverted_index.time | 126 |
| abstract_inverted_index.with | 90, 124, 134 |
| abstract_inverted_index.axial | 19, 51, 115 |
| abstract_inverted_index.based | 148 |
| abstract_inverted_index.data. | 67 |
| abstract_inverted_index.early | 119 |
| abstract_inverted_index.equal | 91 |
| abstract_inverted_index.known | 13 |
| abstract_inverted_index.learn | 78 |
| abstract_inverted_index.model | 96 |
| abstract_inverted_index.named | 35 |
| abstract_inverted_index.onset | 12 |
| abstract_inverted_index.point | 104 |
| abstract_inverted_index.prior | 9 |
| abstract_inverted_index.stall | 11, 16, 42, 61, 86, 111, 145 |
| abstract_inverted_index.surge | 113 |
| abstract_inverted_index.causal | 31 |
| abstract_inverted_index.detect | 99 |
| abstract_inverted_index.domain | 127 |
| abstract_inverted_index.neural | 33 |
| abstract_inverted_index.paper, | 27 |
| abstract_inverted_index.series | 66 |
| abstract_inverted_index.single | 70 |
| abstract_inverted_index.WaveNet | 37, 53, 71, 95 |
| abstract_inverted_index.anomaly | 8, 57, 103 |
| abstract_inverted_index.applied | 39 |
| abstract_inverted_index.capture | 76 |
| abstract_inverted_index.dilated | 30 |
| abstract_inverted_index.predict | 106 |
| abstract_inverted_index.rapidly | 98 |
| abstract_inverted_index.results | 131 |
| abstract_inverted_index.signal. | 121 |
| abstract_inverted_index.trained | 74, 94 |
| abstract_inverted_index.warning | 120 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.WaveNet. | 150 |
| abstract_inverted_index.approach | 147 |
| abstract_inverted_index.observed | 22 |
| abstract_inverted_index.periodic | 7 |
| abstract_inverted_index.rotating | 110 |
| abstract_inverted_index.training | 88 |
| abstract_inverted_index.analysis, | 128 |
| abstract_inverted_index.comparing | 123 |
| abstract_inverted_index.detection | 44, 58, 146 |
| abstract_inverted_index.different | 84 |
| abstract_inverted_index.fidelity. | 92 |
| abstract_inverted_index.implement | 55 |
| abstract_inverted_index.inception | 4, 17, 43, 87 |
| abstract_inverted_index.long-time | 64 |
| abstract_inverted_index.occurrence | 101 |
| abstract_inverted_index.prediction | 46, 62 |
| abstract_inverted_index.spike-type | 15, 41, 60, 85, 144 |
| abstract_inverted_index.aerodynamic | 2 |
| abstract_inverted_index.calculation | 130 |
| abstract_inverted_index.compressors | 20, 116 |
| abstract_inverted_index.feasibility | 142 |
| abstract_inverted_index.instability | 3 |
| abstract_inverted_index.probability | 108 |
| abstract_inverted_index.represented | 133 |
| abstract_inverted_index.statistical | 81 |
| abstract_inverted_index.time-domain | 80 |
| abstract_inverted_index.time-series | 48 |
| abstract_inverted_index.Furthermore, | 68 |
| abstract_inverted_index.aero-engine. | 24 |
| abstract_inverted_index.compressors. | 52 |
| abstract_inverted_index.experimental | 135 |
| abstract_inverted_index.network(CNN) | 34 |
| abstract_inverted_index.convolutional | 32 |
| abstract_inverted_index.effectiveness | 140 |
| abstract_inverted_index.characteristics | 82 |
| abstract_inverted_index.short-length-scale | 6 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5023780872 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I27357992 |
| citation_normalized_percentile.value | 0.74842391 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |