Analysis of signals from air conditioner compressors with ordinal patterns and machine learning
Most machines are equipped with devices that monitor their operation. Air conditioners, in particular, are routinely monitored through various measurements. A desirable outcome of this monitoring is identifying when the device will likely require maintenance. In this study, we present the use of Ord...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2025-03-01
|
| Series: | Journal of Low Frequency Noise, Vibration and Active Control |
| Online Access: | https://doi.org/10.1177/14613484241287620 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850034165846638592 |
|---|---|
| author | Keila Barbosa Alejandro C Frery George DC Cavalcanti |
| author_facet | Keila Barbosa Alejandro C Frery George DC Cavalcanti |
| author_sort | Keila Barbosa |
| collection | DOAJ |
| description | Most machines are equipped with devices that monitor their operation. Air conditioners, in particular, are routinely monitored through various measurements. A desirable outcome of this monitoring is identifying when the device will likely require maintenance. In this study, we present the use of Ordinal Patterns, a symbolic transformation of time series, which allows for the visual assessment of the type of operation. Ordinal Patterns are chosen because they can transform intricate time series into simple and intuitive symbolic representations. The technique is visually appealing, generating points on a plane whose positions reveal hidden dynamics. This approach makes it easier to identify recurring or abnormal patterns in machine operations that may indicate wear or impending failure. Additionally, Ordinal Patterns allow for precise and understandable visualization of operational data, making interpreting results more accessible for professionals who may not be experts in data analysis. We compare two machines under different operational conditions with six measured variables. We analyze the expressiveness of the Ordinal Patterns and identify those variables that best differentiate the two machines. Furthermore, we incorporate machine learning algorithms, such as Artificial Neural Networks, Support Vector Machines, and Decision Trees, to evaluate and validate the effectiveness of Ordinal Patterns as discriminative features. Integrating machine learning methods with a symbolic transformation offers a robust approach for the early and accurate diagnosis of potential failures, enhancing the predictive maintenance of air conditioning equipment. |
| format | Article |
| id | doaj-art-7db5a1c96986441bbe299f46f37e3d8e |
| institution | DOAJ |
| issn | 1461-3484 2048-4046 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Journal of Low Frequency Noise, Vibration and Active Control |
| spelling | doaj-art-7db5a1c96986441bbe299f46f37e3d8e2025-08-20T02:57:55ZengSAGE PublishingJournal of Low Frequency Noise, Vibration and Active Control1461-34842048-40462025-03-014410.1177/14613484241287620Analysis of signals from air conditioner compressors with ordinal patterns and machine learningKeila BarbosaAlejandro C FreryGeorge DC CavalcantiMost machines are equipped with devices that monitor their operation. Air conditioners, in particular, are routinely monitored through various measurements. A desirable outcome of this monitoring is identifying when the device will likely require maintenance. In this study, we present the use of Ordinal Patterns, a symbolic transformation of time series, which allows for the visual assessment of the type of operation. Ordinal Patterns are chosen because they can transform intricate time series into simple and intuitive symbolic representations. The technique is visually appealing, generating points on a plane whose positions reveal hidden dynamics. This approach makes it easier to identify recurring or abnormal patterns in machine operations that may indicate wear or impending failure. Additionally, Ordinal Patterns allow for precise and understandable visualization of operational data, making interpreting results more accessible for professionals who may not be experts in data analysis. We compare two machines under different operational conditions with six measured variables. We analyze the expressiveness of the Ordinal Patterns and identify those variables that best differentiate the two machines. Furthermore, we incorporate machine learning algorithms, such as Artificial Neural Networks, Support Vector Machines, and Decision Trees, to evaluate and validate the effectiveness of Ordinal Patterns as discriminative features. Integrating machine learning methods with a symbolic transformation offers a robust approach for the early and accurate diagnosis of potential failures, enhancing the predictive maintenance of air conditioning equipment.https://doi.org/10.1177/14613484241287620 |
| spellingShingle | Keila Barbosa Alejandro C Frery George DC Cavalcanti Analysis of signals from air conditioner compressors with ordinal patterns and machine learning Journal of Low Frequency Noise, Vibration and Active Control |
| title | Analysis of signals from air conditioner compressors with ordinal patterns and machine learning |
| title_full | Analysis of signals from air conditioner compressors with ordinal patterns and machine learning |
| title_fullStr | Analysis of signals from air conditioner compressors with ordinal patterns and machine learning |
| title_full_unstemmed | Analysis of signals from air conditioner compressors with ordinal patterns and machine learning |
| title_short | Analysis of signals from air conditioner compressors with ordinal patterns and machine learning |
| title_sort | analysis of signals from air conditioner compressors with ordinal patterns and machine learning |
| url | https://doi.org/10.1177/14613484241287620 |
| work_keys_str_mv | AT keilabarbosa analysisofsignalsfromairconditionercompressorswithordinalpatternsandmachinelearning AT alejandrocfrery analysisofsignalsfromairconditionercompressorswithordinalpatternsandmachinelearning AT georgedccavalcanti analysisofsignalsfromairconditionercompressorswithordinalpatternsandmachinelearning |