Журнал Российского общества по неразрушающему контролю и технической диагностике
The journal of the Russian society for non-destructive testing and technical diagnostic
 
| Русский Русский | English English |
 
Главная Archive
18 | 11 | 2024
2024, 02 February

DOI: 10.14489/td.2024.02.pp.056-062

Efanov V. N., Ivanova N. S.
DIAGNOSTICS OF THE STATE OF COMPLEX TECHNICAL OBJECTS ON THE BASIS OF DETECTION OF ANOMALIES OF TIME SEQUENCES
(pp. 56-62)

Abstract. The problem of estimating the state of complex technical objects based on the analysis of time sequence anomalies is considered. The purpose of this research is to develop a method of detecting contextual anomalies of time sequences, which allows to determine the degree of development of degradation processes that lead to failures. The study of modern intellectual means of analyzing time sequences of high dimensionality is carried out. It is shown that contextual anomalies are of the greatest interest from the point of view of technical objects state estimation. We propose a spectral method for analyzing context anomalies. In this case, unlike the known spectral methods, a special basis of exponential functions is used. The methodology of calculating spectral coefficients of the investigated time series is described, on the basis of which a generalized attribute is calculated, allowing to attribute the case under study to a normal or anomalous group. An example of gas turbine engine state estimation with a possible defect of turbocharger rotor is given.

Keywords: complex technical objects, state diagnostics, anomalies, spectral method.

V. N. Efanov, N. S. Ivanova (Ufa University of Science and Technology, Ufa, Russia) E-mail: Данный адрес e-mail защищен от спам-ботов, Вам необходимо включить Javascript для его просмотра. , Данный адрес e-mail защищен от спам-ботов, Вам необходимо включить Javascript для его просмотра.  

1. Equipment maintenance and repair system. Terms and definitions: national standard of the Russian Federation. (2017). Standard No. GOST 18322–2016. Moscow: Standartinform. [in Russian language]
2. Gupta M., Gao J., Aggarwal C. C., Han J. (2014). Outlier Detection for Temporal Data: A Survey. IEEE Transactions on Knowledge and Data Engineering, 26(9), 2250 – 2267. DOI: 10.1109/TKDE.2013.184
3. Cai X., Aydin B., Maydeo S. et al. (2020). Local Outlier Detection for Multi-Type Spatio-Temporal Trajectories. IEEE International Conference on Big Data, 4509 – 4518. Atlanta. DOI: 10.1109/BigData50022.2020.9377801
4. Yu K., Shi W., Santoro N., Ma X. (2019). Real-Time Outlier Detection Over Streaming Data. 2019 IEEE SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI, 125 – 132. Leicester. DOI: 10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00063
5. Cao K., Liu Y., Meng G. et al. (2020). Trajectory Outlier Detection on Trajectory Data Streams. IEEE Access, 8, 34187 – 34196. DOI: 10.1109/ACCESS.2020.2974521
6. Cai X., Aydin B., Ji A., Angryk R. (2021). A Framework for Local Outlier Detection from Spatio-Temporal Trajectory Datasets. 25th International Conference on Pattern Recognition (ICPR), 5682 – 5689. Milan. DOI: 10.1109/ICPR48806.2021.9412274
7. Wang Y., Qin K., Sun H., Lu B. (2021). Spatial-Temporal Analysis of High Plateau Flight Turning Procedure Exceptions Based on QAR Data. IEEE 3rd International Conference on Civil Aviation Safety and Information Technology (ICCASIT), 62 – 64. Changsha. DOI: 10.1109/ICCASIT53235.2021.9633729
8. Song Y., Yu J., Tang D. et al. (2020). Telemetry Data-Based Spacecraft Anomaly Detection Using Generative Adversarial Networks. International Conference on Sensing, Measurement & Data Analytics in the Era of Artificial Intelligence (ICSMD), 297 – 301. Xi'an. DOI: 10.1109/ICSMD50554.2020.9261736
9. Pu J., Wang Y., Liu X., Zhang X. (2019). STLP-OD: Spatial and Temporal Label Propagation for Traffic Outlier Detection. IEEE Access, 7, 63036 – 63044. DOI: 10.1109/ACCESS.2019.2916853
10. Fitters W., Cuzzocrea A., Hassani M. (2021). Enhancing LSTM Prediction of Vehicle Traffic Flow Data via Outlier Correlations. IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), 210 – 217. Madrid. DOI: 10.1109/COMPSAC51774.2021.00039
11. Al Samara M., Bennis I., Abouaissa A., Lorenz P. (2021). An Efficient Outlier Detection and Classification Clustering-Based Approach for WSN. IEEE Global Communications Conference (GLOBECOM), 1 – 6. Madrid. DOI: 10.1109/GLOBECOM46510.2021.9685756
12. Haj-Hassan A., Habib C., Nassar J. (2020). Real-Time Spatio-Temporal Based Outlier Detection Framework for Wireless Body Sensor Networks. IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), 1 – 6. New Delhi. DOI: 10.1109/ANTS50601.2020.9342827.
13. Al Samara M., Bennis I., Abouaissa A., Lorenz P. (2022). OPTICS-Based Outlier Detection with Newton Classification. International Wireless Communications and Mobile Computing (IWCMC), 784 – 789. Dubrovnik. DOI: 10.1109/IWCMC55113.2022.9825224
14. Zhang H., Li Z. (2019). Anomaly Detection Approach for Urban Sensing Based on Credibility and Time-Series Analysis Optimization Model. IEEE Access, 7, 49102 – 49110. DOI: 10.1109/ACCESS.2019.2909967
15. Mo R., Pay Y., Venkatarayalu N.V. et al. (2023). Unsupervised TCN-AE-Based Outlier Detection for Time Series with Seasonality and Trend for Cellular Networks. IEEE Transactions on Wireless Communications, 22(5), 3114 – 3127. DOI: 10.1109/TWC.2022.3216004
16. Dridi A., Boucetta C., Hammami S.E. et al. (2021). STAD: Spatio-Temporal Anomaly Detection Mechanism for Mobile Network Management. IEEE Transactions on Network and Service Management, 18(1), 894 – 906. DOI: 10.1109/TNSM.2020.3048131
17. Mo R., Pei Y., Venkatarayalu N. et al. (2021). An Unsupervised TCN-Based Outlier Detection for Time Series with Sea-sonality and Trend. IEEE VTS 17th Asia Pacific Wireless Communications Symposium (APWCS), 1 – 5. Osaka. DOI: 10.1109/APWCS50173.2021.9548759
18. Borah A., Gruenwald L., Leal E., Panjei E. (2021). A GPU Algorithm for Detecting Contextual Outliers in Multiple Concurrent Data Streams. IEEE International Conference on Big Data (Big Data), 2737 – 2742. Orlando. DOI: 10.1109/BigData52589.2021.9671460
19. Lu W., Cheng Y., Xiao C. et al. (2017). Unsupervised Sequential Outlier Detection with Deep Architectures. IEEE Transactions on Image Processing, 26(9), 4321 – 4330. DOI: 10.1109/TIP.2017.2713048
20. Suetin P. K. (2007). Classical orthogonal polynomials. Moscow: Fizmatlit. [in Russian language]
21. Nikiforov A. F., Suslov S. K. (1985). Classical orthogonal polynomials. Moscow: Znanie. [in Russian language]

This article  is available in electronic format (PDF).

The cost of a single article is 500 rubles. (including VAT 20%). After you place an order within a few days, you will receive following documents to your specified e-mail: account on payment and receipt to pay in the bank.

After depositing your payment on our bank account we send you file of the article by e-mail.

To order articles please copy the article doi:

10.14489/td.2024.02.pp.056-062

and fill out the  form  

 

 

 
Search
Rambler's Top100 Яндекс цитирования