Enhanced Autoregressive Integrated Moving Average Model for Anomaly Detection in Power Plant Operations

Publish Year: 1403
نوع سند: مقاله ژورنالی
زبان: English
View: 27

This Paper With 9 Page And PDF Format Ready To Download

  • Certificate
  • من نویسنده این مقاله هستم

استخراج به نرم افزارهای پژوهشی:

لینک ثابت به این Paper:

شناسه ملی سند علمی:

JR_IJE-37-8_018

تاریخ نمایه سازی: 23 خرداد 1403

Abstract:

This study introduces an Enhanced Autoregressive Integrated Moving Average (E-ARIMA) model for anomaly detection in time-series data, using vibrations monitored by CA ۲۰۲ accelerometers at the Kirkuk Gas Power Plant as a case study. The objective is to overcome the limitations of traditional ARIMA models in analyzing the non-linear and dynamic nature of industrial sensory data. The novel proposed methodology includes data preparation through linear interpolation to address dataset gaps, stationarity confirmation via the Augmented Dickey-Fuller Test, and ARIMA model optimization against the Akaike Information Criterion, with a specialized time-series cross-validation technique. The results show that E-ARIMA model has superior performance in anomaly detection compared to conventional Seasonal ARIMA (SARIMA) and Vector Autoregressive models. In this regard, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) criteria were utilized for this evaluation. Finally, the most important achievement of this research is that the results highlight the enhanced predictive accuracy of the E-ARIMA model, making it a potent tool for industrial applications such as machinery health monitoring, where early detection of anomalies is crucial to prevent costly downtimes and facilitate maintenance planning.

Keywords:

Vibration monitoring , Time-series data , Anomaly Detection , Autoregressive integrated moving average , Gas power plant

Authors

A. T. W. Khalid Fahmi

Department of Mechanical Engineering, Academy of Engineering, RUDN University, Moscow, Russian Federation

K. R. Kashyzadeh

Department of Transport Equipment and Technology, Academy of Engineering, RUDN University, Moscow, Russian Federation

S. Ghorbani

Department of Mechanical Engineering, Academy of Engineering, RUDN University, Moscow, Russian Federation

مراجع و منابع این Paper:

لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :
  • Jahanshahi H, Cevik M, Başar A, editors. Predicting the number ...
  • Ξανθοπούλου ΓΒ. Forecasting power output of photovoltaic systems using machine ...
  • Dorrani Z. Traffic Scene Analysis and Classification using Deep Learning. ...
  • Al Mallah R, Quintero A, Farooq B. Prediction of traffic ...
  • Pan B, Demiryurek U, Shahabi C, editors. Utilizing real-world transportation ...
  • Ilu SY, Prasad R. Time series analysis and prediction of ...
  • Pastén D, Czechowski Z, Toledo B. Time series analysis in ...
  • Vogel E, Saravia G, Pastén D, Muñoz V. Time-series analysis ...
  • Moustra M, Avraamides M, Christodoulou C. Artificial neural networks for ...
  • Fahmi A-TWK, Kashyzadeh KR, Ghorbani S. A comprehensive review on ...
  • Poullikkas A. An overview of current and future sustainable gas ...
  • Tanaka K, Cavalett O, Collins WJ, Cherubini F. Asserting the ...
  • Meher-Homji CB, Chaker M, Bromley AF, editors. The fouling of ...
  • De Michelis C, Rinaldi C, Sampietri C, Vario R. Condition ...
  • Mourad A-HI, Almomani A, Sheikh IA, Elsheikh AH. Failure analysis ...
  • Mevissen F, Meo M. A review of NDT/structural health monitoring ...
  • Qaiser MT. Data Analysis and Prediction of Turbine Failures Based ...
  • Yan W, Yu L. On accurate and reliable anomaly detection ...
  • Zhong S-s, Fu S, Lin L. A novel gas turbine ...
  • Zhou D, Yao Q, Wu H, Ma S, Zhang H. ...
  • Kontopoulou VI, Panagopoulos AD, Kakkos I, Matsopoulos GK. A review ...
  • Fahmi A-TWK, Kashyzadeh KR, Ghorbani S. Fault detection in the ...
  • Paparoditis Efstathios E, Politis DN. The asymptotic size and power ...
  • Mushtaq R. Augmented dickey fuller test. ۲۰۱۱. https://dx.doi.org/۱۰.۲۱۳۹/ssrn.۱۹۱۱۰۶ ...
  • Chodakowska E, Nazarko J, Nazarko Ł. Arima models in electrical ...
  • Kashyzadeh KR, Ghorbani S. New neural network-based algorithm for predicting ...
  • Reza Kashyzadeh K, Amiri N, Ghorbani S, Souri K. Prediction ...
  • Rezaeian N, Gurina R, Saltykova OA, Hezla L, Nohurov M, ...
  • نمایش کامل مراجع