Fault Prediction for Nonlinear System Using Sliding ARMA Combined with Online LS-SVR

Su, Shengchao; Zhang, Wei; Zhao, Shenghui

doi:10.1155/2014/692848

Cited by 7 publications

(6 citation statements)

References 28 publications

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“…In a similar manner, Su et. al used in (Su, Zhang, & Zhao, 2014) least squares support vector regression with sliding ARMA forecasting to model the non-linear time series. They demonstrate their method on a practical case study for the US-made F-16 fighter.…”

Section: Applications Of Prognostics Data-driven Methods In the Aerospace Industrymentioning

confidence: 99%

A Review: Prognostics and Health Management in Automotive and Aerospace

Nguyen

Kefalas

Yang

et al. 2019

IJPHM

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Prognostics and Health Management (PHM) attracts increasing interest of many researchers due to its potentially important applications in diverse disciplines and industries. In general, PHM systems use real-time and historical state information of subsystems and components of the operating systems to provide actionable information, enabling intelligent decision-making for improved performance, safety, reliability, and maintainability. Every year, a substantial number of papers in this area including theory and practical applications, appear in academic journals, conference proceedings and technical reports. This paper aims to summarize and review researches, developments and recent contributions in PHM for automotive and aerospace industries. It can also be considered as the starting point for researchers and practitioners in general to assist them through PHM implementation and help them to accomplish their work more easily.

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Section: Applications Of Prognostics Data-driven Methods In the Aerospace Industrymentioning

confidence: 99%

A Review: Prognostics and Health Management in Automotive and Aerospace

Nguyen

Kefalas

Yang

et al. 2019

IJPHM

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“…Zhang and Zhao [ 30 ] proposed an online fault prediction model for a nonlinear system. The model was developed by combining sliding autoregressive moving average (ARMA) modeling with online least squares support vector regression (LS-SVR) compensation.…”

Section: Literature Reviewmentioning

confidence: 99%

Incremental Ant-Miner Classifier for Online Big Data Analytics

Al-Dawsari

Al-Turaiki

Kurdi

2022

Sensors

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Internet of Things (IoT) environments produce large amounts of data that are challenging to analyze. The most challenging aspect is reducing the quantity of consumed resources and time required to retrain a machine learning model as new data records arrive. Therefore, for big data analytics in IoT environments where datasets are highly dynamic, evolving over time, it is highly advised to adopt an online (also called incremental) machine learning model that can analyze incoming data instantaneously, rather than an offline model (also called static), that should be retrained on the entire dataset as new records arrive. The main contribution of this paper is to introduce the Incremental Ant-Miner (IAM), a machine learning algorithm for online prediction based on one of the most well-established machine learning algorithms, Ant-Miner. IAM classifier tackles the challenge of reducing the time and space overheads associated with the classic offline classifiers, when used for online prediction. IAM can be exploited in managing dynamic environments to ensure timely and space-efficient prediction, achieving high accuracy, precision, recall, and F-measure scores. To show its effectiveness, the proposed IAM was run on six different datasets from different domains, namely horse colic, credit cards, flags, ionosphere, and two breast cancer datasets. The performance of the proposed model was compared to ten state-of-the-art classifiers: naive Bayes, logistic regression, multilayer perceptron, support vector machine, K*, adaptive boosting (AdaBoost), bagging, Projective Adaptive Resonance Theory (PART), decision tree (C4.5), and random forest. The experimental results illustrate the superiority of IAM as it outperformed all the benchmarks in nearly all performance measures. Additionally, IAM only needs to be rerun on the new data increment rather than the entire big dataset on the arrival of new data records, which makes IAM better in time- and resource-saving. These results demonstrate the strong potential and efficiency of the IAM classifier for big data analytics in various areas.

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“…19 In addition, ARMA is also affected by modeling errors, parameter perturbation and external disturbance, so the robustness is unsatisfactory. 20 In most cases, the time series are non-stationary; therefore, ARIMA is put forward to compensate for the above shortcomings. For non-stationary time series, the basic idea of ARIMA model is to use several difference operations to make it become a stationary series, 21 the number of difference is d .…”

Section: Introductionmentioning

confidence: 99%

State prediction for the actuators of civil aircraft based on a fusion framework of relevance vector machine and autoregressive integrated moving average

Guo

Wang

Zhang

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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Relevance vector machine is a newly proposed and effective state prediction algorithm proved by practical applications; however, the accuracy of the single relevance vector machine model for the long-term prediction is unable to achieve satisfactory results with time goes by. Then, an autoregressive integrated moving average model is introduced to correct the prediction error caused by the single relevance vector machine, and a fusion framework based on the combination of relevance vector machine and autoregressive integrated moving average model is adopted to improve the accuracy of long-term prediction. In addition, a targeted approach for retraining the old model is put forward so that the state prediction model can be updated in time and suits the actual situation better. The effectiveness of the proposed fusion framework is illustrated via an aircraft actuator, and the experiments based on a model of civil aircraft actuator data set show that the proposed method yields a satisfied performance in state prediction of aircraft actuators.

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Fault Prediction for Nonlinear System Using Sliding ARMA Combined with Online LS-SVR

Cited by 7 publications

References 28 publications

A Review: Prognostics and Health Management in Automotive and Aerospace

A Review: Prognostics and Health Management in Automotive and Aerospace

Incremental Ant-Miner Classifier for Online Big Data Analytics

State prediction for the actuators of civil aircraft based on a fusion framework of relevance vector machine and autoregressive integrated moving average

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