Residual life prediction for complex systems with multi-phase degradation by ARMA-filtered hidden Markov model

2017 ◽  
Vol 16 (1) ◽  
pp. 19-35 ◽  
Author(s):  
Zhidong Sheng ◽  
Qingpei Hu ◽  
Jian Liu ◽  
Dan Yu
Keyword(s):  
Complex Systems ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Life Prediction ◽  
Residual Life ◽  
Hidden Markov ◽  
Residual Life Prediction ◽  
Multi Phase

scholarly journals Multi-Layer Hidden Markov Model Based Intrusion Detection System

2018 ◽  
Vol 1 (1) ◽  
pp. 265-286 ◽  
Author(s):  
Wondimu Zegeye ◽  
Richard Dean ◽  
Farzad Moazzami
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Detection System ◽  
Traffic Monitoring ◽  
Machine Learning Algorithms ◽  
Full Potential ◽  
Multi Phase

The all IP nature of the next generation (5G) networks is going to open a lot of doors for new vulnerabilities which are going to be challenging in preventing the risk associated with them. Majority of these vulnerabilities might be impossible to detect with simple networking traffic monitoring tools. Intrusion Detection Systems (IDS) which rely on machine learning and artificial intelligence can significantly improve network defense against intruders. This technology can be trained to learn and identify uncommon patterns in massive volume of traffic and notify, using such as alert flags, system administrators for additional investigation. This paper proposes an IDS design which makes use of machine learning algorithms such as Hidden Markov Model (HMM) using a multi-layer approach. This approach has been developed and verified to resolve the common flaws in the application of HMM to IDS commonly referred as the curse of dimensionality. It factors a huge problem of immense dimensionality to a discrete set of manageable and reliable elements. The multi-layer approach can be expanded beyond 2 layers to capture multi-phase attacks over longer spans of time. A pyramid of HMMs can resolve disparate digital events and signatures across protocols and platforms to actionable information where lower layers identify discrete events (such as network scan) and higher layers new states which are the result of multi-phase events of the lower layers. The concepts of this novel approach have been developed but the full potential has not been demonstrated.

scholarly journals A New Residual Life Prediction Method for Complex Systems Based on Wiener Process and Evidential Reasoning

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Xin Zhang ◽  
Shuaiwen Tang ◽  
Taoyuan Liu ◽  
Bangcheng Zhang
Keyword(s):  
Complex Systems ◽  
Wiener Process ◽  
Life Prediction ◽  
Residual Life ◽  
Fuzzy Theory ◽  
Prediction Method ◽  
Parameters Estimation ◽  
Evidential Reasoning ◽  
Likelihood Method ◽  
Residual Life Prediction

A new residual life prediction method for complex systems based on Wiener process and evidential reasoning is proposed to predict the residual life of complex systems effectively. Moreover, the better maintenance strategies and decision supports are provided. For the residual life prediction of complex systems, the maximum likelihood method is adopted to estimate the drift coefficient, and the Bayesian method is adopted to update the parameters of Wiener process. The process of parameters estimation and the probability density function (PDF) of the residual life are deduced. To improve the accuracy of the residual life prediction results, the evidential reasoning (ER) is used to integrate the prediction results of Wiener process. Finally, a case study of gyroscope is examined to illustrate the feasibility and effectiveness of the proposed method, compared with fuzzy theory, which provides an important reference for the optimization of the reliability of complex systems and improvement.

scholarly journals Research on Residual Life Prediction for Electrical Connectors Based on Intermittent Failure and Hidden Semi-Markov Model

2018 ◽  
Vol 8 (8) ◽  
pp. 1373 ◽  
Author(s):  
Qian Li ◽  
Kehong Lv ◽  
Jing Qiu ◽  
Guanjun Liu
Keyword(s):  
Markov Model ◽  
Life Prediction ◽  
Evaluation Method ◽  
Residual Life ◽  
Dynamic Properties ◽  
Prediction Method ◽  
Electrical Connector ◽  
Residual Life Prediction ◽  
Intermittent Failure ◽  
Electrical Connectors

Based on the dynamic properties of electrical connector intermittent failure, the model and methods for residual life prediction for electrical connectors are studied in this paper. Firstly, the mechanism of electrical connector intermittent failure is analyzed, and the area enclosed by the contact resistance curve and the fault threshold is defined as the generalized severity of intermittent failure to describe how severe the electrical connector’s intermittent failure is. Then, the Hidden Semi-Markov Model (HSMM) is introduced to build the residual life prediction model of the electrical connector. Further, the evaluation method of using the state and prediction method for residual life are studied. Finally, by carrying out the residual life prediction test, the effectiveness of the residual life prediction method for electrical connectors based on intermittent failure and HSMM is verified.

Hidden Markov Model for Health Estimation and Prognosis of Turbofan Engines

2011 ◽  
Author(s):  
Andrea Giantomassi ◽  
Francesco Ferracuti ◽  
Alessandro Benini ◽  
Gianluca Ippoliti ◽  
Sauro Longhi ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Prediction Models ◽  
Residual Life ◽  
Hidden Markov ◽  
Life Time ◽  
Turbofan Engines ◽  
Useful Life ◽  
Determinant Factor

Determining the residual life time of systems is a determinant factor for machinery and environment safety. In this paper the problem of estimate the residual useful life (RUL) of turbo-fan engines is addressed. The adopted approach is especially suitable for situations in which a large amount of data is available offline, by allowing the processing of such data for the determination of RUL. The procedure allows to calculate the RUL through the following steps: features extraction by Artificial Neural Networks (ANN) and determination of remaining life time by-prediction models based on a Hidden Markov Model (HMM). Simulations confirm the effectiveness of the proposed approach and the promising power of Bayesian methods.

scholarly journals Model parameter estimation and residual life span prediction of pneumatic diaphragm pump based on hidden Markov model in intelligent spraying

2019 ◽  
Vol 16 (5) ◽  
pp. 172988141987463
Author(s):  
Liangwen Yan ◽  
Jiale Chen ◽  
Peng Yu ◽  
Yue Yu ◽  
Kele Cao ◽  
...  
Keyword(s):  
Health Status ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Life Span ◽  
Continuous Time ◽  
Residual Life ◽  
Hidden Markov ◽  
Model Parameters ◽  
Diaphragm Pump ◽  
State 1

Pneumatic diaphragm pump is an important part in intelligent spraying. When pneumatic diaphragm pump does not work normally, the entire intelligent spraying product line will be malfunctioned. To maintain and manage pneumatic diaphragm pump effectively, the grade analysis of the health status of pneumatic diaphragm pump is generally used according to its working state. Due to the effects of condition monitoring and random faults, some observable health predictions are often inaccurate. There are very few papers dealing with the health monitoring of pneumatic diaphragm pump and their estimation of residual life span. In this article, a method with vector autoregressive model and continuous-time hidden Markov model was proposed to analyze and evaluate the life span of pneumatic diaphragm pump based on the estimation error of the health condition and the cumulative deterioration of pneumatic diaphragm pump. It is modeled through a continuous-time Markov chain with three states, which includes unobservable healthy state 0, unobservable warning state 1, and observable fault state 2. The expectation–maximization algorithm is used to estimate the model parameters of the fitted hidden Markov. Through the posterior probability of pneumatic diaphragm pump in warning state 1, the derived conditional reliability function and mean residual life span formula can be calculated to evaluate the residual life span of pneumatic diaphragm pump. The results showed that the method can effectively predict the residual life span of pneumatic diaphragm pump, illustrate the effectiveness of the model, and improve the accuracy of the health status rating.

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