Integration of Hidden Markov Modelling and Bayesian Network for Fault Detection and Prediction of Complex Engineered Systems

2021 ◽  
pp. 107808
Author(s):  
Morteza Soleimani ◽  
Felician Campean ◽  
Daniel Neagu
Keyword(s):  
Fault Detection ◽  
Bayesian Network ◽  
Hidden Markov ◽  
Markov Modelling ◽  
Complex Engineered Systems ◽  
Engineered Systems

Resilience Modeling and Quantification for Design of Complex Engineered Systems Using Bayesian Networks

2014 ◽  
Author(s):  
Seyedmohsen Hosseini ◽  
Nita Yodo ◽  
Pingfeng Wang
Keyword(s):  
Supply Chain ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Conceptual Framework ◽  
Electric Motor ◽  
Qualitative Assessment ◽  
Complex Engineered Systems ◽  
Engineered Systems ◽  
Quantitative Tool

The concept of engineering resilience has received prevalent attention from academia as well as industry because it contributes a new means of thinking about how to withstand against disruptions and recover properly from them. Although the concept of resilience was scholarly explored in diverse disciplines, there are only few which focus on how to quantitatively measure the engineering resilience. This paper is dedicated to explore the gap between quantitative and qualitative assessment of engineering resilience in the domain of design of complex engineered systems. A conceptual framework is first proposed for the modeling of engineering resilience, and then Bayesian network is employed as a quantitative tool for the assessment and analysis of engineering resilience for complex systems. A case study related to electric motor supply chain is employed to demonstrate the proposed approach. The proposed resilience quantification and analysis approach using Bayesian networks would empower system designers to have a better grasp of the weakness and strength of their own systems against system disruptions induced by adverse failure events.

Resilience Analysis and Allocation for Complex Systems Using Bayesian Network

2015 ◽  
Author(s):  
Nita Yodo ◽  
Pingfeng Wang
Keyword(s):  
Complex Systems ◽  
Bayesian Network ◽  
Industrial Applications ◽  
Engineering System ◽  
Supply Chain System ◽  
Complex Engineered Systems ◽  
Engineered Systems ◽  
Engineering System Design ◽  
Allocation Approach

The concept of resilience has been explored in diverse disciplines. However, there are only a few which focus on how to quantitatively measure engineering resilience and allocate resilience in engineering system design. This paper is dedicated to exploring the gap between quantitative and qualitative assessments of engineering resilience in the domain of designing complex engineered systems, thus optimally allocating resilience into subsystems and components level in industrial applications. A conceptual framework is first proposed for modeling engineering resilience, and then Bayesian Network is employed as a quantitative tool for the assessment and analysis of engineering resilience for complex systems. One industrial-based case study, a supply chain system, is employed to demonstrate the proposed approach. The proposed resilience quantification and allocation approach using Bayesian Networks would empower system designers to have a better grasp of the weakness and strength of their own systems against system disruptions induced by adverse failure events.

A hybrid hidden Markov model towards fault detection of rotating components

2016 ◽  
Vol 23 (19) ◽  
pp. 3175-3195 ◽  
Author(s):  
Ayan Sadhu ◽  
Guru Prakash ◽  
Sriram Narasimhan
Keyword(s):  
Fault Detection ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Time Scale ◽  
Hidden Markov ◽  
Energy Operator ◽  
Fault Classification ◽  
Multiple Faults ◽  
Condition Indicators ◽  
Model Based

A robust hybrid hidden Markov model-based fault detection method is proposed to perform multi-state fault classification of rotating components. The approach presented in this paper enhances the performance of the standard hidden Markov model (HMM) for fault detection by performing a series of pre-processing steps. First, the de-noised time-scale signatures are extracted using wavelet packet decomposition of the vibration data. Subsequently, the Teager Kaiser energy operator is employed to demodulate the time-scale components of the raw vibration signatures, following which the condition indicators are calculated. Out of several possible condition indicators, only relevant features are selected using a decision tree. This pre-processing improves the sensitivity of condition indicators under multiple faults. A Gaussian mixing model-based hidden Markov model (HMM) is then employed for fault detection. The proposed hybrid HMM is an improvement over traditional HMM in that it achieves better separation of the feature space leading to more robust state estimation under multiple fault states and measurement noise scenarios. A simulation employing modulated signals and two experimental validation studies are presented to demonstrate the performance of the proposed method.

scholarly journals Intrusion Detection System using Bayesian Network and Hidden Markov Model

2012 ◽  
Vol 4 ◽  
pp. 506-514 ◽  
Author(s):  
Nagaraju Devarakonda ◽  
Srinivasulu Pamidi ◽  
V. Valli Kumari ◽  
A. Govardhan
Keyword(s):  
Intrusion Detection ◽  
Markov Model ◽  
Bayesian Network ◽  
Hidden Markov Model ◽  
Intrusion Detection System ◽  
Hidden Markov ◽  
Detection System

Fault Detection and Discrimination of Rotating Machinery Using Frequency Symptom Parameter and Bayesian Network

2013 ◽  
Vol 470 ◽  
pp. 683-688
Author(s):  
Hai Yang Jiang ◽  
Hua Qing Wang ◽  
Peng Chen
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Bayesian Network ◽  
Frequency Domain ◽  
Evaluation Method ◽  
Roller Bearing ◽  
Rotating Machinery ◽  
Discrimination Index ◽  
Vibration Signals ◽  
Diagnosis Method

This paper proposes a novel fault diagnosis method for rotating machinery based on symptom parameters and Bayesian Network. Non-dimensional symptom parameters in frequency domain calculated from vibration signals are defined for reflecting the features of vibration signals. In addition, sensitive evaluation method for selecting good non-dimensional symptom parameters using the method of discrimination index is also proposed for detecting and distinguishing faults in rotating machinery. Finally, the application example of diagnosis for a roller bearing by Bayesian Network is given. Diagnosis results show the methods proposed in this paper are effective.

scholarly journals Forensic Engineering Use Of Fault Tree Analysis

2006 ◽  
Vol 23 (2) ◽  
Author(s):  
Frank H. Johnson ◽  
DeWitt William E.
Keyword(s):  
Fault Tree ◽  
Fault Tree Analysis ◽  
Tree Analysis ◽  
Complex Engineered Systems ◽  
Track Record ◽  
Forensic Engineering ◽  
Fire And Explosion ◽  
Engineered Systems ◽  
Analytical Tools

Analytical Tools, Like Fault Tree Analysis, Have A Proven Track Record In The Aviation And Nuclear Industries. A Positive Tree Is Used To Insure That A Complex Engineered System Operates Correctly. A Negative Tree (Or Fault Tree) Is Used To Investigate Failures Of Complex Engineered Systems. Boeings Use Of Fault Tree Analysis To Investigate The Apollo Launch Pad Fire In 1967 Brought National Attention To The Technique. The 2002 Edition Of Nfpa 921, Guide For Fire And Explosion Investigations, Contains A New Chapter Entitled Failure Analysis And Analytical Tools. That Chapter Addresses Fault Tree Analysis With Respect To Fire And Explosion Investigation. This Paper Will Review The Fundamentals Of Fault Tree Analysis, List Recent Peer Reviewed Papers About The Forensic Engineering Use Of Fault Tree Analysis, Present A Relevant Forensic Engineering Case Study, And Conclude With The Results Of A Recent University Study On The Subject.

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