Fault diagnosis and prediction of complex system based on Hidden Markov model

Li, Chen; Wei, Fajie; Wang, Cheng; Zhou, Shenghan

doi:10.3233/jifs-169344

Cited by 11 publications

(12 citation statements)

References 19 publications

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“…A fault diagnosis and prediction system is proposed by Li, Wei, Wang, and Zhou 35 which consists of three parts, namely: data preprocessing, degradation state detection, and fault diagnosis. For feature extraction, the wavelet transforms correlation filter is employed.…”

Section: ∑ ∏mentioning

confidence: 99%

Process Fault Prognosis Using Hidden Markov Model–Bayesian Networks Hybrid Model

Don

Khan

2019

Ind. Eng. Chem. Res.

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A Hidden Markov Model–Bayesian Networks (HMM–BN) hybrid system coupled with a novel prediction technique is employed to predict and isolate 10 identified faults in the Tennessee Eastman (TE) process. HMM is trained offline with Normal Operating Condition data and then used to train the BN. Utilizing the same trained HMM, a history of Log Likelihood (LL) values of process fault data is generated. The same trained HMM determines the LL values of online data strings and compares with the LL history and predicts the most likely future state of the system. This information is then fed to BN as likelihood evidence to isolate the root cause. The system successfully predicts all the selected 10 faults of the TE process while accurately isolating 8 of them. The maximum level of noise that can be handled is presented along with the respective result. These results set the benchmark for future prediction and isolation studies of the TE process.

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Section: ∑ ∏mentioning

confidence: 99%

Process Fault Prognosis Using Hidden Markov Model–Bayesian Networks Hybrid Model

Don

Khan

2019

Ind. Eng. Chem. Res.

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“…Khan et al 10 designed a novel active fault-tolerant control system which consists of an observation filter-based fault detection and identification system, and simulation analysis shows the effectiveness and feasibility of the proposed method. Li et al 11 proposed a fault diagnosis and fault prediction method based on Hidden Markov model, and the effectiveness and accuracy of the method are verified through the experimental results. Cha et al 12 designed an extended Kalman filter and an unscented Kalman filter for the open-cycle liquid propellant rocket engine, and successfully implemented fault detection and diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Sensor fault estimation based on the constrained zonotopic Kalman filter

Liu

Wang

2021

Intl J Robust & Nonlinear

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For solving the additive and multiplicative sensor faults in the constrained system with unknown but bounded noise, the constrained zonotopic Kalman filter-based additive sensor fault estimator and multiplicative sensor fault estimator are designed, respectively. In the fault estimation process, the states of the system are contained in the zonotopic sets estimated by the constrained zonotopic Kalman filter. The fault detection is carried out by judging whether the true value of the system's output is within the upper and lower bounds of its estimated zonotopic set. Once a fault is detected in the system, the corresponding sensor fault estimator can be used to estimate the additive or multiplicative sensor faults, and the corresponding zonotopic set and interval set can be obtained, respectively. Finally, comparative analyses of the proposed constrained zonotopic Kalman filter-based fault estimators and the zonotopic Kalman filter-based fault estimators in both numerical and case simulations prove the feasibility and effectiveness of the proposed algorithm and show the advantages of this algorithm in view of conservativeness.

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“…Software defects are one of the primary reasons for poor software quality, 1 and the study on software defect predictions based on machine learning is a research hotspot in this field 2,3 . Most of the current software defect prediction methods are supervised prediction methods based on labeled datasets 4‐6 . However, in software engineering practices, there is usually a lack of labeled datasets for learning when predicting defects of a new software project, and therefore, it is difficult to directly use a supervised defect prediction method for prediction.…”

Section: Introductionmentioning

confidence: 99%

A novel software defect prediction approach using modified objective cluster analysis

Ren¹,

Zhang

2020

Concurrency and Computation

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A novel approach for software defect prediction of unlabeled datasets is proposed using modified objective cluster analysis (OCA). In this approach, the first step is to construct the distance matrix of instances in the datasets by utilizing the automatically determined clusters through the modified OCA. Then the dipoles within different instances are categorized into two different groups. Finally, the clusters of instances are produced, and software defects can be predicted by imposing a modified consistency criterion. Case study and comparative experiments were conducted based on 12 public datasets selected from the databases of Promise and ReLink using multiple different unsupervised algorithms and cross‐project approaches. There are two experimental settings: experiments with datasets that contain all metrics and experiments with datasets that contain only module size metrics. The results were evaluated by the index of precision, recall, F‐measure, and receiver operating characteristic curve (AUC). Furthermore, a complexity analysis of the tested algorithms was conducted as well. In experiments with datasets with all metrics, the proposed OCA gets the best results in four indexes, and the average values of precision, recall, F‐measure, and AUC were improved by a minimum of 1.52%, 2.78%, 19.84%, and 0.93%, respectively. In experiments with datasets with only module size metrics, the proposed OCA performed the best results in four indexes also, and the average values of precision, F‐measure, and AUC were improved by a minimum of 8.8%, 2.59%, and 8.36%, respectively. The proposed algorithm is of low complexity and provides a new way to efficiently predict software defects with unlabeled datasets.

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Fault diagnosis and prediction of complex system based on Hidden Markov model

Cited by 11 publications

References 19 publications

Process Fault Prognosis Using Hidden Markov Model–Bayesian Networks Hybrid Model

Process Fault Prognosis Using Hidden Markov Model–Bayesian Networks Hybrid Model

Sensor fault estimation based on the constrained zonotopic Kalman filter

A novel software defect prediction approach using modified objective cluster analysis

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