A Robust fault diagnosis and forecasting approach based on Kalman filter and Interval Type-2 Fuzzy Logic for efficiency improvement of centrifugal gas compressor system

Nail, Bachir; Kouzou, Abdellah; Hafaifa, Ahmed; Hadroug, Nadji; Puig, Vicenç

doi:10.29354/diag/108613

Cited by 3 publications

(2 citation statements)

References 37 publications

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“…The developed Bayesian networks in this paper have some advantage over other artificial intelligence methods. In terms of graphical models, it can be seen that the Bayesian networks represented in this work are easy to understand even by non-professionals compared to artificial neuron networks (ANN) [25], and support vector machines (SVM) [26] also, fuzzy logic (FL) [27], and expert systems. In addition, this model is compatible with all centrifugal compressors but it must change the probabilities a priori for each one.…”

Section: Comparison With Other Artificial Intelligence Methodsmentioning

confidence: 99%

Multi Sources Information Fusion Based on Bayesian Network Method to Improve the Fault Prediction of Centrifugal Compressor

Nessaib

Lakehal

2022

Strojnícky Časopis - Journal of Mechanical Engineering

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The centrifugal compressor is an important machine in the oil and gas industry, so the fault prediction of these machines is widely discussed in the literature. Several techniques can and should be used in fault prediction of centrifugal compressors: vibration analysis, non-destructive testing techniques, operating parameters, and other techniques. But in particular cases, these tools are inefficient for making a decision regarding the combined fault diagnosis and prediction. This paper presents a contribution to fault prediction in centrifugal compressor utilizing multi-source information fusion by a Bayesian network. The data fusion does not come from the same source, but rather from vibration analysis, oil analysis, and operating parameters. In addition, the accuracy and ability of fault prediction can be improved compared with the use of data obtained from vibration analysis only or oil analysis. The proposed method accuracy is validated on a BCL 406 type centrifugal compressor. Furthermore, the obtained results showed the effectiveness of the multi-source information fusion by Bayesian network approach gives more accuracy to decision-making in fault prediction and the developed method has an effect in predicting the combined faults.

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Section: Comparison With Other Artificial Intelligence Methodsmentioning

confidence: 99%

Multi Sources Information Fusion Based on Bayesian Network Method to Improve the Fault Prediction of Centrifugal Compressor

Nessaib

Lakehal

2022

Strojnícky Časopis - Journal of Mechanical Engineering

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“…To calculate the estimated value of the actual state of the process, we need only the current measurement and the estimated value from the previous time step. During estimation, the KF operates by propagating the mean and the covariance of the state through the time [34]. The dynamic model as a state space of the observed system is written as:…”

Section: Kalman Filtermentioning

confidence: 99%

Fault diagnosis-based observers using Kalman filters and Luenberger estimators: Application to the pitch system fault actuators

Zemali¹,

Cherroun²,

Hadroug³

et al. 2023

Diagnostyka

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This paper aims to present a robust fault diagnosis structure-based observers for actuator faults in the pitch part system of the wind turbine benchmark. In this work, two linear estimators have been proposed and investigated: the Kalman filter and the Luenberger estimator for observing the output states of the pitch system in order to generate the appropriate residual between the measured positions of blades and the estimated values. An inference step as a decision block is employed to decide the existence of faults in the process, and to classify the detected faults using a predetermined threshold defined by upper and lower limits. All actuator faults in the pitch system of the horizontal wind turbine benchmark are studied and investigated. The obtained simulation results show the ability of the proposed diagnosis system to determine effectively the occurred faults in the pitch system. Estimation of the output variables is effectively realized in both situations: without and with the occurrence of faults in the studied process. A comparison between the two used observers is demonstrated.

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Internal stability improvement of a natural gas centrifugal compressor system based on a new optimal output feedback controller using block transformation and grey wolf optimizer

Nail

Bekhiti

Puig

2021

Journal of Natural Gas Science and Engineering

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A Robust fault diagnosis and forecasting approach based on Kalman filter and Interval Type-2 Fuzzy Logic for efficiency improvement of centrifugal gas compressor system

Cited by 3 publications

References 37 publications

Multi Sources Information Fusion Based on Bayesian Network Method to Improve the Fault Prediction of Centrifugal Compressor

Multi Sources Information Fusion Based on Bayesian Network Method to Improve the Fault Prediction of Centrifugal Compressor

Fault diagnosis-based observers using Kalman filters and Luenberger estimators: Application to the pitch system fault actuators

Internal stability improvement of a natural gas centrifugal compressor system based on a new optimal output feedback controller using block transformation and grey wolf optimizer

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