Neural network based system for detecting and diagnosing faults in steam turbine of thermal power plant

Dhini, Arian; Kusumoputro, Benyamin; Surjandari, Isti

doi:10.1109/icawst.2017.8256435

Cited by 18 publications

(9 citation statements)

References 13 publications

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“…With the continuous operation of a steam turbine, some faults will inevitably occur to its internal components. Due to the complexity of the steam turbine system, the types and the mechanisms of the faults are diverse [1].…”

Section: B Steam Turbine Process Monitoringmentioning

confidence: 99%

“…In Fig. 6, the testing set is set as [1,3,3,8,1], and the training set is set as [2, 0, 0, 8, 7, 2]. The MT is a 5-by-6 matrix.…”

Section: Rul Predictionmentioning

confidence: 99%

“…Steam turbines are the major equipment of electricity production in thermal power plants. A steam turbine is a type of heat engine which transforms heat into mechanical energy and is used in industry to generate electricity by driving an electric generator [1]. To keep a steam turbine system operating well, periodic maintenance is usually provided.…”

Section: Introductionmentioning

confidence: 99%

“…Aiming at fault detection and diagnosis for steam turbines, Salahshoor fused an SVM classifier with an adaptive neuro-fuzzy inference system (ANFIS) to enhance the fault detection and diagnosis performance [18]. Dhini constructed a neural network (NN) based classifier to detect and diagnose faults for steam turbines [1]. Both SVM and NN have good performance on anomaly detection.…”

Section: Introductionmentioning

confidence: 99%

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A Data-Driven Health Prognostics Approach for Steam Turbines Based on Xgboost and DTW

Que

2019

IEEE Access

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A steam turbine is one of the critical components in a power generation system whose failure may result in unexpected consequences, even catastrophic losses. Thus, the reliability of steam turbines needs to be guaranteed all the time, which requires that its health state can be monitored and predicted effectively. Due to various failure modes, it is difficult to build physics-of-failure models used for health prognostics for steam turbines. In this paper, a data-driven integrated framework for health prognostics for steam turbines, which is based on extreme gradient boosting (XGBoost) and dynamic time warping (DTW), is proposed. The proposed framework includes two modules: anomaly detection and remaining useful life (RUL) prediction. The anomalies refer to the overall abnormal operation of steam turbines. In the process of anomaly detection, the temporal variables which can represent the operating conditions of the considered steam turbine are selected first. Appropriately selected temporal variables can reduce the input dimension and will improve real-time performance. Then, XGBoost is used to detect anomalies based on learning historical data. In the process of RUL prediction, a similarity-based algorithm with DTW is used to gain the RUL by contrasting the measured temporal variables with those in the historical cases. The similarity-based algorithm can predict the RUL without establishing a degradation path model, which can avoid the difficulties in parameter estimation for the degradation model and model generalization. The proposed framework is validated by real case studies from an industrial steam turbine. The results show that the proposed approach can detect the anomalies successfully and predict the RUL effectively.INDEX TERMS Dynamic time warping, extreme gradient boosting, remaining useful life prediction, steam turbine.

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Section: B Steam Turbine Process Monitoringmentioning

confidence: 99%

“…In Fig. 6, the testing set is set as [1,3,3,8,1], and the training set is set as [2, 0, 0, 8, 7, 2]. The MT is a 5-by-6 matrix.…”

Section: Rul Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Data-Driven Health Prognostics Approach for Steam Turbines Based on Xgboost and DTW

Que

2019

IEEE Access

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“…In the last years, many algorithms have been applied for the control of thermal solar power plants [4,6,7] but the FDD algorithms are poor. FDI methods divided into two areas: data analysis methods the most widely used and widely used statistical techniques for industrial process monitoring and multivariate methods based on artificial intelligence [5,[8][9][10]; model based on linear observers, nonlinear, in sliding mode and the Kalman filter [11][12][13][14]. The concept of IDEs using these methods is done in two steps.…”

Section: Introductionmentioning

confidence: 99%

Robust Fuzzy Sliding Mode Observer for Faults Detection in Solar Power Plant Application

Zahra¹,

Mourad²,

Ahmed³

2020

I2M

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This article deals with the designing of fuzzy sliding mode observer in order to fault diagnosis in solar power plant. Purpose technique enable to the modulated using Takagi-Sugeno (TS) fuzzy models. Principal of proposed observer is to uses for estimate the state vector of the system; a Linear Matrix Inequalities (LMIs) is performed to ensure stability conditions. Foremother, it is deriving a diagnosis signal-residual. The residual is generated by the comparison of measured and estimated output. Proposed approach performance is tested in solar power plant model through numerical results.

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Optimizing high-speed rotating shaft vibration control: Experimental investigation of squeeze film dampers and a comparative analysis using Artificial Neural Networks (ANN) and Response Surface Methodology (RSM)

Gupta,

Singh

2024

Expert Systems with Applications

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Neural network based system for detecting and diagnosing faults in steam turbine of thermal power plant

Cited by 18 publications

References 13 publications

A Data-Driven Health Prognostics Approach for Steam Turbines Based on Xgboost and DTW

A Data-Driven Health Prognostics Approach for Steam Turbines Based on Xgboost and DTW

Robust Fuzzy Sliding Mode Observer for Faults Detection in Solar Power Plant Application

Optimizing high-speed rotating shaft vibration control: Experimental investigation of squeeze film dampers and a comparative analysis using Artificial Neural Networks (ANN) and Response Surface Methodology (RSM)

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