Using LSTM Network to Predict Circulating Water Pump Bearing Condition on Coal Fired Power Plant

Wisyaldin, Muhammad Kamal; Luciana, Gita Maya; Pariaman, Henry

doi:10.1109/ict-pep50916.2020.9249905

Cited by 7 publications

(10 citation statements)

References 22 publications

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“…The AE approach has been applied in previous studies for the anomaly detection in power plants, including the reheater metal temperature [14], primary air fan generator [21], Pulverizer [15], the vibration of the CWP motor bearing [13], and the motor temperature of 10kV [16].…”

Section: Normal Behavior Model With Autoencoder (Ae)mentioning

confidence: 99%

“…The long short-term memory (LSTM) and gated recurrent unit (GRU) methods have previously been successfully developed to overcome the weaknesses of exploding gradients in RNN and overfitting in time-series applications with deep learning models [13][14][15][16]33]. Prior research combined the Autoencoder approach for feature extraction, generalization of the model by reducing data, and the LSTM model [8], [15] to address the issue of gradient reduction in extensive periods and long sequences.…”

Section: Autoencoder In Combination With Long Shortterm Memory (Lstm)...mentioning

confidence: 99%

“…A combination model of LSTM and autoencoder has been employed in several studies to determine the condition of power plant equipment. [13,15,16].…”

Section: Autoencoder In Combination With Long Shortterm Memory (Lstm)...mentioning

confidence: 99%

“…The use of real-time data from SCADA system for condition monitoring has been extensively researched in the past [10]. In the application of equipment failure prediction, the most chosen method is the data-driven approach, using historical data [12][13][14]. Data-driven methods are preferred over mathematical or physical models of equipment due to the dynamic and non-linear nature of the system [13,15,16].…”

Section: Introductionmentioning

confidence: 99%

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RNN-Autoencoder Approach for Anomaly Detection in Power Plant Predictive Maintenance Systems

2022

IJIES

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Induced Draft Fans (IDF) and Primary Air Fans (PAF) are critical equipment in steam power plants. Anomaly detection based on machine learning models is an approach that is currently being developed for optimization and increasing the effectiveness of predictive maintenance (PDM) as well as increasing the reliability of thermal power plants. The research aims to develop a data-driven model for diagnostic and prognostic equipment, produce accurate predictions using many sensor data taken in real-time from the SCADA system, and design a PDM management framework using an anomaly detection system. This research proposes to use a combined recurrent neural network (RNN)-autoencoder approach as a "normal" behavior model (NBM) with the Mahalanobis Distance (MD) statistical method for the detection of anomalies in power plant equipment. Based on time-series input sensor data, the RNNautoencoder is utilized to predict the behavior of the equipment in health circumstances. In contrast, the MD is used to determine the distance between the actual parameters of the equipment and its "normal" behavior prediction to determine the anomaly condition. This study examined the performance of the long short-term memory (LSTM) and gated recurrent unit (GRU) models in modeling normal behavior with hyperparameter optimization. The LSTM with the best hyperparameters had a validation loss of 5,690 x 10-4 and a validation accuracy of 93.36 percent, whereas the GRU with the best hyperparameters had a validation loss of 4.484 x 10-4 and a validation accuracy of 93.47 percent. GRU can outperform the LSTM model. The proposed framework can detect anomaly conditions in various cases of IDF and PAF equipment disturbances, both in early warning of equipment failure and during downtime conditions.

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Section: Normal Behavior Model With Autoencoder (Ae)mentioning

confidence: 99%

Section: Autoencoder In Combination With Long Shortterm Memory (Lstm)...mentioning

confidence: 99%

“…A combination model of LSTM and autoencoder has been employed in several studies to determine the condition of power plant equipment. [13,15,16].…”

Section: Autoencoder In Combination With Long Shortterm Memory (Lstm)...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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RNN-Autoencoder Approach for Anomaly Detection in Power Plant Predictive Maintenance Systems

2022

IJIES

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“…Coraddu et al (2016;Cipollini et al, 2018), the authors chose the same metric on the same GT dataset example. Also, this metric has been successfully adopted in similar studies in recent years (Wisyaldin et al, 2020;Velasco-Gallego and Lazakis, 2020). Given that there are two outputs, the "GT compressor decay coefficient" and the "GT decay coefficient", this multi-target problem is tackled by decomposing it into two single target sub-problems.…”

Section: Metricmentioning

confidence: 99%

Data Preparation in Machine Learning for Condition-based Maintenance

Masmoudi¹,

Jaoua²,

Jaoua³

et al. 2021

Journal of Computer Science

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Using Machine Learning (ML) prediction to achieve a successful, cost-effective, Condition-Based Maintenance (CBM) strategy has become very attractive in the context of Industry 4.0. In other fields, it is well known that in order to benefit from the prediction capability of ML algorithms, the data preparation phase must be well conducted. Thus, the objective of this paper is to investigate the effect of data preparation on the ML prediction accuracy of Gas Turbines (GTs) performance decay. First a data cleaning technique for robust Linear Regression imputation is proposed based on the Mixed Integer Linear Programming. Then, experiments are conducted to compare the effect of commonly used data cleaning, normalization and reduction techniques on the ML prediction accuracy. Results revealed that the best prediction accuracy of GTs decay, found with the k-Nearest Neighbors ML algorithm, considerately deteriorate when changing the data preparation steps and/or techniques. This study has shown that, for effective CBM application in industry, there is a need to develop a systematic methodology for design and selection of adequate data preparation steps and techniques with the proposed ML algorithms.

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Artificial intelligence based operational strategy development and implementation for vibration reduction of a supercritical steam turbine shaft bearing

Ashraf

Rafique

Uddin

et al. 2022

Alexandria Engineering Journal

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Using LSTM Network to Predict Circulating Water Pump Bearing Condition on Coal Fired Power Plant

Cited by 7 publications

References 22 publications

RNN-Autoencoder Approach for Anomaly Detection in Power Plant Predictive Maintenance Systems

RNN-Autoencoder Approach for Anomaly Detection in Power Plant Predictive Maintenance Systems

Data Preparation in Machine Learning for Condition-based Maintenance

Artificial intelligence based operational strategy development and implementation for vibration reduction of a supercritical steam turbine shaft bearing

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