Anomaly Detection Using LSTM-Autoencoder to Predict Coal Pulverizer Condition on Coal-Fired Power Plant

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

doi:10.5109/4372264

Cited by 10 publications

(14 citation statements)

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“…The quantity and quality of training data are also considerations in the supervised learning method. [15]. However, this is a problem for applying in power plant equipment because most equipment is healthy throughout its entire operating cycle.…”

Section: Unsupervised Learning Methods With Normal Behavior Model App...mentioning

confidence: 99%

“…NBM is designed to investigate the equipment operating variable's healthy behavior patterns under certain conditions and at particular times. This method involves rebuilding the 'typical' behavior of time-series parameter data and then detecting abnormalities using reconstruction errors [10][11][12]15]. The data used to form the NBM of this equipment is obtained from the historical operation of the equipment with special treatment, including eliminating data when equipment downtime occurs, eliminating data when a failure occurs according to operating records, and eliminating abnormal data based on statistical characteristics [11].…”

Section: Unsupervised Learning Methods With Normal Behavior Model App...mentioning

confidence: 99%

“…One model that is widely used to build NBM for anomaly detection applications is the autoencoder (AE) model, which is an unsupervised learning method in which the output of the model is a reconstruction of the input [11,14,15] with latentspace representation techniques [23][24][25]. AE architecture is widely used because it can overcome the difficulty of determining features manually, which requires domain knowledge about data and processes related to it [11,26] and can automatically extract representative features from raw data so that it significantly improves failure detection accuracy [22,27].…”

Section: Normal Behavior Model With Autoencoder (Ae)mentioning

confidence: 99%

“…AE architecture is widely used because it can overcome the difficulty of determining features manually, which requires domain knowledge about data and processes related to it [11,26] and can automatically extract representative features from raw data so that it significantly improves failure detection accuracy [22,27]. AE can also improve the model's generalizability with data reduction [15,28].…”

Section: Normal Behavior Model With Autoencoder (Ae)mentioning

confidence: 99%

“…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: Unsupervised Learning Methods With Normal Behavior Model App...mentioning

confidence: 99%