Auto-encoder based fault early warning model for primary fan of power plant

Lu, Kuan; Gao, Song; Sun, Wei; Jiang, Zhe; Meng, Xiangrong; Zhai, Yikui; Han, Yingkun; Sun, Mengmeng

doi:10.1088/1755-1315/358/4/042060

Cited by 3 publications

(5 citation statements)

References 5 publications

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“…Many studies have developed unsupervised learning methods to overcome the problems of unbalancing categorical training datasets [8,11,21,22]. Anomaly detection was previously performed using the pattern recognition method, which detects deviations in specific datasets using the "normal" behavior model (NBM).…”

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

confidence: 99%

“…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%

“…AE-based methods have been investigated to be combined with neural networks [21] and deep learning [22] to determine the non-linear relationship between features. Traditional AE neural network approaches, on the other hand, ignore the dynamic aspect of time series data [11].…”

Section: Normal Behavior Model With Autoencoder (Ae)mentioning

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%

Section: Normal Behavior Model With Autoencoder (Ae)mentioning

confidence: 99%

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

2022

IJIES

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“…At the same time, due to the long deployment cycle of spare parts, the fault maintenance cost of large-scale wind farms remains high, which has a significant impact on the economic benefits of wind farms [7], [8]. Due to the complex structure, variable operation conditions, and strong coupling between components of wind turbines, failures occur frequently and even chain phenomena, leading to significant accidents such as combustion and collapse of wind turbines [9], [10]. Suppose the fault of the wind turbine can be accurately diagnosed and estimated and its development trend and the potential fault symptoms can be found early [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Study of Wind Turbine Fault Diagnosis and Early Warning Based on SCADA Data

Shi¹,

Liu

Gao

2021

IEEE Access

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Wind turbine fault diagnosis and early warning are important to reduce wind farm operation and maintenance costs and improve power generation efficiency. In this paper, we take the Supervisory Control and Data Acquisition (SCADA) data as the research object and research wind turbine health data purification, fault diagnosis model building, and unit operation status monitoring from a completely datadriven perspective. Firstly, for the problem that Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm cannot identify high-density anomalous data. An anomaly data processing scheme combining a density clustering algorithm and normal power interval estimation is proposed. The accuracy of extracting health data from wind turbines is improved. Secondly, to address the problem that the eXtreme Gradient Boosting (XGBoost) algorithm has more hyperparameters, we propose an optimization scheme based on the Bayesian Optimization Algorithm (BOA) and tree model for feature weight measurement, which improves the efficiency and accuracy of intuitive mapping from SCADA system monitoring data to fault features. Finally, a wind turbine condition monitoring scheme based on the information fusion of multi-characteristic monitoring parameters is designed. The wind turbine condition monitoring scheme proposed in this paper can warn generator system failure 3.67 hours, gearbox system failure 5.

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“…The autoencoder (AE) neural network is an unsupervised learning model, whose output is the reconstruction of the input, thus avoiding manual selection of feature variables. However, the conventional AE-based methods also do not consider the time sequence correlation between variables in power plants, as previously discussed, e.g., AE [30,31], stacked autoencoder (SAE) [32,33], variational autoencoder (VAE) [34], and deep autoencoder Gaussian mixture model [35].…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection of Power Plant Equipment Using Long Short-Term Memory Based Autoencoder Neural Network

Zhang

Yang

et al. 2020

Sensors

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Anomaly detection is of great significance in condition-based maintenance of power plant equipment. The conventional fixed threshold detection method is not able to perform early detection of equipment abnormalities. In this study, a general anomaly detection framework based on a long short-term memory-based autoencoder (LSTM-AE) network is proposed. A normal behavior model (NBM) is established to learn the normal behavior patterns of the operating variables of the equipment in space and time. Based on the similarity analysis between the NBM output distribution and the corresponding measurement distribution, the Mahalanobis distance (MD) is used to describe the overall residual (OR) of the model. The reasonable range is obtained using kernel density estimation (KDE) with a 99% confidence interval, and the OR is monitored to detect abnormalities in real-time. An induced draft fan is chosen as a case study. Results show that the established NBM has excellent accuracy and generalizability, with average root mean square errors of 0.026 and 0.035 for the training and test data, respectively, and average mean absolute percentage errors of 0.027%. Moreover, the abnormal operation case shows that the proposed framework can be effectively used for the early detection of abnormalities.

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Auto-encoder based fault early warning model for primary fan of power plant

Cited by 3 publications

References 5 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

Study of Wind Turbine Fault Diagnosis and Early Warning Based on SCADA Data

Anomaly Detection of Power Plant Equipment Using Long Short-Term Memory Based Autoencoder Neural Network

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