Stacked Multilevel-Denoising Autoencoders: A New Representation Learning Approach for Wind Turbine Gearbox Fault Diagnosis

Jiang, Guoqian; He, Haibo; Xie, Ping; Tang, Youxi

doi:10.1109/tim.2017.2698738

Cited by 234 publications

(96 citation statements)

References 32 publications

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“…The DAE can learn nonlinear and linear correlations in the multivariate sensor-collected data from the power grid [34,35,37,38] PCA requires linear and Gaussian assumption about the data [34].…”

Section: Denoising Autoencoder Principal Component Analysismentioning

confidence: 99%

Mitigating the Impacts of Covert Cyber Attacks in Smart Grids Via Reconstruction of Measurement Data Utilizing Deep Denoising Autoencoders

et al. 2019

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As one of the most diversified cyber-physical systems, the smart grid has become more decumbent to cyber vulnerabilities. An intelligently crafted, covert, data-integrity assault can insert biased values into the measurements collected by a sensor network, to elude the bad data detector in the state estimator, resulting in fallacious control decisions. Thus, such an attack can compromise the secure and reliable operations of smart grids, leading to power network disruptions, economic loss, or a combination of both. To this end, in this paper, we propose a novel idea for the reconstruction of sensor-collected measurement data from power networks, by removing the impacts of the covert data-integrity attack. The proposed reconstruction scheme is based on a latterly developed, unsupervised learning algorithm called a denoising autoencoder, which learns about the robust nonlinear representations from the data to root out the bias added into the sensor measurements by a smart attacker. For a robust, multivariate reconstruction of the attacked measurements from multiple sensors, the denoising autoencoder is used. The proposed scheme was evaluated utilizing standard IEEE 14-bus, 39-bus, 57-bus, and 118-bus systems. Simulation results confirm that the proposed scheme can handle labeled and non-labeled historical measurement data and results in a reasonably good reconstruction of the measurements affected by attacks.

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Section: Denoising Autoencoder Principal Component Analysismentioning

confidence: 99%

Mitigating the Impacts of Covert Cyber Attacks in Smart Grids Via Reconstruction of Measurement Data Utilizing Deep Denoising Autoencoders

et al. 2019

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“…Although these fault diagnosis approaches are able to detect certain industrial system faults, it remains a challenge to design a fault diagnosis model that meets practical production requirements [35][36][37]. In addition, how to accurately detect faults with a low complexity is still a challenge.…”

Section: Introductionmentioning

confidence: 99%

A Novel Low-Complexity Fault Diagnosis Algorithm for Energy Internet in Smart Cities

et al. 2020

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The smart energy system, viewed as an “Energy Internet”, consists of the intelligent integration of decentralized sustainable energy sources, efficient distribution, and optimized power consumption. That implies the fault diagnosis for a smart energy system should be of low complexity. In this paper, we propose a Strong Tracking Unscented Kalman Filter ( S T U K F ) and modified Bayes’ classification-based Modified Three Sigma test ( M T S ), abbreviated as S F B T , for smart energy networks. The theoretical analysis and simulations indicate that S F B T detects faults with a high accuracy and a low complexity of O ( n ) .

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“…Dai et al [16] proposed a multisource information fusion model based on a deep belief network to perform fault detection analyses on a power transformer. Jiang et al [17] proposed a multifeature fusion method for stacked multilevel denoising autoencoders, which can effectively improve the fault diagnosis accuracy of wind turbines by using a deep network architecture formed by stacking.…”

Section: Introductionmentioning

confidence: 99%

Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data

Huang

Liu

2019

Sensors

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Vibration sensing data is an important resource for mechanical fault prediction, which is widely used in the industrial sector. Artificial neural networks (ANNs) are important tools for classifying vibration sensing data. However, their basic structures and hyperparameters must be manually adjusted, which results in the prediction accuracy easily falling into the local optimum. For data with high levels of uncertainty, it is difficult for an ANN to obtain correct prediction results. Therefore, we propose a multifeature fusion model based on Dempster-Shafer evidence theory combined with a particle swarm optimization algorithm and artificial neural network (PSO-ANN). The model first used the particle swarm optimization algorithm to optimize the structure and hyperparameters of the ANN, thereby improving its prediction accuracy. Then, the prediction error data of the multifeature fusion using a PSO-ANN is repredicted using multiple PSO-ANNs with different single feature training to obtain new prediction results. Finally, the Dempster-Shafer evidence theory was applied to the decision-level fusion of the new prediction results preprocessed with prediction accuracy and belief entropy, thus improving the model's ability to process uncertain data. The experimental results indicated that compared to the K-nearest neighbor method, support vector machine, and long short-term memory neural networks, the proposed model can effectively improve the accuracy of fault prediction.

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Stacked Multilevel-Denoising Autoencoders: A New Representation Learning Approach for Wind Turbine Gearbox Fault Diagnosis

Cited by 234 publications

References 32 publications

Mitigating the Impacts of Covert Cyber Attacks in Smart Grids Via Reconstruction of Measurement Data Utilizing Deep Denoising Autoencoders

Mitigating the Impacts of Covert Cyber Attacks in Smart Grids Via Reconstruction of Measurement Data Utilizing Deep Denoising Autoencoders

A Novel Low-Complexity Fault Diagnosis Algorithm for Energy Internet in Smart Cities

Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data

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