Application of Variational Auto-Encoder in Mechanical Fault Early Warning

Ma, Bo; Zhao, Yi; Jiang, Zhinong

doi:10.1109/phm-chongqing.2018.00221

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…Ref. [67] applied an approach similar to that of the aforementioned works and compared it with the classic approach of detection based on the threshold of an indicator. The results showed that the Variational Autoencoder (VAE) models were able to predict all the failure data of the vibration signals of rotating equipment, while the classical approach was not able to adequately attend to any of the points of the tested dataset.…”

Section: Approach Based On Detection Of Anomalies and Failuresmentioning

confidence: 99%

Data-Driven Models Applied to Predictive and Prescriptive Maintenance of Wind Turbine: A Systematic Review of Approaches Based on Failure Detection, Diagnosis, and Prognosis

Santiago,

Barbosa,

Mergulhão

et al. 2024

Energies

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Wind energy has achieved a leading position among renewable energies. The global installed capacity in 2022 was 906 GW of power, with a growth of 8.4% compared to the same period in the previous year. The forecast is that the barrier of 1,000,000 MW of installed wind capacity in the world will be exceeded in July 2023, according to data from the World Association of Wind Energy. In order to support the expected growth in the wind sector, maintenance strategies for wind turbines must provide the reliability and availability necessary to achieve these goals. The usual maintenance procedures may present difficulties in keeping up with the expansion of this energy source. The objective of this work was to carry out a systematic review of the literature focused on research on the predictive and prescriptive maintenance of wind turbines based on the implementation of data-oriented models with the use of artificial intelligence tools. Deep machine learning models involving the detection, diagnosis, and prognosis of failures in this equipment were addressed.

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Section: Approach Based On Detection Of Anomalies and Failuresmentioning

confidence: 99%

Data-Driven Models Applied to Predictive and Prescriptive Maintenance of Wind Turbine: A Systematic Review of Approaches Based on Failure Detection, Diagnosis, and Prognosis

Santiago,

Barbosa,

Mergulhão

et al. 2024

Energies

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“…Based on fuzzy neural network (FNN), auto-encoder (AE), VAE, and LSTM, it is proved that the remaining methods except FNN are robust to noise. Ma et al [23] proposed a fault early warning method based on VAE, and it showed high performance compared to the kernel principal components analysis method. Chen et al [24] evaluated the fault diagnosis performance of rolling bearing for deep Boltzmann machines, deep belief network, and stacked auto-encoder (SAE).…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Condition Diagnosis of Linear Motion Guide Using Generative Model Based on Images

2021

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Recently, linear motion (LM) guides have been widely used in industrial processes, especially for precise positioning applications. A LM guide typically requires a custom design for specific characteristics of several industrial fields, which is time consuming for manufacturing process; additionally, and the production line with failed LM guides cannot be fixed rapidly. Therefore, to reduce production loss during such periods, it is important to prepare for maintenance and management in case of a fault through a real-time diagnosis of LM guide conditions. Currently, studies on condition diagnosis applying deep learning algorithms are actively being conducted, and actual measured signals in the industrial fields are being used as training data. In this study, the condition diagnosis of LM guides is conducted through a variational autoencoder (VAE) with convolutional layers. Normal and fault data are measured using an LM guide unit that consists of four LM blocks and one ball screw. The measured signals are converted to spectrogram images through short-time Fourier transform, and only normal data are used to perform network learning. The trained model is applied to classify the normal and fault states, and the reconstruction error is utilized as the evaluation metrics for classification performance. In order to validate the performance, the results are compared to those of a restricted Boltzmann Machine (RBM), and a stacked auto-encoder and a VAE, which does not consist of a convolutional network. Through this study, it is shown that the LM guide diagnosis is possible with a network (VAE with convolution layers) learned with only normal states and the performance is shown to be superior to other networks.INDEX TERMS LM guide, fault diagnosis, variational auto-encoder, short-time Fourier transform, unsupervised learning.

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“…The change process of the excitation signal can be considered to follow an unknown distribution, so the distribution of the response signals is the linear superposition of each unknown distribution. If the distribution characteristics of the response signal can be characterized, the changes of the excitation signals will be accurately identified [10]. Li et al [11] proposed a performance degradation assessment method based on Gaussian mixture model (GMM), that characterized the statistical distribution of the response signals by constructing the GMM of the signals.…”

Section: Introductionmentioning

confidence: 99%

Machinery Early Fault Detection Based on Dirichlet Process Mixture Model

Zhao

Zhang

et al. 2019

IEEE Access

Self Cite

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The most commonly used single feature-based anomaly detection method for the complex machinery, such as large wind power equipment, steam turbine generator sets, and reciprocating compressors, exhibits a defect of low-alarm accuracy due to the non-stationary characteristic of the vibration signals. In order to improve the accuracy of fault detection, a novel method based on the Dirichlet process mixture model (DPMM) is proposed. First, the features of the mechanical vibration signals are used to construct the feature space of the equipment. The DPMM modeling method is then applied to self-learn the probabilistic mixture model of the feature space. The normal working condition model is used as the benchmark model. The early fault detection is realized by using a precise difference measurement method based on Kullback-Leibler divergence to calculate the difference between the real-time model and the benchmark model accurately, and by comparing the calculation result with a self-learned alarm threshold. The effectiveness and the adaptability of this novel early fault detection method are verified by comparing it to the single feature-based anomaly detection method and the Gaussian mixture model (GMM)-based early fault detection method.

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Application of Variational Auto-Encoder in Mechanical Fault Early Warning

Cited by 4 publications

References 4 publications

Data-Driven Models Applied to Predictive and Prescriptive Maintenance of Wind Turbine: A Systematic Review of Approaches Based on Failure Detection, Diagnosis, and Prognosis

Data-Driven Models Applied to Predictive and Prescriptive Maintenance of Wind Turbine: A Systematic Review of Approaches Based on Failure Detection, Diagnosis, and Prognosis

Unsupervised Condition Diagnosis of Linear Motion Guide Using Generative Model Based on Images

Machinery Early Fault Detection Based on Dirichlet Process Mixture Model

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