Fault Diagnosis of Rotating Machinery under Noisy Environment Conditions Based on a 1-D Convolutional Autoencoder and 1-D Convolutional Neural Network

Liu, Xingchen; Zhou, Qicai; Zhao, Jiong; Shen, Hehong; Xiong, Xiaolei

doi:10.3390/s19040972

Cited by 89 publications

(55 citation statements)

References 33 publications

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“…CNN-based architectures can also be combined with other types of networks for the purpose of fault diagnosis. In Liu et al (2019b) , for instance, a one-dimensional convolutional-DAE is proposed to extract features from bearing and gearbox data. This model is given corrupted time-series as input and its goal is to clean and reconstruct them at the output level.…”

Section: Artificial Intelligence-based Prognostic and Health Managemementioning

confidence: 99%

Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead

Biggio

Kastanis

2020

Front. Artif. Intell.

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Prognostic and Health Management (PHM) systems are some of the main protagonists of the Industry 4.0 revolution. Efficiently detecting whether an industrial component has deviated from its normal operating condition or predicting when a fault will occur are the main challenges these systems aim at addressing. Efficient PHM methods promise to decrease the probability of extreme failure events, thus improving the safety level of industrial machines. Furthermore, they could potentially drastically reduce the often conspicuous costs associated with scheduled maintenance operations. The increasing availability of data and the stunning progress of Machine Learning (ML) and Deep Learning (DL) techniques over the last decade represent two strong motivating factors for the development of data-driven PHM systems. On the other hand, the black-box nature of DL models significantly hinders their level of interpretability, de facto limiting their application to real-world scenarios. In this work, we explore the intersection of Artificial Intelligence (AI) methods and PHM applications. We present a thorough review of existing works both in the contexts of fault diagnosis and fault prognosis, highlighting the benefits and the drawbacks introduced by the adoption of AI techniques. Our goal is to highlight potentially fruitful research directions along with characterizing the main challenges that need to be addressed in order to realize the promises of AI-based PHM systems.

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Section: Artificial Intelligence-based Prognostic and Health Managemementioning

confidence: 99%

Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead

Biggio

Kastanis

2020

Front. Artif. Intell.

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“…According to Figure 27 In order to investigate the performance of the proposed method in the presence of environmental noise, the white Gaussian noise is added to the testing data with different signal-to-noise (SNR) values for more realistic assumptions. SNR is defined as follows 9,[51][52][53] SNR dB = 10 log 10 P signal P noise ð27Þ…”

Section: Damage Detection Of the Lab-scale Offshore Jacket Modelmentioning

confidence: 99%

Deep neural networks–based damage detection using vibration signals of finite element model and real intact state: An evaluation via a lab-scale offshore jacket structure

Mousavi

Varahram

Ettefagh

et al. 2020

Structural Health Monitoring

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Structural health monitoring of mechanical systems is essential to avoid their catastrophic failure. In this article, an effective deep neural network is developed for extracting the damage-sensitive features from frequency data of vibration signals to damage detection of mechanical systems in the presence of the uncertainties such as modeling errors, measurement errors, and environmental noises. For this purpose, the finite element method is used to analyze a mechanical system (finite element model). Then, vibration experiments are carried out on the laboratory-scale model. Vibration signals of real intact system are used to updating the finite element model and minimizing the disparities between the natural frequencies of the finite element model and real system. Some parts of the signals that are not related to the nature of the system are removed using the complete ensemble empirical mode decomposition technique. Frequency domain decomposition method is used to extract frequency data. The proposed deep neural network is trained using frequency data of the finite element model and real intact state and then is tested using frequency data of the real system. The proposed network is designed in two stages, namely, the pre-training classification based on deep auto-encoder and Softmax layer (first stage), and the re-training classification based on backpropagation algorithm for fine tuning of the network (second stage). The proposed method is validated using a lab-scale offshore jacket structure. The results show that the proposed method can learn features from the frequency data and achieve higher accuracy than other comparative methods.

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“…As a result, conventional algorithm classification under experimental conditions may severely degrade the accuracy under noisy environmental conditions, which are ubiquitous in practical industrial applications. A one-dimensional (1-D) denoising convolutional autoencoder (DCAE) [10] and a 1-D convolutional neural network (CNN) proved to be excellent solutions to address this problem [11], whereby the former is used for noise reduction of raw vibration signals and the latter for fault diagnosis using the de-noised signals. The DCAE model can be trained using noisy data for learning.…”

Section: Review Of the Contributions In This Special Issuementioning

confidence: 99%

Special Issue “Advanced Signal Processing in Intelligent Systems for Health Monitoring”

Abbod

Shieh

2019

Sensors

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Recently, significant developments have been achieved in the field of artificial intelligence, in particular the introduction of deep learning technology that has improved the learning and prediction accuracy to unpresented levels, especially when dealing with big data and high-resolution images. Significant developments have occurred in the area of medical signal processing, measurement techniques, and health monitoring, such as vital biological signs for biomedical systems and noise and vibration of mechanical systems, which are carried out by instruments that generate large data sets. These big data sets, ultimately driven by high population growth, would require Artificial Intelligence techniques to analyse and model. In this Special Issue, papers are presented on the latest signal processing and deep learning techniques used for health monitoring of biomedical and mechanical systems.

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Fault Diagnosis of Rotating Machinery under Noisy Environment Conditions Based on a 1-D Convolutional Autoencoder and 1-D Convolutional Neural Network

Cited by 89 publications

References 33 publications

Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead

Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead

Deep neural networks–based damage detection using vibration signals of finite element model and real intact state: An evaluation via a lab-scale offshore jacket structure

Special Issue “Advanced Signal Processing in Intelligent Systems for Health Monitoring”

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