Rotating Machinery Diagnostics Using Deep Learning on Orbit Plot Images

Jeong, Haedong; Park, Seungtae; Woo, Sun-Hee; Lee, Seung−Chul

doi:10.1016/j.promfg.2016.08.083

Cited by 58 publications

(34 citation statements)

References 12 publications

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“…Haedong et al diagnosed rotating machinery fault based on orbit plot and the orbit plot images are used as input in CNN. The proposed method enable to classify fault mode via orbit plot [61]. Janssens et al applied CNN for bearing fault diagnosis [62].…”

Section: Deep Learningmentioning

confidence: 99%

An intelligent bearing fault diagnosis system: A review

et al. 2019

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Rolling element bearing (REB) is a well-known component that most extensively used in the industry. They operate in extreme condition (high temperature, dirty environment) which may lead to unexpected failure after the certain operation. Faulty on bearing cause severe equipment damage, financial loss and threaten people's life. Development of proper fault diagnosis system of REB capable of preventing unexpected failure from occurs and maintain the machine work in the healthy state. Over a few decades, machine learning is introduced to provide a consistent fault diagnosis result. Hence, this paper reviewed the development of bearing diagnosis method using machine learning models.

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Section: Deep Learningmentioning

confidence: 99%

An intelligent bearing fault diagnosis system: A review

et al. 2019

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“…However, other researchers believe that these onedimensional based deep learning methods cannot work well in complex system network application, since methods like CNNs are proposed for image recognition, and there are great differences between the two-dimensional images (2D images) and the raw signals [26]. In other words, the biggest challenge for CNNs based fault diagnosis is how to overcome the information presentation where traditional CNNs is mainly developed for images, while most fault diagnosis applications in the energy internet are based on time series signals.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for Energy Internet Using Correlation Processing-Based Convolutional Neural Networks

Yang

Pang

Zhou

et al. 2019

IEEE Trans. Syst. Man Cybern, Syst.

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Fault feature extraction based on prior knowledge and raw data is increasingly becoming more challenging in energy internet fault diagnosis due to complicated network topology and coupling disturbances introduced into the systems. Deep learning methods that have emerged in recent years, such as the convolutional neural networks (CNNs), have shown a number of advantages and great potentials in the field of feature extraction and image recognition. However, CNNs does not work well in fault diagnosis for industrial systems, due to the totally different data representations between images used in recognition and signals obtained from industrial processes. This paper tackles this problem by introducing a novel and generic fault diagnosis method for complicated system namely the Spearman rank correlation based CNNs (SR-CNN). By imposing the Spearman rank correlation image layer on the typical CNNs, the multiple time series signals measured by the phasor measurement units (PMUs) is converted to appropriate data images, which are then fed to the CNNs. With the aid of this novel design, different fault features can be comprehensively extracted while the fault can be identified more quickly and precisely than other conventional approaches. To validate the efficacy of the proposed approach, an IEEE defined power gird with multi new energy resources are used as the test platform. The experimental results confirm the effectiveness and superiority of the proposed method in energy internet fault diagnosis over conventional methods.

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“…In addition, some other methods that visualize vibration signals of rotary machinery, such as the shaft center orbit [8] and symmetrized dot pattern (SDP) analysis [9], have been widely used for nonlinear unstable signal characteristic extraction due to their unique ability to present the characteristics of a vibration signal. For example, Jeong et al [8] developed a rotary machinery fault diagnosis method based on deep learning of the shaft center orbit, in which the shaft center orbit of the rotary machinery served as the input of the deep learning model. is method served to enhance the diagnostic accuracy of traditional rotary machinery faults, but the need for pretreatment, such as centering and location, for the image identification of the shaft center orbit made the diagnosis model more complex.…”

Section: Introductionmentioning

confidence: 99%

“…Zeng and Jie [16] developed a CNN-based signal time-frequency image identification method and an automobile gearbox vibration fault diagnosis model in which the shaft center orbit was demonstrated to be important in the visualization of the vibration signals of rotary machinery. In a study by Jeong et al [8], rotary machinery fault diagnosis was studied via shaft center orbit image identification using CNN.…”

Section: Introductionmentioning

confidence: 99%

An SDP Characteristic Information Fusion‐Based CNN Vibration Fault Diagnosis Method

Xiao-hong

Zhao

Hou

et al. 2019

Shock and Vibration

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This study proposes a symmetrized dot pattern (SDP) characteristic information fusion-based convolutional neural network (CNN) fault diagnosis method to resolve issues of high complexity, nonlinearity, and instability in original rotor vibration signals. The method was used to conduct information fusion of real modal components of vibration signals and SDP image identification using CNN in order to achieve vibration fault diagnosis. Compared with other graphic processing methods, the proposed method more fully expressed the characteristics of different vibration signals and thus presented variations between different vibration states in a simpler and more intuitive way. The proposed method was experimentally investigated using simulation signals and rotor test-rig signals, and its validity and advancements were demonstrated using experimental analysis. By using CNN through deep learning to adaptively extract SDP characteristic information, vibration fault identification was ultimately realized.

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Rotating Machinery Diagnostics Using Deep Learning on Orbit Plot Images

Cited by 58 publications

References 12 publications

An intelligent bearing fault diagnosis system: A review

An intelligent bearing fault diagnosis system: A review

Fault Diagnosis for Energy Internet Using Correlation Processing-Based Convolutional Neural Networks

An SDP Characteristic Information Fusion‐Based CNN Vibration Fault Diagnosis Method

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