A study on the condition based maintenance evaluation system of smart plant device using convolutional neural network

There are two main causes of plant accidents: poor maintenance management and human error. In this study, we implemented a smart plant maintenance system that can reduce human errors based on the conditional based maintenance (CBM) concept. Unlike smart plant technology, which focuses on existing technology, we interviewed actual engineers and implemented a system reflecting their needs. First, we implemented three methods for learning defective images using convolutional neural network (CNN) and found that blob detection processing improves learning accuracy. Second, the fitness for service API (FFS API) methodology used in the actual pitting corrosion maintenance evaluation method was used to implement the CBM system. Finally, we verified the reliability of this system by conducting validation through actual case study.

[1]  Oral Büyüköztürk,et al.  Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks , 2017, Comput. Aided Civ. Infrastructure Eng..

[2]  Rajalingappaa Shanmugamani,et al.  Detection and classification of surface defects of gun barrels using computer vision and machine learning , 2015 .

[3]  Bo-Suk Yang,et al.  Data-driven approach to machine condition prognosis using least square regression tree , 2009 .

[4]  Takayasu Tahara.  Fitness-for-Service Assessment for Pressure Equipment in Japan , 2003 .

[5]  Jiming Wang.  Theoretical research and application of petrochemical Cyber-physical Systems , 2017 .

[6]  Sung-Soo Hwang,et al.  Integrated approach for diagnostics and prognostics of HP LNG pump based on health state probability estimation , 2012 .

[7]  Xiang Li,et al.  Remaining useful life estimation in prognostics using deep convolution neural networks , 2018, Reliab. Eng. Syst. Saf..

[8]  Sangkee Min,et al.  Machine health management in smart factory: A review , 2018 .

[9]  ChaYoung-Jin,et al.  Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks , 2017 .

[10]  Qiang Miao,et al.  Singularity detection in machinery health monitoring using Lipschitz exponent function , 2007 .

[11]  Mohammad R. Jahanshahi,et al.  Evaluation of deep learning approaches based on convolutional neural networks for corrosion detection , 2018 .

[12]  Kay Chen Tan,et al.  Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[13]  Dimitrios Kosmopoulos,et al.  Multiclass defect detection and classification in weld radiographic images using geometric and texture features , 2010, Expert Syst. Appl..

[14]  DongSik Gu,et al.  Detection of faults in gearboxes using acoustic emission signal , 2011 .