Intelligent Model-based Integrity Assessment of Nonstationary Mechanical System
DOI:
https://doi.org/10.13052/jwe1540-9589.2022Keywords:
Integrity assessment, nonstationary mechanical system, model-based, improved particle filterAbstract
The fault diagnosis model for nonstationary mechanical system is proposed in the condition monitoring. The algorithm with an improved particle filter and Back Propagation for intelligent fault identification is developed, which is used to reduce the noise of the experimental vibration signals to delete the negative effect of the noise on the feature extraction of the original vibration signal. The proposed integrated method is applied for the trouble shoot of the impellers inside the centrifugal pump. The principal component analysis (PCA) method optimizes the clean vibration signal to choose the optimal eigenvalue features.The constructed BP neural network is trained to get the condition models for fault identification. The proposed novel model is compared with the BP neural network based on traditional PF and particle swarm optimization particle filter (PSO-PF) algorithm. The BP neural network diagnosis method based on the improved PF algorithm is much better for the integrity assessment of the centrifugal pump impeller. This method is much significant for big data mining in the fault diagnosis method of the complex mechanical system.
Downloads
References
C. Lim, D. Kim, et al., The Operation Concept and Procedure of Mechanical Ground Support Equipment for KSLV-II Launch Complex, Journal of the Korean Society of Propulsion Engineers, 22(4), 2018, 125–132.
Y.J. Li, Q.C. Chang, et al., Complex data study on mechanical fault diagnosing, Journal of Intelligent & Fuzzy Systems, 2018, 34(2), 1169–1176.
Chen Hanxin, Fan Dongliang, Huang Jinmin, et al., Finite element analysis model on ultrasonic phased array technique for material defect time of flight diffraction detection, Science of advanced material 12(5) (2020), 665–675.
Z.X. Li, S.J. Han, J.G. Wang, et al., Time-Delayed Feedback Tristable Stochastic Resonance Weak Fault Diagnosis Method and Its Application, Shock and Vibration, 2019, 2097164.
Chen Hanxin, Huang Wenjian, Huang Jinmin, et al., Multi-fault condition monitoring of slurry pump with principle component analysis and sequential hypothesis test [J], International Journal of Pattern Recognition and Artificial Intelligence (2019), https://doi.org/10.1142/S0218001420590193.
Z.P. Liu, L. Zhang, Naturally Damaged Wind Turbine Blade Bearing Fault Detection Using Novel Iterative Nonlinear Filter and Morphological Analysis, IEEE Transactions on Industrial Electronics, 2020, 67(10), 8713–8722.
Hanxin Chen, Dong Liang Fan, Lu Fang, Wenjian Huang, Jinmin Huang, Chenghao Cao, Liu Yang, Yibin He and Li Zeng, Particle swarm optimization algorithm with mutation operator for particle filter noise reduction in mechanical fault diagnosis, International journal of pattern recognition and artificial intelligence (2020), https://doi.org/10.1142/S0218001420580124.
X.C. Li, X.Y. Yang, Y.J. Yang, et al., An intelligent diagnostic and prognostic framework for large-scale rotating machinery in the presence of scarce failure data, Struct. Health Monit., 2020, 19(5), 1375–1390.
A. Ul-Hamid, L.M. Al-Hadhrami, A.I. Mohammed, et al., Failure analysis of an impeller blade, Materials and Corrosion, 2015, 66(3), 286–295.
Hanxin Chen et. al., Model-based method with nonlinear ultrasonic system identification for mechanical structural health assessment, Transactions on emerging telecommunications technologies (2020), e3955, http://doi.org/10.1002/ett.2955.
P.K. Pradhan, S.K. Roy, A.R. Mohanty, Detection of Broken Impeller in Submersible Pump by Estimation of Rotational Frequency from Motor Current Signal, Journal of Vibration Engineering & Technologies, 2020, 8(4), 613–620.
Q.C. Sun, B.W. Shi, et al., Fatigue Strength Evaluation for Remanufacturing Impeller of Centrifugal Compressor Based on Modified Grey Relational Model, Advances in Materials Science and Engineering, 2020, 1236130.
W. Li, Y. Huang, A method for damage detection of a jacket platform under random wave excitations using cross correlation analysis and PCA-based method, Ocean Engineering, 2020, 214, 107734.
F. Asghar, M. Talha, S.Y. Kim, et al., Hotelling T2
Index based PCA Method for Fault Detection in Transient State Processes, Journal of Institute of Control, Robotics and Systems, 2016, 22(4), 276–280.
Cadini F, Sbarufatti C, Corbetta M, et al. Particle filtering-based adaptive training of neural networks for real-time structural damage diagnosis and prognosis [J]. Structural Control and Health Monitoring, 2019(11).
