Journal ArticleDOI
Particle swarm optimization algorithm to solve the deconvolution problem for rolling element bearing fault diagnosis.
TLDR
The study of experimental bearing fault signal shows that the PSO based deconvolution methods delivered better performance for rolling element bearing fault detection than the traditional deconVolution methods.Abstract:
Extraction of the fault related impulses from the raw vibration signal is important for rolling element bearing fault diagnosis. Deconvolution techniques, such as minimum entropy deconvolution (MED), MED adjusted (MEDA) and maximum correlated kurtosis deconvolution (MCKD), optimal MED adjusted (OMEDA) and multipoint optimal MED adjusted (MOMEDA), are typical techniques for enhancing the impulse-like component in the fault signal. This paper introduces the particle swarm optimization (PSO) algorithm to solve the filter of deconvolution problem. The proposed approaches solve the filter coefficients of the deconvolution problems by the PSO algorithm, assisted by a generalized spherical coordinate transformation. Compared with MED, MEDA, and OMEDA, the proposed PSO-MED and PSO-OMEDA can effectively overcome the influence of large random impulses and tend to deconvolve a series of periodic impulses rather than a signal impulse. Compared with MCKD and MOMEDA, the proposed PSO-MCKD and PSO-MOMEDA can achieve good performances even when the fault period is inaccurate. The effectiveness of the proposed methods is validated by the simulated signals. The study of experimental bearing fault signal shows that the PSO based deconvolution methods delivered better performance for rolling element bearing fault detection than the traditional deconvolution methods. Additionally, the proposed methods are compared with the following two popular signal processing methods: the ensemble empirical mode decomposition (EEMD) and fast kurtogram, which are used to highlight the improved performance of the proposed methods.read more
Citations
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Journal ArticleDOI
Intelligent fault diagnosis among different rotating machines using novel stacked transfer auto-encoder optimized by PSO.
TL;DR: A novel method using novel stacked transfer auto-encoder (NSTAE) optimized by particle swarm optimization (PSO) to enable the pre-trained source-domain NSAE to adapt to the target-domain samples.
Journal ArticleDOI
A review on the application of blind deconvolution in machinery fault diagnosis
Archana Rajmane,Yonghao Miao,Yonghao Miao,Boyao Zhang,Jing Lin,Ming Zhao,Hanyang Liu,Zongyang Liu,Hao Li +8 more
TL;DR: This paper provides a comprehensive review of blind deconvolution methods from history to state-of-the-art methods and finally to research prospects, as well as provides a survey and summarize the current progress of BDMs applied in machinery fault diagnosis.
Journal ArticleDOI
A novel feature extraction method for bearing fault classification with one dimensional ternary patterns.
TL;DR: A novel feature extraction method for bearing faults called one-dimensional ternary pattern (1D-TP) is applied, which uses patterns obtained from comparisons between neighbors of each value on vibration signals to identify the size (mm) of the fault.
Journal ArticleDOI
Constrained design optimization of selected mechanical system components using Rao algorithms
R.V. Rao,R.B. Pawar +1 more
TL;DR: The comparison of results shows the ability and the efficiency of Rao algorithms for solving complex design optimization problems of mechanical components.
Journal ArticleDOI
Adaptive maximum second-order cyclostationarity blind deconvolution and its application for locomotive bearing fault diagnosis
TL;DR: An adaptive CYCBD (ACYCBD) is presented, which can extract the weak impulses submerged in the raw vibration signal without any prior information about the period by employing it on the synthesized signals and experimental data collected from a locomotive bearing test rig.
References
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Proceedings ArticleDOI
Particle swarm optimization
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Journal ArticleDOI
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Zhaohua Wu,Norden E. Huang +1 more
TL;DR: The effect of the added white noise is to provide a uniform reference frame in the time–frequency space; therefore, the added noise collates the portion of the signal of comparable scale in one IMF.
Journal ArticleDOI
Fast computation of the kurtogram for the detection of transient faults
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Journal ArticleDOI
Particle swarm optimization (PSO). A tutorial
Federico Marini,Beata Walczak +1 more
TL;DR: The potential of particle swarm optimization for solving various kinds of optimization problems in chemometrics is shown through an extensive description of the algorithm (highlighting the importance of the proper choice of its metaparameters) and by means of selected worked examples in the fields of signal warping, estimation robust PCA solutions and variable selection.
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