Fault diagnosis based on Walsh transform and rough sets

Xiang, Xiuqiao; Zhou, Jing; Li, Chaoshun; Li, Qingqing; Luo, Zhimeng

doi:10.1016/j.ymssp.2008.10.004

Cited by 40 publications

(13 citation statements)

References 20 publications

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“…These test data were simulated by employing electro-discharge machining with the defect sizes 0.007in, 0.014 in, 0.021 in, 0.028 in, and 0.040 in. For each kind of working condition, signals were measured under rotating speed of 1730 r/min, 1750 r/min, 1772 r/min, and 1797 r/min respectively, with a sampling frequency of 12 KHz per channel by using the accelerometer attached to the 12 o'clock position at the drive end bearing of the motor housing with magnetic bases [11].…”

Section: Experimental Datamentioning

confidence: 99%

Intelligent Fault Diagnosis of Rotating Machinery Using Support vector Machine and Improved ABC

Pan¹,

Xiao²,

Hu³

2019

Proceedings of the 2019 International Conference on Modeling, Simulation and Big Data Analysis (MSBDA 2019)

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An intelligent fault diagnosis method by means of local mean decomposition (LMD), support vector machine (SVM) and improved artificial swarm (IABC) is proposed in this paper. Firstly, the vibration signals are decomposed by means of LMD method and frequency-domain and time-domain statistical characteristics of fault information are extracted. Then, a classifier model is present, which combines IABC and SVM to improve classification accuracy. Finally, SVM model identifies different fault situations adopting the optimal features and model parameters. The experiment's result shows the effectiveness of the proposed method in fault feature extraction and fault diagnosis of rolling bearings.

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Section: Experimental Datamentioning

confidence: 99%

Intelligent Fault Diagnosis of Rotating Machinery Using Support vector Machine and Improved ABC

Pan¹,

Xiao²,

Hu³

2019

Proceedings of the 2019 International Conference on Modeling, Simulation and Big Data Analysis (MSBDA 2019)

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“…Since the detailed information about the experimental dataset and the specific setup of the experiment are clearly shown and high cited in Refs. [34][35][36][37], experiments are respectively followed the experimental setup as the work they done. Our implementation is carried out in the Matlab of 7 version environment and Windows 7 operating system on Inter(R) core(TM) i3-4150 CPU @ 3.5 GHz Processor running at 8.0 GB RAM.…”

Section: Experimental Analysis and Comparisonsmentioning

confidence: 99%

“…[34] With the same experimental setup as mentioned in Ref. [34], three classes of fault signals including ball fault, inner race fault and outer race fault (Load Zone Centered at 12:00) with the defect sizes of 0.007 in. at the drive end bearing are collected for experiment, which are described in Table 4.…”

Section: Casementioning

confidence: 99%

Intelligent fault diagnosis of roller bearings with multivariable ensemble-based incremental support vector machine

Zhang

Wang

Chen

2015

Knowledge-Based Systems

157

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“…The knowledge-based approach uses various graphical models for diagnostic knowledge representation and infer, for example, fault tree [8], signed directed graph [9], Petri net [10], Bayesian network [11][12][13] and signal-flow graph [14,15], etc. The datadriven approach is preferred when the system monitoring data is available, and various artificial intelligent (AI) methods (e.g., neural network [16], support vector machine [17,18], fuzzy set [19], rough set [20]) are illustrative of data-driven techniques.…”

Section: Introductionmentioning

confidence: 99%

An incremental FGRA-based fault diagnosis method

Zhang

Liu

et al. 2014

Proceeding of the 11th World Congress on Intelligent Control and Automation

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In order to effectively solve the uncertainty smallsamples fault diagnosis problem, a practical data-driven greybased fault detection and diagnosis (FDD) method for complex equipments is investigated. Firstly, an improved fuzzy-grey relational analysis (FGRA) technique is proposed by introducing dynamic identification coefficient and fuzzy relational weight. Compared with the traditional Deng's grey relational analysis (DGRA) technique, the proposed FGRA technique not only can strengthen the veracity and reliability but also can reduce the dependence of uncertain man-made identification and weight coefficient. Secondly, a simple and practical FGRA-based fault diagnosis process is designed. It belongs to a data-driven analytic method which does not need to consider the either statistic assumptions or distributions of diagnosis variables. Finally, the validity and practicability of the proposed FGRA-based method is demonstrated by a example of rotor fault diagnosis, and the results show that the proposed method is more effective than the DGRA-based method. Index Terms -fault detection and diagnosis (FDD); fuzzy-grey relational analysis (FGRA); dynamic identification coefficient; fuzzy relational weight; diesel engine.

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Fault diagnosis based on Walsh transform and rough sets

Cited by 40 publications

References 20 publications

Intelligent Fault Diagnosis of Rotating Machinery Using Support vector Machine and Improved ABC

Intelligent Fault Diagnosis of Rotating Machinery Using Support vector Machine and Improved ABC

Intelligent fault diagnosis of roller bearings with multivariable ensemble-based incremental support vector machine

An incremental FGRA-based fault diagnosis method

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