A sequential fuzzy diagnosis method for rotating machinery using ant colony optimization and possibility theory

Sun, Hao; Li, Ké; Chen, Peng; Wang, Huaqing; Xueliang, Ping; Cao, Yi

doi:10.1007/s12206-014-0112-8

Cited by 6 publications

(6 citation statements)

References 20 publications

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“…It is seen in Fig. 5 that the majority of the studies are considered bearing faults [ [71] , [72] , [73] , [74] , [75] ] followed by rotor faults [ [76] , [77] , [78] , [79] , [80] ], gear faults [ [81] , [82] , [83] , [84] , [85] ], shaft faults [ 21 , 46 , 71 , 72 , 86 ], stator [ 7 , 25 , 87 ], and other component faults [ 28 , [88] , [89] , [90] ]. The pie chart shown in Fig.…”

Section: Fault Diagnosis and Prognosis In Rotating Machinerymentioning

confidence: 99%

Machine learning for fault analysis in rotating machinery: A comprehensive review

Daş

Birant

2023

Heliyon

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Section: Fault Diagnosis and Prognosis In Rotating Machinerymentioning

confidence: 99%

Machine learning for fault analysis in rotating machinery: A comprehensive review

Daş

Birant

2023

Heliyon

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“…In fault diagnosis, the primary goal is to minimize the cost, so test cost is a key element of consideration. And the information gain is introduced to estimate the effect of a test, which is given by IG F, t j = − P F jp log 2 P F jp +P F jf log 2 P F jf (8) The value of information gain represents the ambiguous fault group isolation capability of the test t j , and the larger means the better. We use the information gain per unit cost to evaluate the test action, so the reward function is defined as…”

Section: Fault Diagnosis Model Based On Pomdpmentioning

confidence: 99%

“…But the combination explosion, large amount of calculation, easy to get stuck in backtracking and iteration proliferation of them can't meet the current diagnostic requirements in complexity systems. In the recent years, the intelligent algorithms such as the genetic algorithm [7], ant colony algorithm [8], particle swarm optimization [9], support vector machine [10] applied in sequential diagnosis have been advised to get the optimal solution in large complex real-world systems for their powerful calculation ability and optimization mechanism.…”

Section: Introductionmentioning

confidence: 99%

A POMDP-Based Optimization Method for Sequential Diagnostic Strategy With Unreliable Tests

et al. 2019

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This paper considers the sequential fault diagnosis problem with unreliable tests which exist widely in practice. This problem involves real-time inference of the most likely set of failure sources, i.e., fault state, based on unreliable test outcomes. The purpose of this paper is to optimize test set and diagnostic strategy so as to cut down the test cost while isolating the fault accurately, and a method for optimal diagnosis strategy based on partially observable Markov decision process (POMDP) is presented. The components of the POMDP tailed to optimizing the diagnostic strategy are specified, and the solution to the POMDP-based model, namely the optimal strategy, is obtained to describe the optimal test sequence for fault diagnosis. The performance of the proposed method is evaluated with simulation experiments. All the results indicate that this method performs good in diagnostic efficiency and accuracy, even compared with the strategies of traditional methods.INDEX TERMS Sequential fault diagnosis, optimal diagnostic strategy, unreliable tests, POMDP.

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“…Venugopal et al [7] present a signal processing technique for machine performance monitoring which exploits fluctuations in phase angles of machine rotational frequency signals to determine their dynamic temporal coherence. Sun et al [8] propose a fault diagnosis method for rotating machinery using ant colony optimization and possibility theory. Mueller and Pratt [9] present the theory and practical application for using radio frequency polarimetry in analyzing rotating machines.…”

Section: Related Workmentioning

confidence: 99%

“…. , 8), where is the order. Similar to Daubechieds wavelet, Symlets wavelet does not have specified algebraic expression.…”

Section: Fault Diagnosis With Wavelet Transformmentioning

confidence: 99%

Condition Monitoring and Fault Diagnosis for an Antifalling Safety Device

Yang

Liang

2015

Shock and Vibration

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There is a constant need for the safe operation and reliability of antifalling safety device (AFSD) of an elevator. This paper reports an experimental study on rotation speed and catching torque monitoring and fault diagnosis of an antifalling safety device in a construction elevator. Denoising the signal using wavelet transform is presented in this paper. Based on the denoising effects for several types of wavelets, the sym8 wavelet basis, which introduces the high order approximation and an adaptive threshold, is employed for denoising the signal. The experimental result shows a maximum data error reduction of 7.5% is obtained and SNRs (signal-to-noise ratio) of rotation speed and catching torque are improved for 3.9% and 6.4%, respectively.

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A sequential fuzzy diagnosis method for rotating machinery using ant colony optimization and possibility theory

Cited by 6 publications

References 20 publications

Machine learning for fault analysis in rotating machinery: A comprehensive review

Machine learning for fault analysis in rotating machinery: A comprehensive review

A POMDP-Based Optimization Method for Sequential Diagnostic Strategy With Unreliable Tests

Condition Monitoring and Fault Diagnosis for an Antifalling Safety Device

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