Exploiting Phase Fluctuations to Improve Machine Performance Monitoring

Venugopal, Shreya; Wagstaff, Ronald A.; Sharma, J. P.

doi:10.1109/tase.2006.879918

Cited by 4 publications

(2 citation statements)

References 20 publications

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“…Li et al [6] propose an intelligent diagnosis method for condition diagnosis of rotating machinery by using wavelet transform and ant colony optimization, in order to detect faults and distinguish fault types at an early stage. 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.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

confidence: 99%

Condition Monitoring and Fault Diagnosis for an Antifalling Safety Device

Yang

Liang

2015

Shock and Vibration

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“…Condition monitoring is vital in machine maintenance, especially in the manufacturing environment for safe-guarding the efficiency and reliability of manufacturing machinery [1]. It is important to have a proper maintenance strategy in order to avoid machine or process failures; therefore minimizing the overall production time and cost [2].…”

Section: Introductionmentioning

confidence: 99%

Condition Monitoring of Broken Rotor Bars Using a Hybrid FMM-GA Model

Seera

Lim

Loo

2014

Neural Information Processing

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Abstract.A condition monitoring system for induction motors using a hybrid Fuzzy Min-Max (FMM) neural network and Genetic Algorithm (GA) is presented in this paper. Two types of experiments, one from the finite element method and another from real laboratory tests of broken rotor bars in an induction motor are conducted. The induction motor with broken rotor bars is operated under different load conditions. FMM is first used for learning and distinguishing between a healthy motor and one with broken rotor bars. The GA is then utilized for extracting fuzzy if-then rules using the don't care approach in minimizing the number of rules. The results clearly demonstrate the effectiveness of the hybrid FMM-GA model in condition monitoring of broken rotor bars in induction motors.

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