Tacholess Speed Estimation in Order Tracking: A Review With Application to Rotating Machine Fault Diagnosis

Lu, Siliang; Yan, Ruqiang; Liu, Yongbin; Wang, Qunjing

doi:10.1109/tim.2019.2902806

Cited by 169 publications

(66 citation statements)

References 120 publications

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“…The phase space of the features after dimension reduction and fusion is reconstructed to form the performance degradation index with accurately describing the running state and declining trend of rolling bearings. At the same time, the KELM is a new learning algorithm with single hidden layer feedforward neural network [34], which has the advantages of strong generalization learning ability and fast training speed, and has been successfully applied to fault diagnosis, power load prediction and wind power prediction [35][36][37]. Therefore, it is used to establish the performance degradation prediction model of rolling bearings.…”

Section: Introductionmentioning

confidence: 99%

A New Performance Degradation Evaluation Method Integrating PCA, PSR and KELM

Zhang

Guo³

et al. 2021

IEEE Access

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Section: Introductionmentioning

confidence: 99%

A New Performance Degradation Evaluation Method Integrating PCA, PSR and KELM

Zhang

Guo³

et al. 2021

IEEE Access

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“…All these methods use the mechanical position information to carry out the OT. The position must either be extracted from a sensor or estimated, this results in three OT techniques which are perfectly detailed in [2][3][4]:…”

Section: Introductionmentioning

confidence: 99%

A Tacholess Order Analysis Method for PMSG Mechanical Fault Detection with Varying Speeds

et al. 2021

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This article deals with the detection of mechanical faults in synchronous machines from single current measurement at variable speed. The proposed approach is based on an order tracking method in which the analysis signal is sampled as a function of the mechanical angle. In this case, the spectral components become independent of the speed and the frequency analysis can be exploited. Order tracking is generally implemented from a position measurement. In this work we present a method that allows us to estimate this position and the analysis signal from only one current measurement. The proposed approach allows an intuitive adjustment of the algorithm parameters. Secondly, a statistical method is used to finalize the diagnosis. At variable speed, this type of method is difficult to implement and we show that order tracking makes it possible to simplify the analysis. The procedure is tested in simulation and on a experimental test bench.

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“…The complexity of operation environment makes gear failure occur frequently, which is very easy to cause equipment failure [2,3]. The fault signal detection of gearbox under multiple working conditions is of significant practical meanings to monitor the occurrence of serious faults and guarantee the normal operation of mechanical equipment [4][5][6]. Because the change of gear operation condition will cause the change of vibration signal characteristics, it is difficult to extract the gear failure characteristics under variable load effectively by traditional methods [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%