Lifetime estimation of harmonic reducer for manufacturing robot using accelerated life test

Lee, Joo-Hong; Cho, You‐Hee; Kim, Dau-Sik; Park, Jong-Won

doi:10.1007/s12206-022-0520-0

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…A lot of existing research using accelerating test data to verify their prognostic methods [50][51][52], and their studies show that features extracted in accelerating test data can be used to validate the remaining useful life, indicate the degradation process, and evaluate the reliability. In our case, we need to make sure that the test of harmonic reducers cannot exceed the tolerance limit [53]. In this case, we use the stress limit of the fexible gear of harmonic reducer to be the acceleration stress level limit of accelerated testing [54].…”

Section: Accelerated Life Test Of Harmonic Reducers As Shown Inmentioning

confidence: 99%

Discovering New Prognostic Features for the Harmonic Reducer in Remaining Useful Life Prediction

Jiang

et al. 2023

Shock and Vibration

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Vibration and current signals are widely used in fault diagnosis and life prediction of electromechanical transmission systems. However, due to the complex working environment of harmonic reducer in the industrial robot, using a single signal for failure analysis or life prediction may risk to false alarming due to lacking of fault information. In the early operation stage of equipment, fault information contained in the signal is too weak to be extracted. In addition, there is certain correlation among the features, which can lead to meaningless superposition of the calculation process. Therefore, a new health index construction method is proposed integrating the current signal and vibration signal and reducing the redundancy among the features in multidomains and can effectively enhance the fault information. In addition, in view of the local optimum and slow speed caused by the random initialization of the network model, an improved life prediction method is proposed to optimize BP neural network to improve the prediction performance. The proposed method is verified by the test data of the harmonic reducer. Results show that the proposed method can predict the remaining useful life of the harmonic reducer more accurately and effectively.

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