Study on the degradation law of harmonic gear drive backlash with wear and assembly errors

To assess the required simplicity of physical law, it is proposed that the finite information quantity (FIQ)-based approach be used. The approach proved to be reliable and accurate when analyzing the results of measuring physical constants. The method is based on the idea that using a finite amount of information in the model enables one to calculate the smallest preliminary and unremovable comparative uncertainty (respectively, relative uncertainty) depending on a qualitative-quantitative set of variables. The method does not require the usually applied constraints to the input data and works well with numerous statistical assumptions: the normality of the probability distributions of the data, observations, absence of outliers, etc. This paper provides researchers with a tool for analyzing the required level of simplicity of the resulting formulas. The FIQ-based approach is applied to verify the required level of simplicity of different physical laws. 

Simplicity of Physical Laws. Informationaltheoretical Limits

As an important transmission component of industrial robots, the harmonic reducer determines the positioning accuracy, bearing capacity and service life of the robot end-effector. Predicting the performance can grasp the working status in advance and avoid major losses caused by uncertain factors such as component damage. The current paper focuses on a harmonic reducer performance prediction algorithm based on Multivariate State Estimation Technique (MSET) and LargeVis dimensionality reduction. Firstly, an accelerated life test platform is designed to collect multi-dimensional parameters that can characterize the operating state of the harmonic reducer throughout the life cycle. Afterwards, as far as the MSET method is concerned, the fault warning threshold is set according to the residual between the constructed memory matrix of the health state data and the actual observed value. Finally, utilizing LargeVis to reduce the dimensionality of multi-dimensional features, combining with Mahalanobis distance to construct a health index degradation model, and then selecting Long Short-Term Memory (LSTM) network to predict the downward trend of the harmonic reducer. The analysis of the accelerated life test data of the harmonic reducer demonstrates that the proposed method can send out the fault warning signal 18 minutes in advance in the sample with a life of 5.7 hours, and has a strong ability to predict the degradation trend of the harmonic reducer.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09756024.pdf

Harmonic Reducer Performance Prediction Algorithm Based on Multivariate State Estimation and LargeVis Dimensionality Reduction

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

Harmonic reducer is a critical and vulnerable component of industrial robots. Its dynamic rules are difficult to express because harmonic reducers always operate under continuously varying operating conditions in practical application scenarios. How to achieve fault diagnosis under dynamic operating parameters is a challenge. This paper proposes a fault diagnosis scheme for harmonic reducer under practical operating conditions. Hidden Markov Model is used to depict the dynamic rules, novel features state transition probability and observation probability are extracted to construct the mapping relationship between external excitation and monitoring signals. A CNN framework is employed for fault recognition based on fault impacts on the mapping relationship. The results of the verification experiment show that the scheme can achieve high accuracy fault diagnosis in dynamic operating conditions by fully utilizing Hidden Markov Model. The mapping relationship can serve as a behavioral model to support digital-twin modeling and health management for industrial robots. 

A fault diagnosis scheme for harmonic reducer under practical operating conditions

The harmonic reducer of the space drive mechanism is one of the critical components of the satellite remote sensing system and its reliability is always required strictly in practice. During the manufacturing and usage processes of the harmonic reducer, uncertainties are ubiquitous and performance degradation is inevitable, which is mainly caused by the wear between the meshed tooth surfaces. They influence the reliability of harmonic reducer. In this paper, a performance margin modeling and reliability analysis for harmonic reducer considering multi-source uncertainties and wear is proposed. Firstly, based on the functional principle and the influence of wear, the performance margin model is established using hysteresis and transmission errors as key performance parameters. Then multi-source uncertainties are analyzed and quantified, including manufacturing errors, uncertainties in operational and environmental conditions, and uncertainties in performance thresholds. Finally, the reliability model is constructed based on the performance margin. A case study of a harmonic reducer is applied and the reliability sensitivity analysis is implemented to show the practicability of the proposed method. The results show that the proposed method can provide some suggestions to the design and manufacturing phases of the harmonic reducer.

/pdf/performance-margin-modeling-and-reliability-analysis-for-3meqxgr2dc.pdf

Performance Margin Modeling and Reliability Analysis for Harmonic Reducer Considering Multi-Source Uncertainties and Wear

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Performance Margin Modeling and Reliability Analysis for Harmonic Reducer Considering Multi-Source Uncertainties and Wear