Performance degradation assessment methodology of harmonic reducer by using low-frequency time series data and genetic programming

Abstract

The harmonic reducer, as an important transmission component in the industrial robot, determines the positioning accuracy, carrying capacity, and service life of the industrial robot. However, the performance and reliability of harmonic reducer will gradually decline or even fail under long-time and high-intensity operations. The sudden stop and failure of the industrial robot will directly affect production efficiency. In this paper, a data-driven method for performance degradation assessment (PDA) of the harmonic reducer is studied. An accelerated degradation test rig was set up to collect the run-to-failure datasets of harmonic reducer, including process data, such as torque and speed, and non-process data, such as vibration signals, which were captured under a low sampling frequency. Firstly, a sliding window-based kurtosis and root mean square (RMS) are applied to non-process data, which can detect the incipient failure time (IFT) of harmonic reducer. Subsequently, a data-level fusion of process data is conducted based on genetic programming to construct a monotonic health index for degradation assessment and trend analysis. The experimental study shows that the proposed method is effective to realize the PDA of harmonic reducer.