A fault diagnosis scheme for harmonic reducer under practical operating conditions

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.