基于自适应和弦变换旋转蒸发策略的电机轴承未知故障诊断OA

Permanent magnet synchronous motors(PMSMs)are widely used in critical fields,and the operational status of their rolling bearings directly affects the efficiency and reliability of electromechanical systems.However,complex working environments and variable conditions often result in undetected bearing faults.These faults violate the assumptions of traditional data-driven fault diagnosis methods,specifically the requirement for balanced sample sizes across different states and for consistent state categories between the source and target domains,leading to misdiagnosis or missed diagnosis.This study proposes an adaptive chord transformation rotation evaporation strategy(ACTRES)for diagnosing unknown faults in PMSM bearings. ACTRES comprises two core modules designed to enhance diagnostic accuracy and efficiency.First,a rotational evaporation strategy is introduced to mitigate the catastrophic forgetting of old and new samples during lifelong learning.This strategy simulates the solvent-solution separation mechanism in chemical synthesis:it clusters features of old task data using K-means to obtain local prototypes,generates pseudo-samples by fusing Gaussian noise with learnable embedding vectors,and harmonizes the constraints between evaporation loss and state category loss.This process effectively reconstructs the global distribution of known fault categories,thereby improving diagnostic accuracy.Second,an adaptive chord transformation method is inspired by music theory.Chord transposition,such as shifting between bass,midrange,and treble tones,parallels the mapping of different fault sizes.This method enables affine-invariant transfer of fault features.By employing a chord constructor and a global optimal function,it maps fault patterns across varying sizes.The negative impacts of knowledge,memory,and model expansion on diagnostic speed are eliminated. Experimental validation was conducted using the case western reserve university(CWRU)public dataset and a self-built PMSM test platform.The CWRU dataset features SKF6205 bearings with fault sizes of 0.007,0.014,and 0.021 inches.ACTRES achieves an average diagnostic accuracy of over 95.7％for unknown faults.On the self-built platform,tests were carried out under three conditions:constant speed,variable speed,and simultaneous speed-load variation.The constant speed is 1 200 r/min;the variable speeds are 600 r/min,1 200 r/min,and 1 800 r/min;the simultaneous speed-load variation involves 50％ and 100％load changes.Compared with the modified auxiliary classifier generative adversarial network(MACGAN),the theory-guided progressive transfer learning network(TPTLN),and the multi-source information fusion deep self-attention reinforcement learning(MSIF-DSARL),ACTRES demonstrates superior performance. In conclusion,ACTRES effectively addresses the key challenges in unknown fault diagnosis.Future work will integrate motor structure models to extend the method to unknown-fault diagnosis across different motor types,such as asynchronous and synchronous reluctance motors.