Scholars and engineers attach great importance to fault detection in mechanical systems due to the unpredictable faults that arise from long-term operations under complex and extreme conditions. The fact that each type of fault embodies unique characteristics makes it challenging to obtain sufficient fault samples, and conventional machine learning methods fail to provide satisfactory fault diagnosis results. To address this issue, a simulation-driven fault detection method has been proposed in this paper. Firstly, the DT model of the gear transmission system was established. An improved multi-objective sparrow search algorithm (MOSSA) was employed to update the model and obtain an adequate number of simulation fault samples as well. Secondly, a two-stage adversarial domain adaptation model with full-scale feature fusion (ADAM-FF) was utilized to align and integrate the features of simulated and generated fault samples. This enables model training and classification of combined samples, facilitating the detection of unknown faults in actual measurements. Lastly, a simulation-driven equipment health index assessment model which accurately and non-destructively evaluates the degradation status of the equipment was introduced. This model effectively quantifies the extent of equipment degradation, thereby facilitating the transfer from the simulation realm to practical engineering applications. To validate the effectiveness of the proposed fault detection method, an experimental study was conducted on the extruder gear reducer of a petrochemical enterprise. The proposed fault detection method has the potential for widespread application across a range of large-scale mechanical equipment. As such, the utilization of this method will enable proactive maintenance planning, ensure safe and stable equipment operations, and minimize energy loss.

中文翻译：

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大型装备齿轮传动系统仿真驱动故障检测

由于机械系统在复杂、极端的条件下长期运行所产生的故障难以预测，因此学者和工程师非常重视机械系统的故障检测。由于每种故障都具有独特的特征，因此很难获得足够的故障样本，而传统的机器学习方法无法提供令人满意的故障诊断结果。为了解决这个问题，本文提出了一种仿真驱动的故障检测方法。首先建立了齿轮传动系统的DT模型。采用改进的多目标麻雀搜索算法（MOSSA）来更新模型并获得足够数量的模拟故障样本。其次，利用具有全尺寸特征融合的两阶段对抗域适应模型（ADAM-FF）来对齐和集成模拟和生成的故障样本的特征。这使得模型训练和组合样本分类成为可能，有利于实际测量中未知故障的检测。最后，提出了一种仿真驱动的设备健康指标评估模型，可以准确、无损地评估设备的退化状态。该模型有效量化了设备退化程度，从而促进从仿真领域向实际工程应用的转移。为了验证所提故障检测方法的有效性，对某石化企业挤出机齿轮减速机进行了实验研究。所提出的故障检测方法具有在一系列大型机械设备中广泛应用的潜力。因此，利用这种方法将能够主动制定维护计划，确保设备安全稳定运行，并最大限度地减少能源损失。