Abstract Skewed distribution and incompleteness of monitored data might cause feature submergence and information loss, rendering the fault diagnosis from imbalanced and incomplete data commonly existing in industrial systems is still an intractable problem. Therefore, in order to improve the accuracy of fault diagnosis from imbalanced and incomplete data, this paper proposes a fusion autoencoder (FAE) network and an ensemble diagnosis scheme. A designed multi-level denoising strategy and a variable-scale resampling strategy are adopted as compensation for information loss and skewed distribution. The multiple FAE networks are constructed by combining the advantages of improved sparse autoencoder (SAE) with denoising autoencoder (DAE) to enhance the adaptability. Different hyper parameters are configured for each FAE to ameliorate the diagnostic flexibility, and Bagging strategy is employed to integrate each network into a complete FAE fault diagnosis model. Furthermore, evaluation criteria are suggested and the application range of the model is tested. Finally, different experiments are conducted to verify the effectiveness and practicability of the proposed method.

中文翻译：

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一种改进的集成融合自编码器模型，用于不平衡和不完整数据的故障诊断

摘要 监测数据的偏态分布和不完整性可能导致特征淹没和信息丢失，使得工业系统中普遍存在的不平衡和不完整数据的故障诊断仍然是一个棘手的问题。因此，为了提高来自不平衡和不完整数据的故障诊断的准确性，本文提出了融合自编码器（FAE）网络和集成诊断方案。采用设计的多级去噪策略和可变尺度重采样策略作为信息丢失和偏态分布的补偿。通过结合改进的稀疏自编码器（SAE）和去噪自编码器（DAE）的优点构建多个FAE网络以增强适应性。为每个 FAE 配置不同的超参数以提高诊断灵活性，并采用 Bagging 策略将每个网络集成到一个完整的 FAE 故障诊断模型中。此外，提出了评价标准，并对模型的应用范围进行了测试。最后，通过不同的实验来验证所提出方法的有效性和实用性。