A Novel Multitask Adversarial Network via Redundant Lifting for Multicomponent Intelligent Fault Detection Under Sharp Speed Variation
A Novel Multitask Adversarial Network via Redundant Lifting for Multicomponent Intelligent Fault Detection Under Sharp Speed Variation
复制标题
一种基于冗余提升的新型多任务对抗网络，用于急速变化下的多组件智能故障检测
DOI：
10.1109/tim.2021.3055821
复制
发表时间：
2021
影响因子：
5.6
通讯作者：
Zhou Zitong
中科院分区：
文献类型：
--
作者：
Shi Zhen;Chen Jinglong;Zi Yanyang;Zhou Zitong
智能故障检测对设备的安全运行起着至关重要的作用，在机械系统中得到了广泛的应用。然而，在高速变化下，信号症状和多部件故障模式的复杂映射使其难以实现。在故障状态下，由于样本不足，分类误差较大。针对这些问题，提出了一种新的多任务冗余提升对抗网络（MRLAN）。首先，受第二代冗余小波变换的启发，建立重构网络，生成高质量的振动信号时频表示，消除速度波动，扩大训练样本；重构网络由转置卷积层、恢复更新层、恢复预测层和合并层组成。然后，构建多任务类别判别器，与距离判别器组成双判别器，分离多组件的特征，从而实现多任务（多组件故障定位和单组件健康评估）。最后，通过生成网络和双鉴别器之间的对抗过程交替优化模型。通过两个实例验证了该方法的有效性，并建立了其他方法来显示其优越性。结果表明，在速度波动大、训练数据少的情况下，MRLAN在多分量故障诊断中具有令人满意的性能。
Playing a vital role in the safe operation of equipment, intelligent fault detection has been extensively applied in mechanical systems. However, the complicated mapping of signal symptoms and multicomponent failure modes under sharp speed variation makes it hard. The classification error is large due to insufficient samples in the fault condition. Aiming at these problems, a novel multitask redundant lifting adversarial network (MRLAN) is proposed. First, inspired by redundant second-generation wavelet transform, a reconstruction network is established to generate high-quality time–frequency representation of vibration signal, thus eliminating speed fluctuations and expanding training samples. The reconstruction network consists of transposed convolution layer, resume update layer, resume predict layer, and merge layer. Then, multitask category discriminator, which forms dual discriminator together with distance discriminator, is constructed to separate the features of multiple components, thereby realizing multiple tasks (multicomponent fault location and single-component health assessment). Finally, the model is alternately optimized through adversarial process between the generation network and the double discriminators. The proposed method is validated by two cases and other methods are also established to show its superiority. The results show satisfactory performance of MRLAN in multicomponent fault diagnosis under sharp speed fluctuation and little training data.