Unsupervised Multimodal Anomaly Detection With Missing Sources for Liquid Rocket Engine.

To achieve reliable and automatic anomaly detection (AD) for large equipment such as liquid rocket engine (LRE), multisource data are commonly manipulated in deep learning pipelines. However, current AD methods mainly aim at single source or single modality, whereas existing multimodal methods cannot effectively cope with a common issue, modality incompleteness. To this end, we propose an unsupervised multimodal method for AD with missing sources in LRE system.

Generative adversarial network in mechanical fault diagnosis under small sample: A systematic review on applications and future perspectives.

Intelligent fault diagnosis has been a promising way for condition-based maintenance. However, the small sample problem has limited the application of intelligent fault diagnosis into real industrial manufacturing. Recently, the generative adversarial network (GAN) is considered as a promising way to solve the problem of small sample.

Multi-Frequency Augmentation framework via information active capture for machinery intelligent fault diagnosis.

Data-driven methods, especially deep neural network, received increasing attention in machinery fault diagnosis field. Many works focus on how to design effective model while ignoring a fundamental problem, i.e.

Heterogeneous bi-directional recurrent neural network combining fusion health indicator for predictive analytics of rotating machinery.

Data-driven intelligent methods arise the increasing demand for predictive analytics to evaluate the operational reliability and natural degradation of rotating machinery. Nevertheless, accurate and timely predictive analytics is still regarded as an extremely challenging mission, because the quality of predictive maintenance depends not only on the capability of intelligent model, but also on the construction of effective health indicators To overcome this issue, a novel heterogeneous bi-directional gated recurrent unit (GRU) model combining with fusion health indicator (Fusion-HI) is proposed for predictive analytics in this paper. First, the support evidence space is constructed to reflect the operating state of mechanical equipment.

Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions.

The research on intelligent fault diagnosis has yielded remarkable achievements based on artificial intelligence-related technologies. In engineering scenarios, machines usually work in a normal condition, which means limited fault data can be collected. Intelligent fault diagnosis with small & imbalanced data (S&I-IFD), which refers to build intelligent diagnosis models using limited machine faulty samples to achieve accurate fault identification, has been attracting the attention of researchers.

SDA: Regularization with Cut-Flip and Mix-Normal for machinery fault diagnosis under small dataset.

Data-driven intelligent diagnosis model plays a key role in the monitoring and maintenance of mechanical equipment. However, due to practical limitations, the fault data is difficult to obtain, which makes model training unsatisfactory and results in poor testing performance. Based on the characteristics of 1-D mechanical vibration signal, this paper proposes Supervised Data Augmentation (SDA) as a regularization method to provide more effective training samples, which includes Cut-Flip and Mix-Normal.

Protection of ginsenoside Rg1 on central nerve cell damage and the influence on neuron apoptosis.

This paper aimed to verify the function of ginsenoside in the repair of peripheral nerve injury through the model of sciatic nerve injury in rat. The method was to prepare the model of SD rat injury of sciatic nerve, and to conduct treatment with different dose of ginsenoside Rg1. At the same time, the control group was established.