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. The proposed method handles intramodality fusion, intermodality fusion, and decision fusion in a unified framework composed of multiple deep autoencoders (AEs) and a skip-connected AE. Specifically, the first module restores missing sources to construct a complete modality, thus advancing the secondary reconstruction. Different from vanilla reconstruction-based methods, the proposed method minimizes reconstruction loss and meanwhile maximizes the dissimilarity of representations in two latent spaces. Utilizing reconstruction errors and latent representation discrepancy, the anomaly score is acquired. At decision level, the model performance can be further enhanced via anomaly score fusion. To demonstrate the effectiveness, extensive experiments are carried out on multivariate time-series data from static ignition of several LREs. The results indicate the superiority and potential of the proposed method for AD with missing sources for LRE.

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. Nowadays, the research on S&I-IFD has achieved fruitful results, but a review of the latest achievements is still lacking, and the future research directions are not clear enough. To address this, we review the research results on S&I-IFD and provides some future perspectives in this paper. The existing research results are divided into three categories: the data augmentation-based, the feature learning-based, and the classifier design-based. Data augmentation-based strategy improves the performance of diagnosis models by augmenting training data. Feature learning-based strategy identifies faults accurately by extracting features from small & imbalanced data. Classifier design-based strategy achieves high diagnosis accuracy by constructing classifiers suitable for small & imbalanced data. Finally, this paper points out the research challenges faced by S&I-IFD and provides some directions that may bring breakthroughs, including meta-learning and zero-shot learning.

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. Then the evidence features from multiple domains are integrated to obtain the optimal Fusion-HI by the modified de-noising auto-encoder (MDAE). Finally, a hybrid prediction network is designed combining with the gate attention algorithm, which consists of multi-scale convolution layers, bi-directional GRU layers, smoothed and de-noised layers, and regression layers. Three experimental whole lifetime data and one industrial entire life cycle data are analyzed to validate the feasibility of the proposed approach in two case studies respectively. Relevant experimental results indicate that the Fusion-HI is capable to sensitively characterize the degradation state of equipment, while the prediction accuracy of presented heterogeneous model is superior to that of conventional prediction approaches.

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., directly using raw machinery signal as the input of model. In this work, we analyze from two aspects: model mechanism and mechanical monitoring signal, it shows the limitation of learning raw data directly, which led to the research idea of improving the generalization ability of model by multi-frequency information augmentation. In order to make machinery intelligent model capture multi-frequency information more directly and actively, Multi-Frequency Augmentation framework is proposed in this paper. Firstly, we proposed a data augmentation method to split the raw sample into sample pair. And we could choose to further augment the dataset by Frequency Components Recombination, especially under few-shot scenes. Then, Multi-Frequency Capture Network is built to achieve feature augmentation by learning the sample pair. Finally, fault diagnosis is performed on testing set. The effectiveness and compatibility of Multi-Frequency Augmentation framework is verified with two experiments, which also verifies the feasibility of the proposed research idea. In addition, it could also achieve competitive performance with latest literature methods. Further discussion indicate that the proposed framework provides a new perspective to analyze the model and dataset, which has good application potential.

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. For this purpose, this paper reviews the related research results on small-sample-focused fault diagnosis methods using the GAN. First, a systematic description of the GAN, and its variants, including structure-focused and loss-focused improvements, are introduced in the paper. Second, the paper reviews the related GAN-based intelligent fault diagnosis methods and classifies these studies into three main categories, deep generative adversarial networks for data augmentation, adversarial training for transfer learning, and other application scenarios (including GAN for anomaly detection and semi-supervised adversarial learning). Finally, the paper discusses several limitations of existing studies and points out future perspectives of GAN-based applications.

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. Cut-Flip is used directly on the raw sample without parameter selection. Mix-Normal mixes the data and labels of a random sample with a random normal sample at a certain ratio. The proposed SDA is verified on two bearing datasets with some popular intelligent diagnosis networks. Besides, we also design a Batch Normalization CNN (BNCNN) to learn the small dataset. Results show that SDA can significantly improve the classification accuracy of BNCNN by 10%-30% under 1-8 samples of each class. The proposed method also shows a competitive performance with existing advanced methods. Finally, we further discuss each data augmentation method through a series of ablation experiments and summarize the advantages and disadvantages of the proposed SDA.

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. The regenerative repair, functional recovery and the situation of target organ, etc. were evaluated by neuromorphic metrology index, fluorescence gold retrograde tag, animal behavior index (sciatic nerve index). The result was the situation of nerve regenerative repair and functional recovery in high dose ginsenoside Rg1 group was obviously superior to other groups, the recovery of sciatic nerve index, target muscle, etc. were fine and mostly close to normal. It was concluded that ginsenoside Rg1 could effectively promote the regenerative repair of peripheral nerve injury, and accelerate the recovery of its nerve function. It could also promote the regeneration of peripheral nerve and the recovery of its nerve function.