Fault diagnosis is vital to ensuring the security of rotating machinery operations. While fault data obtained from mechanical equipment for this issue are often insufficient and of no labels. In this case, supervised algorithms cannot come into play. Hence, this article proposes a self-supervised simple Siamese framework (SSF) for bearing fault diagnosis based on the contrastive learning algorithm SimSiam which uses a simplified Siamese network to find the distinguishable features of different fault categories. SSF consists of a weight-sharing encoder applied on two inputs, a nonlinear predictor and a linear classifier. SSF learns invariant characteristics of fault samples via maximizing the similarity between two views of each inputted sample. Several data augmentation (DA) methods for vibration signals, which provide different sample views for the model, are also studied, for it is crucial for contrastive learning. After fine-tuning the learned encoder and a linear layer classifier with a small subset of labeled data (1%-5% of the total samples), the network achieves satisfactory performance for bearing fault diagnosis. A series of experiments based on the data from three different scenarios are used to verify the proposed methods, getting 100%, 99.38%, and 98.87% accuracy separately.

Deep learning has demonstrated splendid performance in mechanical fault diagnosis on condition that source and target data are identically distributed. In engineering practice, however, the domain shift between source and target domains significantly limits the further application of intelligent algorithms. Despite various transfer techniques proposed, either they focus on single-source domain adaptation (SDA) or they utilize multisource domain globally or locally, which both cannot address the cross-domain diagnosis effectively, especially with category shift. To this end, we propose globally localized multisource DA for cross-domain fault diagnosis with category shift. Specifically, we construct a GlocalNet to fuse multisource information comprehensively, which consists of a feature generator and three classifiers. By optimizing the Wasserstein discrepancy of classifiers locally and accumulative higher order multisource moment globally, multisource DA is achieved from domain and class levels thus to reduce the shift on domain and category. To refine the classifier at sample level, a distilling strategy is presented. Finally, an adaptive weighting policy is employed for reliable result. To evaluate the effectiveness, the proposed method is compared with multiple methods on four bearing vibration datasets. Experimental results indicate the superiority and practicability of the proposed method for cross-domain fault diagnosis.

Industrial Internet of Things (IIoT) is mainly a data-oriented network, so intelligent processing of massive data is desiderated to realize the interconnection between machines. Currently, deep-learning-based methods are widely applied for intelligent construction of the IIoT, so as to maximize the self-monitoring and self-management capabilities of various machines. However, the quantity and quality of data and the optimization of parameters greatly limit the properties of such methods. As a breakthrough of artificial intelligence (AI), deep reinforcement learning (DRL) provides inspiration and direction, which combines the advantages of deep learning and reinforcement learning to construct an end-to-end fault identification system. Therefore, a novel deep dual reinforcement learning model was proposed, which consisted of an actor model and a critic model. The dual structures avoid the over-self-optimization of the network. The action model continually learns the knowledge of identifying unknown samples by the$\varepsilon $-$greedy$algorithm, while the critic model dynamically adjusts the policy to guide the action model in right training direction. The effectiveness of the proposed method was verified by three bearing data sets. The results indicate that the proposed method enables agents to independently realize precise fault quantitative identification. The establishment of an experience storage unit overcomes the problem of insufficient samples, which avoids blind trial and error of the proposed mode.

Class imbalance issue has been a major problem in mechanical fault detection, which exists when the number of instances presenting in a class is significantly fewer than that in another class. This article focuses on the problem of zero-shot fault detection of rolling bearing, which is the extreme case of class imbalance. Aiming at this problem, a two-stage zero-shot fault recognition method is proposed. First, inspired by the conditional generative adversarial network, a novel feature generating network which is composed of a feature extractor, a discriminator, and a generator is designed to capture the potential distribution of normal samples. Then, the generator will generate abundant pseudofault features by adding an additional sequence to the condition. Second, an improved deep neural network is trained with these synthetic pseudofault features as the classifier. Specially, a condition index is designed to represent different fault classes so that it can recognize the unseen fault samples. Finally, the effectiveness of the proposed method is verified by three datasets and a comparison method is also given to show the superiority. Results show that the feature generation network can effectively detect the typical faults even though the fault data are unavailable during training, which is practical for industrial application.

Recently, intelligent fault diagnosis has achieved fruitful research results. However, the small sample is still the major problem in fault diagnosis owing to lacking fault data of machines. In view of this, a signals augmented semi-supervised learning scheme is proposed for intelligent fault diagnosis in the case of small sample. In the proposed method, fault signal samples are generated by generative adversarial networks (GAN). The fault classifier is trained in a semi-supervised way using the generated samples and a small number of real samples. Besides, attention mechanism is applied in the fault classifier for sensitive feature extraction. The trained fault classifier is capable of accurate fault classification. Results indicate that the proposed method is effective in mechanical fault diagnosis under the small sample condition.

Data imbalance is a major problem in intelligent fault diagnosis. Aiming at this problem, the paper proposed a novel adversarial learning framework for zero-shot fault recognition of mechanical systems. The proposed network consists of three parts which are the feature extractor, the generator and the discriminator. Trained with normal samples, the proposed method is capable of generating unseen fault samples by changing the condition of the generator. After, these synthetic samples are used to train an improved deep neural network for fault recognition. Results show that the proposed method can recognize the unseen faults even though none of fault samples are available during training, which is meaningful for industry application.

Intelligent fault detection is an important application of artificial intelligence and has been widely used in many mechanical systems. The shipborne antenna that is a typical and an important mechanical system plays an irreplaceable role in ships. Considering the tough working environment and heavy background noise, fault detection is difficult for the shipborne antenna. Therefore, the paper presents an intelligent fault detection method via multiscale inner product with locally connected feature extraction for shipborne antenna fault detection. Inspired by inner product principle, this paper takes advantage of inner product to capture fault information in the vibration signals and detect the faults in rolling bearing of the shipborne antenna. Meanwhile, multiscale analysis is employed in two layers of the network to improve the feature extraction ability. The local features under different scales are collected and used for fault classification. Finally, the proposed method is verified by three datasets and comparison methods are also developed to show its superiority. Results show that the proposed method can learn sensitive features directly from raw vibration signals and detect the faults in rolling bearing of shipborne antenna effectively.

Confusion of frequency spectrum under varying speed condition leads to the efficacy degradation of fault recognition for rolling element bearings. Meanwhile, a widely-used method is transforming non-stationary vibration signals into angular domain based on angular re-sampling to reduce influence of speed fluctuation. However, single domain usually cannot describe health information comprehensively. Thus a multi-domain indexes description method is proposed in this paper to improve fault recognition by multi-domain fusion. First the input indexes of multiple domains including time domain, frequency domain, angular domain and order domain are organized to train SOM neural network. Then an optimized fault cognition model is established based on the SOM input planes. Finally, faults are recognized based on multi-domain indexes model and extra complexity indexes are added to enhance recognition of compound faults. Experimental results show that the proposed method can distinguish different faults effectively and have good practical significance.