Rotating machinery is widely applied in various types of industrial applications. As a promising field for reliability of modern industrial systems, early fault diagnosis (EFD) techniques have attracted increasing attention from both academia and industry. EFD is critical to provide appropriate information for taking necessary maintenance actions and thereby prevent severe failures and reduce financial losses. A massive amounts of research work has been conducted in last two decades to develop EFD techniques. This paper reviews and summarizes the research works on EFD of gears, rotors, and bearings. The main purpose of this paper is to serve as a guidemap for researchers in the field of early fault diagnosis. After a brief introduction of early fault diagnosis techniques, the applications of EFD of rotating machine are reviewed in two aspects: fault frequency-based methods and artificial intelligence-based methods. Finally, a summary and some new research prospects are discussed.

Recent works suggest that using knowledge transfer strategies to tackle cross-domain diagnosis problems is promising for achieving engineering diagnosis. This paper presents a diagnosis scheme for rolling bearing under a challenging domain generalization scenario, in which more potential discrepancies among multiple source domains should be eliminated and only normal samples of the target domain are available during the training stage. In order to achieve sufficient generalization performance, a diagnosis scheme combined some a priori diagnosis knowledge and a deep domain generalization network for fault diagnosis (DDGFD) is elaborated. Through the signal preprocessing steps guided by the a priori diagnosis knowledge, the inputs of DDGFD with a primary consistent meaning across domains are constructed from the vibration signal. On this basis, DDGFD would intently release its talent on learning discriminative and domain-invariant fault features from source domains, and then generalize the learned knowledge to identify unseen target samples. On cross-domain tasks organized using broad bearing datasets, the superiority of DDGFD is validated by comparing its performance with various data-driven diagnosis methods.

The cross-domain fault diagnosis problem based on deep domain adaptation (deep DA) has gained great attention in recent years. However, a required but not easily satisfied assumption in many researches that the label space of the source domain and the target domain should be identical limits their applications in practice. In industrial reality, the label space of the target domain may be only a subset of that of the source domain, which is defined as partial DA diagnosis scenario. Focusing on this scenario, this paper proposes a novel diagnosis method named instance weighted mean maximum discrepancy (IWMMD). A new weighting mechanism inspired by instance discrimination is designed to realize DA on shared label space between domains. Also, discrimination structure enhancement for both domains is introduced to encourage better classification ability and safer domain alignment. The effectiveness of IWMMD is verified by two datasets. In the gear dataset, the diagnosis accuracy is 89.65% with a 5.11% improvement. In the bearing dataset, the diagnosis accuracy is 96.28% with a 4.46% improvement. The results and analysis show that the proposed method can reduce the negative transfer effects caused by outlier class samples in source domain and learn a more separable discrimination structure, which is effective in both no-partial and partial diagnosis scenarios and time-efficient.

The health status of the momentum wheel is vital for a satellite. Recently, research on anomaly detection for satellites has become more and more extensive. Previous research mostly required simulation models for key components. However, the physical models are difficult to construct, and the simulation data does not match the telemetry data in engineering applications. To overcome the above problem, this paper proposes a new anomaly detection framework based on real telemetry data. First, the time-domain and frequency-domain features of the preprocessed telemetry signal are calculated, and the effective features are selected through evaluation. Second, a new Huffman-multi-scale entropy (HMSE) system is proposed, which can effectively improve the discrimination between different data types. Third, this paper adopts a multi-class SVM model based on the directed acyclic graph (DAG) principle and proposes an improved adaptive particle swarm optimization (APSO) method to train the SVM model. The proposed method is applied to anomaly detection for satellite momentum wheel voltage telemetry data. The recognition accuracy and detection rate of the method proposed in this paper can reach 99.60% and 99.87%. Compared with other methods, the proposed method can effectively improve the recognition accuracy and detection rate, and it can also effectively reduce the false alarm rate and the missed alarm rate.