The theoretical developments of data-driven fault diagnosis methods have yielded fruitful achievements and significantly benefited industry practices. However, most methods are developed based on the assumption of data balance, which is incompatible with engineering scenarios. First, the normal state accounts for the majority of the equipment’s lifespan; second, the probability of various faults varies, both of which result in an imbalance in the data. The consequence of data imbalance in intelligent fault diagnosis methods has attracted extensive attention from the research community, and a significant number of papers have been published. Nevertheless, a comprehensive review of achievements in this field is still missing, and the research perspectives have not been thoroughly investigated. To end this, we review and discuss all the research achievements in fault diagnosis under data imbalance in this paper, based on our best knowledge. First, the existing imbalanced learning methods are classified into three categories: data processing methods, model construction methods, and training optimization methods. Then, the three methodologies are introduced and discussed in detail: the data processing method is to optimize the inputs of the intelligent fault diagnosis model so that the imbalance rate of the sample set involved in training is reduced; the model construction method is to design the structure and the features of the intelligent fault diagnosis model so that the model itself is resistant to the effects of imbalance; the training optimization method is an optimisation of the training process for intelligent fault diagnosis models, raising the importance of the minority class in the training. Finally, this paper summarizes the prospects of the imbalanced learning problem in intelligent fault diagnosis, discusses the possible solutions, and provides some recommendations.

WC-Co alloys have enjoyed great practical significance owing to their excellent properties during the past decades. Despite the advantages, however, recently there have been concerns about the challenges associated with the use of Co, i.e. price instability, toxicity and properties degeneration, which necessitates the fabrication of binderless tungsten carbide (BTC). On the other hand, BTC or BTC composites, none of them, to date has been commercialized and produced on an industrial scale, but only used to a limited extent for specialized applications, such as mechanical seals undergoing high burthen as well as high temperature electrical contacts. There are two challenges in developing BTC: fully densifying the sintered body together with achieving a high toughness. Thus, this review applies towards comprehensively summarize the current knowledge of sintering behavior, microstructure, and mechanical properties of BTC, highlighting the densification improving strategies as well as toughening methods, so as to provide reference for those who would like to enhance the performance of BTC with better reliability advancing them to further wide applications and prepare the material in a way that is environment friendly, harmless to human health and low in production cost. This paper shows that the fabrication of highly dense and high-performance BTC is economically and technically feasible. The properties of BTC can be tailored by judiciously selecting the chemical composition coupled with taking into careful account the effects of processing techniques and parameters.

The effective fault diagnosis of bearing can guarantee the safety of rotating machinery and is very important for its stable operation. The information fusion of multi-sensor data has been a feasible method to enhance the performance of fault diagnosis. However, how to fuse the joint information from different channels or even different kinds of sensors is still an important challenge. The present study proposes a novel multi-sensor information coupling network (MICN) for bearing fault diagnosis, which handles the signals from the same or different types of sensors, and the deeper features can be extracted from multi-sensors independently and simultaneously fused layer by layer. Especially, during the multi-layer feature fusion process, a novel feature-level information coupling model is developed based on the mutual attention mechanism. Finally, to validate the efficiency of the proposed method, several different experiments are designed, and the results show validity and superiority.

In contrast to fault diagnosis relying solely on a single sensor, the method of multi-sensor information fusion for fault diagnosis (MSIFFD) broadens the spectrum of available information sources. It is renowned for its high accuracy and reliability, traits that have attracted growing attention within the research community and led to the generation of a substantial body of publications. However, there is currently a lack of a comprehensive and systematic review in this domain. Thereby, this review aims to thoroughly explore all research achievements in the field of MSIFFD. At the outset, an analysis is undertaken to delineate the fusion level of multi-sensor information, with a specific focus on its location and types of inputs and outputs within the information flow. Subsequently, an examination of the six primary fundamental operations for amalgamating multi-sensor information is undertaken to clarify the motivations and processes involved in information fusion across various operations. Following this, factors influencing fusion diagnostics and strategies aimed at improving their performance are examined to explore approaches for enhancing fusion accuracy. The subsequent sections delve into the analysis of sensor types and application scenarios, providing a reference guide for practical applications. Finally, this review outlines potential future challenges in the field of MSIFFD and provides a range of recommendations and possible solutions for consideration.

Cage stability directly affects the dynamic performance of rolling bearing, which, in turn, affects the operating state of rotating equipment. The random collision between the rolling elements and the cage pocket is the main reason for cage instability. In this paper, from the perspective of the relative sliding velocity between the rolling elements and the bearing raceway, the interactions of the rolling elements and the cage pockets were analyzed, and the four zones with different collision features were defined. On this basis, and on the basis of the bearing dynamics model, the interaction of two adjacent rolling elements and the cage pockets in the a’–b’ area is discussed, and the peak impact force of the adjacent two balls and the cage pockets was investigated in terms of the rotation speed, radial load, acceleration/deceleration, and materials. When the ball runs close to the loaded zone, the probability of multiball random collision increases, which leads to an increase in the cage instability. At the entrance of the loaded zone, the peak impact force has the greatest impact on the cage stability during the acceleration process. Compared to the radial load applied to the bearing, the peak impact force is more sensitive to the bearing speed changes. The multiball collision analysis method provides a new idea for the research of cage stability.