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Prediction of remaining useful life (RUL) is greatly significant for improving the safety and reliability of manufacturing equipment. However, in real industry, it is difficult for RUL prediction models trained on a small sample of faults to obtain satisfactory accuracy. To overcome this drawback, this paper presents a long short-term memory (LSTM) neural network with transfer learning and ensemble learning and combines it with an unsupervised health indicator (HI) construction method for remaining-useful-life prediction. This study consists of the following parts: (1) utilizing the characteristics of deep belief networks and self-organizing map networks to translate raw sensor data to a synthetic HI that can effectively reflect system health; and (2) introducing transfer learning and ensemble learning to provide the required degradation mechanism for the RUL prediction model based on LSTM to improve the performance of the model. The performance of the proposed method is verified by two bearing datasets collected from experimental data, and the results show that the proposed method obtains better performance than comparable methods.

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The solid-liquid two-phase abrasive flow precision machining technology is widely used in aerospace, precision machinery, the automotive industry and other fields, and is an advanced manufacturing technology that effectively improves the inner surface quality of workpieces. In this paper, the fifth-order variable-diameter pipe parts are researched. By discussing the collision between the abrasive particles and the wall surface, it is revealed that the material removal of the workpiece is caused by plastic deformation, and the mechanism of precision machining of the abrasive flow is clarified. Through numerical analysis and experimental research, it is found that the incident angle can affect the precision machining quality of the abrasive flow. When the inlet velocity of the abrasive flow is 45 m/s and the incident angle is 15°, the fifth-order variable-diameter pipe can obtain the best surface quality. Abrasive flow machining improves the surface quality of small holes better than that of large holes. To obtain uniform surface quality, it is necessary to use two-way machining to perform abrasive flow machining. The surface texture of the fifth-order variable-diameter pipe workpiece after precision machining by abrasive flow becomes clear and smooth, and the surface quality is significantly improved. The research results can provide theoretical guidance and technical support for the popularization and application of solid-liquid two-phase abrasive flow precision machining technology, with significant academic value and application value.