Tool condition monitoring (TCM) is of great importance for improving the manufacturing efficiency and surface quality of workpieces. Data-driven machine learning methods are widely used in TCM and have achieved many good results. However, in actual industrial scenes, labeled data are not available in time in the target domain that significantly affect the performance of data-driven methods. To overcome this problem, a new TCM method combining the Markov transition field (MTF) and the deep domain adaptation network (DDAN) is proposed. A few vibration signals collected in the TCM experiments were represented in 2D images through MTF to enrich the features of the raw signals. The transferred ResNet50 was used to extract deep features of these 2D images. DDAN was employed to extract deep domain-invariant features between the source and target domains, in which the maximum mean discrepancy (MMD) is applied to measure the distance between two different distributions. TCM experiments show that the proposed method significantly outperforms the other three benchmark methods and is more robust under varying working conditions.

Multiple signal classification (MUSIC) algorithm-based structural health monitoring technology is a promising method because of its directional scanning ability and easy arrangement of the sensor array. However, in previous MUSIC-based impact location methods, the narrowband signals at a particular central frequency had to be extracted from the wideband Lamb waves induced by each impact using a wavelet transform. Additionally, the specific center frequency had to be obtained after carefully analyzing the impact signal, which is time consuming. Aiming at solving this problem, this paper presents an improved approach that combines the optimized ensemble empirical mode decomposition (EEMD) and two-dimensional multiple signal classification (2D-MUSIC) algorithm for real-time impact localization on composite structures. Firstly, the impact signal at an unknown position is obtained using a unified linear sensor array. Secondly, the fast Hilbert Huang transform (HHT) with an optimized EEMD algorithm is introduced to extract intrinsic mode functions (IMFs) from impact signals. Then, all IMFs in the whole frequency domain are directly used as the input vector of the 2D-MUSIC model separately to locate the impact source. Experimental data collected from a cross-ply glass fiber reinforced composite plate are used to validate the proposed approach. The results show that the use of optimized EEMD and 2D-MUSIC is suitable for real-time impact localization of composite structures.

Damage to a compressor impeller can sometimes cause serious accidents, heavy casualties and property loss, etc. Therefore, it is necessary to conduct damage monitoring and identification for the compressor impeller. A damage identification method based on probabilistic neural networks (PNNs) with modal information fusion is proposed for a compressor impeller. The modal shape of the compressor impeller can be acquired by experimental modal analysis. Combining waveform capacity dimension, a singular value decomposition is applied to extract damage feature information from the system modal shape. The two damage indicators are fused by a multi-dimensional feature vector. Finally, a PNN model is constructed and used to identify structural damage. The experimental results indicate that the proposed method is effective in detecting damage to the compressor impeller.

Condition monitoring of rotating components is crucial for ensuring the reliability and safety of mechanical systems, and artificial intelligence (AI) plays a significant role in achieving the goal. However, the high costs and complexity associated with components like high-speed motorized spindles present significant challenges in collecting complete fault samples. Therefore, we propose a new approach to tackle the challenges. The process commences with establishing a lumped parameter dynamic model for the high-speed motorized spindle. Then, the parameters such as stiffness, eccentricity, and damping in the lumped parameter model were optimized using genetic algorithm. Subsequently, simulated fault samples are acquired by introducing excitation to normal simulated signals. Finally, transfer learning techniques are utilized for intelligent fault diagnosis. The training set consists of simulated fault samples, while the testing set comprises experimental fault samples. Our approach aims to enhance the efficiency and accuracy of fault diagnosis for high-speed motorized spindles while also addressing the challenge of high cost.

The high-speed motorized spindles play a pivotal role in modern machining, holding vast application potential and profound research significance. To analyze the dynamic characteristics of the high-speed motorized spindle system, an fourteen-degree-of-freedom dynamic model has been established using the lumped parameter method, and numerical methods have been employed for computation. Then, the bearing stiffness, the variation of the restoring force, and the unbalanced magnetic force are considered. Additionally, the parameters of the model were optimized using the genetic algorithm. Finally, the effects of spindle speed, initial eccentricity, and bearing parameters on the vibration behavior and stability of the motorized spindle were analyzed using bifurcation diagrams, time-frequency waveform, spectra, trajectories, and Poincaré. The study provides a valuable reference for the design and optimization of motorized spindle systems.