Identifying the key components of CNC lathe and analyzing the fault propagation behavior is a powerful guarantee for the fault diagnosis and health maintenance of CNC lathe. The traditional key component identification studies are mostly based on the feature parameter evaluation of the fault propagation model, disregarding the dynamics and influence of fault propagation. Therefore, this paper proposes a key component identification method based on the dynamic influence of fault propagation. Based on the CNC lathe architecture and fault data, the cascaded faults are analyzed. The improved Floyd algorithm is used to iterate and transform the direct correlation matrix expressing the cascaded fault information, and the fault propagation structure model of each component is constructed. The coupling degree function is introduced to calculate the dynamic impact degree between components, and the dynamic fault propagation rate of each component is calculated with the dynamic fault rate model. Based on this, the dynamic influence value of fault propagation is obtained by using the improved ASP algorithm. The key components of the system are identified by synthesizing the fault propagation structure model and the dynamic influence value of fault propagation. Taking a certain type of CNC lathe as an example, the proposed method is verified to be scientific and effective by comparing with the traditional identification method of key components based on fault propagation intensity.

This article presents a performance evaluation of a flexible manufacturing system (FMS), in which the system and its components can have different performance levels ranging from perfect functioning to total failure. An original multistate transition process for the FMS’s main component, Computerized Numerical Control (CNC) machine tools, proposed in the current article and treated as an aging process because of its increasing failure rate is investigated. The Markov reward approach is utilized to analyze the availability of CNC machine tools and sensitivity analysis of each non-operational state to the machine’s downtime. To overcome the “state explosion problem” in the subsequent FMS performance evaluation process, an application of the Lz-transform method is proposed to determine the performance evaluation and availability of such aging multistate FMS. An illustrated case is presented to demonstrate the performance evaluation process. The results show that the suggested methods can be implemented in engineering decision making and construction of various aging multistate FMS related to demands, availability, and output performance.

This paper exploits the Markov reward method to availability and importance evaluation for CNC machine tools. The presented paper regards CNC machine tools as a multi-state system that state transitions process caused by elements and subsystem’s failures and corresponding maintenance activities during the lifetime. Simultaneously, considering the time-varying characteristics of the failure rates of subsystems, the non-homogeneous Markov reward model is introduced for evaluation of availability and subsystem importance identification for aging multi-state CNC machine tools under minimal repair. Corresponding procedures for the failure rates, state transition matrix, and reward matrix definition are suggested for the availability and importance measures. A numerical example is presented in order to illustrate the approach.

The fault transitivity of machining center components causes their fault propagation indexes to demonstrate dynamic time variability, which affects their importance. The method proposed in this study overcomes the biases of existing methods of evaluating the importance of system components, as they are mostly based on single indexes; the fault propagation probability and fault propagation risk are selected to perform a comprehensive evaluation. This study first establishes a network hierarchical structure model for machining center components, and the degree of influence of fault propagation among the components is calculated. On this basis, the improved adjacent spreading paths (ASP) algorithm is used to calculate the fault propagation index of each component; from the two perspectives of fault propagation probability and fault propagation risk, an evaluation mechanism involving the combined variable weight is used to comprehensively evaluate components’ importance. Taking a certain type of machining center as an example, through a comparison with ranking results from other node importance methods, it is verified that the proposed method can more effectively distinguish the differences in the importance of each component, thus illustrating the effectiveness and practical value of this method.

Sliding-window few-shot evaluations can place support and query windows over shared raw samples. We audited episode-internal overlap, source-noise policy, normalization, and fixed-feature construction using Case Western Reserve University (CWRU) data and a HUSTbearing cross-speed task reconstructed from raw files. In the primary CWRU 0 hp to 3 hp (0 to approximately 2.24 kW), 10-way, 5-shot evaluation at a signal-to-noise ratio (SNR) of −5 dB across 20 seeds, common-pool construction increased log-compressed fast Fourier transform prototype (Log-FFT) accuracy by 3.83 percentage points (95% confidence interval (CI): [+3.63, +4.02]). The corresponding increase for the clean source-supervised cross-entropy prototype encoder (Source-CE-Proto) was 0.95 percentage points and was not statistically distinguishable from zero (95% CI: [−0.004, +1.90]; exact p = 0.05084). Under raw-index-separated evaluation, leave-one-SNR-out Source-CE-Proto achieved 93.23% without direct −5 dB source exposure, compared with 82.43% for Log-FFT. A No-log FFT control achieved 99.09% in the primary task and also exceeded the learned encoders in a second CWRU load pair. HUSTbearing likewise showed channel-dependent fixed-feature accuracy and overlap effects. These results do not support a general method ranking; they support reporting raw-index provenance, source-noise distributions, normalization, and exact feature construction before interpreting method rankings in few-shot bearing fault diagnosis benchmarks.