Control chart pattern recognition (CCPR) can quickly recognize anomalies in charts, making it an important tool for narrowing the search scope of abnormal causes. Most studies assume that the observations are normal, independent and identically distributed (NIID), while the assumption of independence cannot always be satisfied under continuous manufacturing processes. Recent research has considered the existence of autocorrelation, but the recognition rate is overestimated. In this paper, a novel scheme is proposed to recognize control chart patterns (CCPs) in which the inherent noise is autocorrelated. By assuming that the inherent noise follows a first-order autoregressive (AR (1)) process, the one-dimensional convolutional neural network (1DCNN) is applied for extracting features in the proposed scheme, while the grey-wolf-optimizer-based support vector machine (GWOSVM) is employed as a classifier. The simulation results reveal that the proposed scheme can effectively identify seven types of CCPs. The overall accuracy is 89.02% for all the autoregressive coefficients, and the highest accuracy is 99.43% when the autoregressive coefficient is on the interval (−0.3, 0]. Comparative experiments indicate that the proposed scheme has great potential to identify CCPs in autocorrelated processes.

Condition monitoring of rotating machinery is critical for ensuring industrial safety and operational reliability. As a core component of intelligent diagnostic systems, domain adaptation methods have achieved notable progress in mechanical fault diagnosis. However, most existing approaches presume a fully shared label space between source and target domains, limiting their effectiveness under partial domain adaptation scenarios commonly encountered in industrial practice. In addition, they often struggle with classification uncertainty near decision boundaries. To address these challenges, this paper proposes a Selective Adversarial Augmentation Network (SAAN) for cross-domain rolling bearing fault diagnosis with partial label space alignment. The proposed framework designs a multi-level feature extraction module to enhance transferable feature representation and a Balanced Augmentation Selective Adversarial Module (BASAM) to dynamically balance class distributions and selectively filter irrelevant source classes, thereby mitigating negative transfer and achieving fine-grained class alignment. Furthermore, an uncertainty suppression mechanism is put forth to reinforce classifier boundaries by minimizing the impact of ambiguous samples. Comprehensive experiments conducted on public and proprietary bearing datasets demonstrate that SAAN consistently surpasses state-of-the-art benchmarks in diagnostic accuracy and robustness, providing an effective solution for practical applications under class-imbalanced and variable operating conditions.

With the development of urbanization, building maintenance units (BMUs) have been widely used in super high-rise buildings. As aerial work machinery, condition monitoring plays a vital role in the safety maintenance and management of BMUs. However, BMUs have multi-source heterogeneous data relationships that are difficult for systems to understand. Moreover, at this stage, there is a lack of sufficient samples to support fault diagnosis data. Therefore, this paper proposes a real-time monitoring and fault diagnosis system for BMU operating conditions. This system, based on the Internet of Things (IoT) architecture, acquires and stores data from distributed BMU systems, improving the data collection and sharing rate throughout the entire process. A collaborative fault reasoning chain diagnosis model was established based on heterogeneous knowledge sources and real-time process signals, which increased the accuracy of fault identification to 97%. Finally, through simulation testing and evaluations, the system can stably transmit data within 6–7 days and accurately analyze the operational and fault status of BMU, with an error rate within 5%. It effectively improves the efficiency and accuracy of BMU condition monitoring and fault diagnosis and also provides a new method for the practical application of intelligent BMU operation and maintenance.

Control chart pattern recognition was initially focused on single patterns with the assumption of normal, independent, and identical distribution. In practice, though, these assumptions are rarely valid in manufacturing processes, due to numerous influencing factors and short intervals in data collecting. It is necessary to consider that the inherent disturbance is autocorrelated and that two single patterns appear at the same time. This study presents a novel framework integrating Empirical Mode Decomposition (EMD) and one-dimensional Convolutional Neural Networks (1DCNN) with feature component selection for recognizing concurrent control chart patterns in autocorrelated manufacturing processes. We assume the inherent disturbance follows a first-order autoregressive (AR (1)) process and simulate eleven concurrent patterns. Then, the EMD method decomposes the concurrent pattern into a series of feature components, wherein the correlation coefficient is employed as the index by which to select the two feature components. Finally, the selected feature components and raw data are combined to create a feature vector that acts as the input for the 1DCNN model. The simulation results demonstrate that the proposed model achieves a recognition accuracy of 92.39%, outperforming both the singular spectrum analysis–support vector machine (SSA-SVM) and the singular spectrum analysis–random forest (SSA-RF) methods in terms of accuracy and robustness.