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Fault detection for distributed parameter systems (DPSs) generally requires the complete model information to be known so far. However, for numerous industrial applications, it is common that accurate first-principles physical models are extremely difficult to obtain. Hence, the applicability of traditional model-based methods is being restricted. To pave the way, an adaptive neural network (AdNN) is constructed to simultaneously estimate the state variable and the unknown nonlinearity for a class of partially known nonlinear DPSs. Moreover, considering that full-state measurement is unrealistic in applications, the proposed adaptive neural observer is based on a reduced-order model, which also increases the computation efficiency. Then, the residual generation and evaluation are conducted using the output estimation error of the proposed adaptive neural observer. Bearing the effects of the neglected fast dynamics in mind, a data-driven threshold generation scheme is proposed. Extensive experimental results are presented and analyzed to validate the effectiveness of the proposed method.

Distributed parameter systems (DPSs) are commonly used to characterize various industrial processes, but the coupling of spatiotemporal data and time-delay effects poses challenges for their fault detection. This article proposes a fault detection method for a class of nonlinear parabolic DPSs with limited sensors. A time/space separation method is first applied to decouple the spatiotemporal data to obtain time coefficients that are available for data-driven modeling. Then, the obtained dominant time coefficients are modeled by a dynamic partial least-squares (D-PLSs) method. Finally, the residual space is utilized to establish two monitoring statistics and a reference boundary is established with the aid of the mirrored data kernel density estimation (KDE). This method exploits the separable characteristics of parabolic DPSs and is a data-driven method that is independent of an explicit mathematical model of the system processes. The proposed method is validated on a curing oven experimental platform, and comparative results with other methods show that it achieves satisfactory performance in fault detection accuracy and first-time detection timeliness.

Surface defect detection is very important for the printed circuit board (PCB) to ensure their quality requirements. This article proposes a reliable and lightweight adaptive convolution (LAC) network for PCB surface defect detection. First, an automated optical inspection (AOI) for collecting PCB defects is introduced, and the formation mechanism of PCB defects is systematically analyzed. After that, LAC strategically aggregates multiple convolution kernels and simplifies model complexity through tensor decomposition. Furthermore, the confidence gate learning (CGL) strategy aims to cope with dataset noise by combining collaborative learning (CL) and confidence evaluation. Complexity and convergence analyses support the theoretical basis of the method. Finally, three industrial defect datasets are used to evaluate the effectiveness. The results show that the methodology has powerful feature representation, visual interpretability, and detection robustness.

This article proposes an adaptive control for a flexible manipulator (FM) under the influence of distributed disturbances, unknown dead zones, and input quantization. First, the hybrid effect of the unknown dead zone and input quantization is formulated and represented based on some essential transformations. Then, an adaptive robust quantized control with online updating laws is developed to address the uncertainty of the dead zone, ensure robustness and angle position, and dampen the vibration in the FM system. Subsequently, the Lyapunov theoretical analysis is employed to ensure the bounded stability of the system. Finally, numerical simulations and experiments with a Quanser platform are given to further verify the feasibility and superiority of the designed scheme.

In industrial applications, the modeling of distributed parameter systems (DPSs) is of significance for process control and monitoring. Due to infinite dimension, spatiotemporal coupled dynamics, nonlinearity, and model uncertainties, however, modeling and online applications of DPSs are very difficult. To address these issues, an online spatiotemporal modeling method is proposed based on confidence-aware multiscale learning. From the spacial-scale perspective, an evolutionary learning-based spatial basis function is designed by learning from two dimensionality reduction methods, including Karhunen–Lo$\grave{e}$ ve and diffusion maps. From the temporal-scale perspective, an efficient broad learning system is developed as reduced-order model to online address temporal dynamics of DPSs. As for the spatiotemporal-scale learning, Gaussian process regression is proposed as confidence-aware estimator to compensate for model generalization errors caused by spatiotemporal coupled dynamics. Through integration with the three-scale learning, the proposed method enables online confidence-aware prediction for DPSs. Experiments based on the curing process in snap curing oven demonstrate the effectiveness of proposed method.