This article introduces and evaluates adaptive polynomial Kolmogorov–Arnold network (AdaptPolyKAN) architectures for intelligent fault diagnosis, addressing limitations of classical KANs in dynamic and nonlinear industrial systems. While classical KANs offer interpretability, their fixed univariate mappings lack the flexibility needed for evolving operating conditions. The proposed AdaptPolyKAN uses adaptive polynomial expansions that adjust the degree of each basis according to local reconstruction errors, enabling real-time adaptation, improved accuracy, and efficient handling of complex nonstationary fault patterns. Three variants are examined-Standard PolyKAN, SplineKAN, and Online PolyKAN-alongside the proposed AdaptPolyKAN. Their performance is benchmarked against classical KAN, artificial neural networks, support vector machines, and random forest models in both real and simulated fault scenarios. The evaluation uses datasets from a cement rotary kiln at the Ain El Kebira plant, consisting of 768 normal samples, multiple simulated sensor faults, and one real fault. Monitored variables include temperatures, pressures, motor currents, and rotational speeds from 44 sensors recorded at 20-s intervals. The dataset contains noise, class imbalance, and limited duration, reflecting realistic industrial conditions. Comprehensive metrics—including accuracy, precision, recall, F1- score, false alarm rate (FAR), and missed detection rate (MDR)—demonstrate the superiority of the proposed approach. AdaptPolyKAN achieves 98.4% accuracy, balanced precision and recall (98.3% and 98.4%), and the lowest FAR (0.0060), while maintaining competitive MDR (0.178). Online PolyKAN adapts effectively to changing fault patterns, whereas classical KAN suffers from elevated false alarms. Overall, AdaptPolyKAN provides reliable detection in nonlinear and time-varying processes, offering a practical and interpretable solution for safety-critical industrial environments.

The accurate detection and diagnosis of faults are critical for maintaining optimal operation and ensuring the reliability of industrial processes. Notably, the topic of online fault detection and diagnosis has recently presented a significant challenge. This work mainly deploys a neural network technique for the comprehensive detection and diagnosis of faults within the Tennessee Eastman Process (TEP) on a low-computational power system, the Raspberry Pi board. The devolved methodology showcases a remarkable level of accuracy (94.50%) in diagnosing the various TEP faults, affirming its robustness and effectiveness. To elevate the practical applicability of the proposed approach, a meticulous investigation into the implementation of the suggested approach on a Raspberry Pi 4 card was undertaken. The successful realization of this implementation not only highlights the adaptability of the approach but also paves the way for its seamless integration into practical industrial applications.

The key idea behind this study is to integrate a moving window dynamic PCA (MW-DPCA) methodology for fault detection within the Tennessee Eastman process (TEP) into a low-computational power system, the Raspberry Pi 4 card, for real-time application. Indeed, the paramount importance of real-time fault detection (FD) in intricate industrial processes presents a critical challenge. Various data-driven techniques have been developed to ensure safety, maintain operational stability, and optimize productivity in such processes. Principal Component Analysis (PCA) is a fundamental data-driven technique that utilizes dimensionality reduction to extract the most informative features from high-dimensional data, simplifying analysis and potentially revealing underlying fault patterns. However, PCA primarily focuses on static relationships and may miss crucial temporal dynamics for fault identification. This is where dynamic PCA (DPCA) excels. By incorporating lagged values of variables, DPCA captures the temporal evolution of features, enabling a more comprehensive understanding of process behavior and improving the detection of faults involving dynamic changes. In order to address the stochastic measurements, a moving average filter tool is also employed. The results obtained and the successful realization of this implementation demonstrate the adaptability of the approach and pave the way for its seamless integration into practical industrial applications.

Grid Connected Photovoltaic (GCPV) sys-tems has been a rising research area in the industry fields. Therefore the high reliability, performance and safety operation of GCPV systems has become a high priority. Thus, it is important of developing an intel-ligent fault detection and diagnosis method that aims at increasing the efficiency of these systems. Therefore, the present study proposes an enhanced intelligent fault diagnosis approach. In the developed procedure, the measured normal and faulty data are applied together to extract the relevant feature, reduce the impact of noise, and then special features are fed to a neural network classifiers. To do that, a Multiscale Principal Component Analysis (MSPCA)-based Artificial Neural Network (ANN) method is proposed to provide the relia-bility and safety of the GCPV systems. From the GCPV measurements, features are appropriately extracted and scaled through multiscale principal component analysis. The ANN classifier is used in classifying twenty-one faults that can occur in GCPV systems operating under different working conditions. The diagnosis results show that the developed MSPCA-based ANN method not only able to detect faults, but also can effectively distinguish between different kinds of faults.