SCADA-Driven big data framework for fault prediction in spiral steel pipe manufacturing using fuzzy and neural network models

The increasing complexity of spiral steel pipe production necessitates the implementation of intelligent forecasting methods to predict potential failures. This, in turn, enables the development of reliable evaluation techniques aimed at minimizing unanticipated breakdowns and enhancing the efficacy of maintenance strategies. In the present study, a novel SCADA-integrated framework is proposed, which incorporates Fuzzy Comprehensive Evaluation (FCE) and Artificial Neural Networks (ANN) into mid-to-long-term reliability analysis and machine learning-based short-term fault prediction. The architecture performs dynamic analysis on the health of the equipment, welding, alignment, hydraulics, and motor systems using a synthetic SCADA dataset that includes more than 100,000 time-series data points. The generation of imprecise reliability grades is predicated on essential indicators, including mean time between failures (MTBF), mean time to repair (MTTR), the level of failure, and the difficulty of its detection. These indicators are subsequently modeled through artificial neural networks (ANNs) to enable real-time inference. The multi-week sensor window and alarm logs are used with tree-based classifiers and statistical models to predict faults up to four weeks in advance. The mean prediction accuracy is over 91%, and a cost-benefit analysis indicates that active maintenance planning can result in significant financial savings. The combined use of fuzzy logic and neural networks is particularly valuable in manufacturing environments because it integrates human-like reasoning with data-driven learning, enabling robust decision-making under uncertainty. The all-inclusive solution is a financially reasonable and scalable alternative for implementing predictive diagnostics in industrial steel pipe production settings.