Time-Series Prediction of Failures in an Industrial Assembly Line Using Artificial Learning

This study evaluates the efficacy of six artificial learning (AL) models—nonlinear autoregressive (NAR), long short-term memory (LSTM), adaptive neuro-fuzzy inference system (ANFIS), gated recurrent unit (GRU), multilayer perceptron (MLP), and CNN-RNN hybrid networks—for time-series data for failure prediction in aerospace assembly lines. The data consist of 45,654 records of configurations of failure. The models are trained to predict failures and assessed via error metrics (RMSE, MAE, MAPE), residual analysis, variance analysis, and computational efficiency. The results indicate that NAR and MLP models, respectively, achieve the lowest residuals (clustered near zero) and minimal variance, demonstrating robust calibration and stability. MLP exhibits strong accuracy (MAE = 2.122, MAPE = 0.876%, RMSE = 1.418, and ME = 1.145) but higher residual variability, while LSTM and CNN-RNN show sensitivity to data noise and computational inefficiency. ANFIS balances interpretability and performance but requires extensive training iterations. The study underscores NAR as optimal for precision-critical aerospace applications, where error minimisation and generalisability are paramount. However, the reliance on a single failure-related variable “configuration” and exclusion of exogenous factors may constrain holistic failure prediction. These findings advance predictive maintenance strategies in high-stakes manufacturing environments with future work integrating multivariable datasets and domain-specific constraints.