# Time-Series Prediction of Failures in an Industrial Assembly Line Using Artificial Learning
## 1 Introduction
Fuzzy embedded long short-term memory architecture leverages the strengths of both systems to achieve high prediction accuracies with interpretable results [18]. In active power filters, to suppress harmonics, LSTM-FNN is used, demonstrating enhanced accuracy and robustness for power quality management [19]. To classify neuromuscular motor states (e.g., Forward, Reverse, Hand-Grip) from EMG signals, LSTM-FNN resolves the limitations of existing methods that ignore temporal signal correlations. The approach achieves 91.3% accuracy for four-way actions and 96.7% for two-way classifications. The FIS-LSTM framework enhances interpretability and reliability for applications in rehabilitation and medical devices, demonstrating robust performance in capturing dynamic neuromuscular patterns [20]. These hybrid frameworks highlight the potential of FNN-LSTM in complex, noisy environments, though challenges remain in optimising fuzzy rule scalability and computational efficiency. Future research should focus on dynamic fuzzy parameter adaptation and lightweight architectures for real-time applications. Despite the advancements in predictive modelling, existing studies on fault detection in assembly lines primarily focus on network station optimisation and process efficiency. However, the impact of stochastic variables, modelling uncertainties, and practical constraints limit the applicability of these models [21]. Moreover, heuristic and metaheuristic algorithms are increasingly needed, particularly for large-scale, complex systems [22]. There remains a significant research gap in developing holistic, real-time, and generalisable fault prediction models that account for diverse error types and their influence on production efficiency. While AI and machine learning have been successfully implemented across various domains, their application in aerospace assembly line fault prediction is still underexplored. As a result of the above explanations, it is clear that modelling and prediction are still ongoing studies to be able to select an optimum model. However, so far there have not been any unique techniques leading to accurate simulation for any type of data or process. Moreover, whichever model is selected, determining its parameters will be the next problem. There are some solution alternatives for modelling or prediction, such as interpolation, regression, optimisation, and artificial learning (AL). Thus, initial estimates of the parameters can be determined easily via regression of the experimental data. This has the potential to eliminate very expensive and time-consuming trial and error. After a literature review, the idea of comparing the performance of the AL models was raised. This led the authors to an expectation and a research question, \"Can any failure in an industrial assembly line, which has a part of manufacturing lines, be determined using AL models?\" In the light of the above explanations, expectation, and the techniques' potential, it is hypothesised that AL techniques can be used to predict failures. Thus, it was decided to investigate this subject. In the literature on modelling and prediction, there are too many studies. However, few studies have handled failure prediction using AL methods. This study aims to address that gap by evaluating the performance of six AL models--NAR, LSTM, GRU, adaptive neuro-fuzzy inference system (ANFIS), MLP, and CNN-RNN--for failure prediction in an assembly line. A time series, so-called \"configuration\", is handled. Each model inherently has distinct features. NAR is a variant of MLP specialised for time series. LSTM and GRU are deep recurrent networks, while ANFIS is a hybrid neuro-fuzzy system. Also, CNN-RNN is another hybrid model integrating both convolutional and recurrent neural networks. The trained networks are assessed through testing-validation iterations, with an additional closed-loop forecast. Subsequent sections present the significance and novelty of the study. Section 3 explains the materials and methods used. It also gives the background of the models.