# Time-Series Prediction of Failures in an Industrial Assembly Line Using Artificial Learning
## 1 Introduction  
Unforeseen failures during production and assembly processes remain one of the most critical challenges in industrial plants. Such failures can lead to increased production costs, reduced product quality, diminished customer satisfaction, and weakened competitiveness. Key sources of production failures include machine malfunctions, human error, designdeficiencies, and limitations in control mechanisms [1]. Identifying which of these variables contributes to a specific failure is particularly complex in high-variation environments like the aerospace industry, where accurate and timely fault prediction is essential. Automation has long been employed as a solution to improve process control and implement fault-prevention strategies [1; 2]. While automation is effective in reducing machine-based failures, it often falls short in addressing human-related and complex systemic failures. In recent years, data-driven approaches have gained prominence due to their ability to model and predict system behaviours without relying on predefined physical laws. These methods derive models from statistical patterns in historical process data, offering a viable means of understanding complex, dynamic systems [3; 4]. Unlike traditional model-based methods, which depend on simplified assumptions and are often limited to systems with primitive geometries, data-driven techniques can capture nonlinear relationships and hidden patterns within big datasets. Tools such as regression, simulation, and artificial intelligence (AI) are frequently used for this purpose [5; 6]. Among these, machine-learning techniques--particularly those applied to time-series data--have emerged as highly effective for fault prediction. Notably, methods like multilayer perceptron (MLP), nonlinear autoregressive models (NARXs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and gated recurrent units (GRUs) have been widely studied [7; 8; 9; 10]. Convolutional neural networks (CNNs) were first developed for image recognition. However, they have a regression layer for time-series analysis. Hybrid CNN-RNN models have been shown to successfully capture both local and temporal patterns in sequential data [11; 12]. Recent studies have explored self-similar neural networks, particularly fuzzy neural network-long short-term memory (FNN-LSTM) hybrid architectures, to enhance time-series forecasting by integrating fuzzy logic for uncertainty handling and LSTM for temporal dependencies. [PERSON] et al. (2022) proposed an FNN-LSTM model for prediction of the rate of penetration, combining fuzzy rule-based pre-processing with LSTM layers to address nonlinearity and noise, achieving a better RMSE in exchange for an accuracy reduction of only 5% compared to standalone LSTM or FNN [13]. Similarly, [PERSON] et al. (2023) applied FNN-LSTM to an individual knowledge-testing trajectory. The use of the FNN direct propagation network as part of a hybrid algorithmic module makes it possible to construct a trajectory with an individual testing length, regardless of the number of thematic blocks [14]. A thermal control method combining fuzzy logic and LSTM neural networks stabilises the electrolysis stack temperature within \(\pm 1\,^{\circ}\mathrm{C}\) under a 10 kW power output [15]. Short-term load forecasting challenges in smart grids are dealt with by proposing a hybrid model integrating fuzzy C-means (FCM) clustering and an improved LSTM neural network, demonstrating enhanced accuracy for uncertain load conditions and advancing adaptive forecasting frameworks for grid stability [16]. LSTM-based time-series forecasting limitations (error accumulation, weak temporal correlations, low interpretability) are addressed by integrating a fuzzy inference system (FIS). A Wang-Mendel-based method constructs simplified and complete fuzzy rules, while fuzzy prediction fusion, a memory-strengthening layer, and parameter segmentation enhance reasoning, long-term dependency retention, and efficiency [17].