## 1 Introduction
These data are characterized by high dimensionality, nonlinearity, and noise interference, making it difficult for traditional methods to model them comprehensively. The complexity of equipment structures and operating conditions results in fault patterns with multiple mechanisms (e.g., mechanical wear, fatigue, overloading, and nonlinear vibrations) and interactions between fault modes, complicating their analysis and prediction. In practical industrial scenarios, fault data are typically scarce and unevenly distributed. Healthy state data dominate the datasets, while fault state data are often limited and insufficient. This imbalance poses challenges for training models that can effectively recognize and predict faults. Current research methods for time-series fault prediction can be broadly divided into two categories: statistical model-based methods and artificial intelligence (AI)-based methods. * Statistical Model-Based Methods. These methods use linear or nonlinear regression models to analyze the relationships between equipment operating parameters and potential faults, building predictive models based on historical data. One of the main advantages of these methods is their simplicity, ease of implementation, and, importantly, their transparency and interpretability. This makes them not only easier to implement but also more likely to be accepted as effective prediction tools. However, their performance can be limited when handling large datasets with complex relationships [5]. For example, traditional time-series statistical models can effectively capture the temporal dependencies of faults. However, they generally assume linearity in the data, which limits their ability to address nonlinear issues [6; 7]. * AI-Based Methods. Machine learning methods can extract features from large historical datasets, enabling the recognition of complex, nonlinear fault patterns and improving prediction accuracy. However, these approaches often require extensive manual feature selection and extraction, which can be time-consuming and labor-intensive [8]. Expert systems, on the other hand, can make quick predictions and decisions for specific fault patterns, but they rely heavily on domain experts' knowledge and experience, which limits their scalability and generalizability [9]. Long short-term memory (LSTM) networks, specifically designed to handle sequential data, have demonstrated strong capabilities in capturing long-term dependencies in time-series data. By leveraging mechanisms for memory retention and forgetting, LSTMs dynamically adjust the focus on prior information, resulting in more accurate fault predictions [10; 11; 12]. However, LSTM models are sensitive to noise, and their learning capacity may be adversely affected when processing data with significant noise levels. Given the inherent limitations of both statistical model-based and AI-based approaches, hybrid predictive models have emerged as a promising solution. These models combine the strengths of different methods, offering more accurate and stable prediction performance compared to standalone models [13]. To enhance the accuracy and stability of industrial equipment fault prediction, this paper proposes a hybrid predictive model based on the temporal convolutional network (TCN) and bidirectional long short-term memory network (BiLSTM). The main contributions of this study are summarized as follows:  
* A novel TCN-BiLSTM hybrid model is proposed, which integrates the ability of BiLSTM to capture bidirectional dependencies in time-series data with the efficient multi-scale temporal feature extraction capability of TCN. This combination improves the model's ability to process complex time-series data. * The proposed model incorporates techniques such as batch normalization, dropout, and an optimized Leaky ReLU activation function into the network structure. These enhancements effectively mitigate the vanishing and exploding gradient problems in deep networks, significantly improving the training efficiency and stability of the model.