# Lab-to-Field Generalization Gap: Assessment of Transfer Learning for Bearing Fault Detection
## 1 Introduction  
The integration of Artificial Intelligence (AI) has accelerated in various fields, including industrial applications [1]. Modern production environments require automated fault detection systems to maintain operational efficiency and ensure equipment reliability [2]. However, the adoption of AI in condition-based and predictive maintenance remains limited [3]. A critical limitation of AI-based maintenance models is their dependence on large volumes of high-quality fault data, which is often scarce in real-world industrial settings. Unlike domains where data collection is scalable, acquiring comprehensive failure data in the industry is expensive, time-consuming, and often impractical because of safety concerns: deliberately running machinery to failure is not a viable option in most cases. One common approach to overcome the data scarcity problem is to train Machine Learning (ML) models using lab-recorded testbed data, where operating conditions are controlled, and data acquisition follows a structured approach. Although this facilitatesthe systematic analysis of failure modes, it does not fully capture the complexity of a real industrial environment, leading to poor generalization when deployed in production [4]. This challenge arises due to the domain shift--the significant differences between a controlled testbed environment and a workshop condition caused by factors such as ambient noise [5]. The fundamental assumption of traditional ML models that training and deployment data have similar distributions is frequently violated in real workshop environments [6]. Transfer Learning (TL) has emerged as a promising solution for addressing generalization challenges by allowing knowledge transfer from laboratory-recorded data to real workshop conditions [7]. A summary of the main concepts of TL for Industry 4.0, its taxonomy, and its applications in maintenance is provided in [8]. Domain adaptation, a subset of TL techniques, explicitly aims to reduce the domain shift, enabling models to adapt to new environments by leveraging existing knowledge [6]. Maximum Mean Discrepancy (MMD) is a widely used metric in domain adaptation for measuring the distribution divergence and aligning feature distributions between domains [9]. Various MMD-based algorithms have been proposed, with Transfer Component Analysis (TCA) demonstrating strong performance and effectively handling cases where little or no labeled data are available in the target domain [10]. TCA has been extensively used in predictive maintenance, including bearing fault diagnosis [11], gearbox condition monitoring [12], lithium-ion battery health estimation [13], and heat pump fault detection [14]. Among these applications, bearing failure detection has received significant research attention due to the critical role of bearings in rotating machinery [15]. Bearings are subjected to diverse failure modes and operate under varying conditions, including different loads and speeds. TCA addresses these challenges by transferring knowledge across different operating conditions, thereby enhancing the robustness of fault classification. Several modifications have been proposed to further optimize TCA performance. Improved TCA incorporates local discriminant weights [16]. Weighted TCA addresses both marginal and conditional distribution mismatches [17]. TCA with Preserving Local Manifold Structure retains the feature locality and label information [18]. Feature Transfer TCA introduces online feature transfer frameworks, enabling fault diagnosis models to be dynamically updated using real-time sensor data [11]. While several modified versions of TCA have demonstrated enhanced performance on specific datasets, this study employs the standard TCA as a widely recognized and well-established baseline to ensure general applicability and comparability of results across broader contexts. The existing research mainly focuses on transferring knowledge across varying operating conditions of the same machine or among similar machines in controlled lab environments.