## 1 Introduction
Among model-based approaches, parameter estimation techniques offer significant potential for detecting multiplicative faults, as they enable direct monitoring of physical changes within the system. Compared to traditional parameter estimation-based fault detection methods, the advantages of the proposed Sliding Window Algorithm (SWA) [43] and Sliding Integral Algorithm (SIA) [44] become more apparent. Classical parameter estimation techniques such as LS [18; 32; 38] and RLS [30; 31] are widely used due to their simplicity and low computational requirements. LS is primarily an offline method, as it processes a fixed batch of data and assumes time-invariant parameters during estimation. In contrast, RLS is designed for real-time application, updating estimates recursively as new data become available and using a forgetting factor to adapt to changing system dynamics. However, despite its real-time capability, RLS often struggles with transient disturbances and abrupt parameter changes caused by faults, especially when the forgetting factor is not optimally tuned. These limitations can lead to delayed or inaccurate fault detection in systems subject to abrupt changes [26; 30]. Advanced methods such as EKF [29; 34; 35] and UKF [36; 37] offer improved performance in noisy and nonlinear environments. However, their reliance on accurate system models and careful tuning of noise covariances increases implementation complexity and computational burden, limiting their suitability for embedded or real-time applications. Similarly, algebraic techniques such as the AGT offer analytical insight and enable fault localization. However, they are generally unsuitable for real-time implementation due to their dependence on signal derivatives, symbolic computations, and high sensitivity to measurement noise [33]. Optimization-based techniques such as GA, PSO, and WOA offer global optimization capabilities for parameter estimation, but their high computational complexity makes them impractical for real-time applications [41; 42]. In contrast, SWA and SIA offer robust and low-complexity alternatives that preserve interpretability and can be scaled to more complex systems with relative ease. SWA, applied to discrete-time models with short data windows, enhances responsiveness to abrupt faults, while SIA, using continuous-time integral formulations, reduces noise and transient sensitivity--making it suitable for gradual parameter changes. Both methods are non-intrusive, require minimal tuning, and are computationally efficient, making them ideal for embedded real-time applications. Furthermore, by linking estimated model parameters (e.g., \(a_{1}\), \(b_{0}\), \(b_{1}\)) to physical quantities (e.g., resistance, inertia), they enable not only fault detection and isolation but also identification of fault severity. This paper presents a real-time, parameter-estimation-based framework for detection, isolation, and identification (FDII) of multiplicative faults in a DC motor and its amplifier. The system is implemented on a hardware-in-the-loop experimental platform using LabVIEW 2009 and NI CompactRIO CRIO-9075 (National Instruments, Austin, TX, USA), enabling remote access, real-time fault injection, and online testing. The key contributions include:  
1. Development of an interactive laboratory platform for real-time simulation and testing of FDII algorithms, specifically designed to support both practical implementation and engineering education. This is achieved through a hardware-in-the-loop, remotely accessible setup built using LabVIEW and NI CompactRIO, enabling real-time fault injection, detection, isolation, and identification in both the DC motor and its electronic amplifier--representing a significant advancement in applied FDI research and training environments.