# ML-Based Maintenance and Control Process Analysis, Simulation, and Automation--A Review
## 4 Discussion
It introduces RCMPhe (feature extraction technique) to effectively capture fault features from aircraft engine bearing signals. The BO-SVM (Bayesian Optimisation-Support Vector Machine) is used to classify and identify different fault states in bearings. The combined approach offers a highly accurate fault diagnosis system despite the limitation of limited training data. The effectiveness of the proposed method has been demonstrated through tests on real aero-engine bearing data sets, demonstrating its application in real maintenance scenarios. The method optimises the predictive maintenance of aircraft engines, offering highly accurate fault detection with minimal data [38]. The concept of flexible tensor singular value decomposition and its application to multi-sensor signal fusion processing introduces a flexible tensor singular value decomposition (t-SVD) technique specifically adapted to multi-sensor data fusion. This method defines a flexible o-order mode product(p,q) for tensor manipulation, enabling more adaptive data transformations. Using this new tensor decomposition structure, a new multi-sensor signal processing algorithm was developed that adapts to different data patterns more efficiently than traditional methods. In tests, this new algorithm significantly outperforms existing signal processing techniques, especially in handling complex, multi-modal sensor data. Its flexibility makes it particularly suitable for predictive maintenance tasks where data from multiple sensors must be combined and analysed [39]. Multiagent Soft Actor-Critic Aided Active Disturbance Rejection Control (MASAC-ADRC) for DC Solid-State Transformers (dcSST) focuses on uncertainty estimation and compensation in modular systems. It optimises controller gains adaptively using the MASAC method, increasing adaptability to changing environmental conditions without relying on pre-existing data sets. A neural network is used to map optimal ADRC parameters from measured states, making it suitable for real-time applications with dynamic environmental changes. The MASAC-ADRC method achieves excellent dynamic performance in terms of overshoot, settling time and mean squared error, with an improvement of more than 50% over traditional methods. However, the complexity of neural network training and computational load may be a limitation for real-time applications in some systems [40]. The MA-TD3 (Autonomous Input Voltage Sharing Control and Triple Phase Shift Modulation) algorithm for ISOP-DAB converters in DC microgrids formulates the control problem as a Markov game with multiple deep learning agents with reinforcement (DRL). The MA-TD3 algorithm trains these agents offline, allowing them to make real-time control decisions during online operations. The method is highly adaptive, finding optimal combinations of modulation variables in environments with uncertainty and stochastic behaviour, even without accurate model information. It has the advantage of being able to balance the input voltage division (IVS) and minimise the current load in different sub-modules. However, the reliance on complex DRL training processes and the need for extensive offline training data may limit its immediate applicability in rapidly changing environments [41]. Both methods excel in adaptive control under uncertainty, with MASAC-ADRC offering a better dynamic response and MA-TD3 excelling in decentralised control. A key advantage of MASAC-ADRC is real-time optimisation without predefined data sets, while MA-TD3 excels in managing stochastic environments. However, both approaches face challenges in terms of computational complexity and the need for robust offline training or neural network calibration.