Researchers are exploring various methods for fault detection and tool condition monitoring in machine tools, including the use of multi-sensor fusion, ensemble learning, and deep learning techniques. Different approaches, such as transfer learning, transformer-based models, and neural networks, are being investigated for their effectiveness in predicting tool wear and detecting faults. The development of these methods is supported by the creation of open datasets and the proposal of novel calibration strategies for measurement fusion operations.