This paper presents an online model-free sensor fault-tolerant control scheme capable of tolerating the most common faults affecting an induction motor. This approach involves using neural networks for fault detection to provide the controller with sufficient information to counteract adverse consequences due to sensor faults, such as degradation in performance, reliability, and even failures in the control system. The proposed approach does not consider the knowledge of the nominal model of the system or when the fault may occur. Therefore, a high-order recurrent neural network trained online by the Extended Kalman Filter is used to obtain a mathematical model of the system. The obtained model is used to synthesize a discrete-time sliding mode control. Then, the fault-detection and -isolation stage is performed by independent neural networks, which have as input the signal from the current sensor and the position sensor, respectively. In this way, the neural classifiers continuously monitor the sensors, showing the ability to know the sensor status. The combination of controller and fault detection maintains the operation of the motor during the time of the fault occurrence, whether due to sensor disconnection, degradation, or connection failure. In fact, the MLP neural network achieves an accuracy between 95% and 99% and shows an AUC of 97% to 99%, and this neural network correctly classifies true positives with acceptable performance. The Recall value is high, between 97% and 99%, and the F1 score confirms a good performance. In contrast, the CNN shows a higher accuracy, between 96% and 99% in accuracy and 98% to 99% in AUC. In addition, its Recall and F1 reflect a better balance and capacity to handle complex data, demonstrating its superiority to MLP in fault classification. Therefore, neural networks are a promising approach in areas such as fault-tolerant control.

One of the main challenges when working with time series captured online using sensors is the appearance of noise or null values, generally caused by sensor failures or temporary disconnections. These errors compromise data reliability and can lead to incorrect decisions. Particularly in the treatment of diabetes mellitus, where medical decisions depend on continuous glucose monitoring (CGM) systems provided by modern sensors, the presence of corrupted data can pose a significant risk to patient health. This work presents an approach that encompasses online detection and imputation of anomalous data using physiological inputs (insulin and carbohydrate intake), which enables decision-making in automatic glucose monitoring systems or for glucose control purposes. Four deep neural network architectures are proposed: CNN-LSTM, GRU, 1D-CNN, and Transformer-LSTM, under a controlled fault injection protocol and compared with the ARIMA model and the Temporal Convolutional Network (TCN). The obtained performance is compared using regression (MAE, RMSE, MARD) and classification (accuracy, precision, recall, F1-score, AUC) metrics. Results show that the CNN-LSTM network is the most effective for fault detection, achieving an F1-score of 0.876 and an accuracy of 0.979. Regarding data imputation, the 1D-CNN network obtained the best performance, with an MAE of 2.96 mg/dL and an RMSE of 3.75 mg/dL. Then, validation on the OhioT1DM dataset, containing real CGM data with natural sensor disconnections, showed that the CNN–LSTM model accurately detected anomalies and reliably imputed missing glucose segments under real-world conditions.