Reliability of biomedical sensors is crucial for operational continuity and clinical safety, especially for critical patient monitoring systems. A Bayesian Deep Network (BDN) based model for predicting biomedical sensor failure probabilities is described in this work. The model analyzes various operational variables: sensor output signal, temperature, humidity, vibration, power consumption, and gives fault estimations in probabilities at every time interval. Differing from classical deterministic deep networks, the BDN considers weights and activation functions of the network as probabilistic variables, thus enabling the quantification of prediction confidence through epistemic uncertainty estimation via Monte Carlo sampling. Compared to standard Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) frameworks, the proposed method demonstrates 12% greater accuracy in positive predictions and 18% less false alarm rate.This suggests potential of Bayesian deep learning to enhance reliability for predictive maintenance of biomedical devices.