A method for monitoring a condition of a system or process, comprising: acquiring sensor data from a plurality of sensors disposed within the system; streaming the acquired sensor data in real-time to a computer system ; at the computer system , collecting the acquired sensor data corresponding to a current time value and a plurality of earlier time values to yield a plurality of sensor data observations; applying, by the computer system , a Kalman filter having Kalman filter parameters θ to determine a plurality of hidden sensor state variables based on the plurality of sensor data observations; determining, by the computer system , a probability of malfunction or failure within the system using a logistic function parameterized by the plurality of hidden sensor state variables; if the probability indicates a system malfunction or failure, automatically initiating a remedial action comprising one or more of generating an alert, partially or fully suspending operation of the system , generating a service order for system maintenance, or generating a purchase order for replacement parts for the system , wherein the logistic function is: P y t | u t , w = 1 1 + exp − y t w T u t where y t is equal to one of two discrete values at time t, indicating failure or normal operation of the system , uf represents the plurality of hidden sensor state variables at the given time t, where the plurality of hidden sensor state variable values are real numbers having a Gaussian distribution depending on Kalman filter parameters θ , w is a parameter of the logistic function and w T is the transpose of w, and wherein θ and w are learned by maximizing a log likelihood function with respect to θ and w using a set of training data. acquiring sensor data from a plurality of sensors disposed within the system; streaming the acquired sensor data in real-time to a computer system ; at the computer system , collecting the acquired sensor data corresponding to a current time value and a plurality of earlier time values to yield a plurality of sensor data observations; applying, by the computer system , a Kalman filter having Kalman filter parameters θ to determine a plurality of hidden sensor state variables based on the plurality of sensor data observations; determining, by the computer system , a probability of malfunction or failure within the system using a logistic function parameterized by the plurality of hidden sensor state variables; if the probability indicates a system malfunction or failure, automatically initiating a remedial action comprising one or more of generating an alert, partially or fully suspending operation of the system , generating a service order for system maintenance, or generating a purchase order for replacement parts for the system , wherein the logistic function is: P y t | u t , w = 1 1 + exp − y t w T u t where y t is equal to one of two discrete values at time t, indicating failure or normal operation of the system , uf represents the plurality of hidden sensor state variables at the given time t, where the plurality of hidden sensor state variable values are real numbers having a Gaussian distribution depending on Kalman filter parameters θ , w is a parameter of the logistic function and w T is the transpose of w, and wherein θ and w are learned by maximizing a log likelihood function with respect to θ and w using a set of training data. <EOS>