A method for monitoring a condition of a system or process, comprising: acquiring sensor data from a plurality of sensors (32, 33, 34) 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 (32, 33, 34) 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.