Fast incipient fault diagnosis is becoming one of the key requirements for safe and optimal process operations. There has been considerable work done in this area with a variety of approaches being proposed for incipient fault detection and diagnosis (FDD). Incipient FDD problem is particularly difficult in the case of chemical processes as these processes are usually characterized by complex operations, high dimensionality and inherent nonlinearity. Neural networks have been shown to solve FDD problems in chemical processes as they develop inherently non-linear input-output maps and are well suited for high dimensionality problems. In this work, to enhance the neural network framework, we address the following three issues, (i) speed of training; (ii) introduction of time explicitly into the classifier design; and (iii) online updation using a mirror-like process model.