Thanks to technological developments in software and hardware fields, the behavior of industrial process can be represented by a large volume of data. These data make it possible for the researchers to study the behaviors of the industrial process using data-based approaches. Investigating plant faults, fault diagnosis, and fault propagation analysis are among the most popular subjects which have always been under special attention. This issue is a matter of importance since the propagation of a fault in plants can cause abnormal or even hazardous situations leading to considerable financial, health, or environmental costs. Hence, in this research, we concentrate on the fault behavior in plants using alarm data. Employing alarm data for fault analysis does not necessarily require the process model. Therefore, it is less dependent on the knowledge of process experts. Moreover, alarm data is lower in volume and higher in time-resolution in comparison with process data. In this research, we consider two aspects of the fault analysis in the plants: fault propagation and fault diagnosis. This research firstly presents a framework for deriving a process topology indicating the fault propagation path using process mining methods. When a fault occurs in some part of the plant, it often propagates due to process variable interconnections and triggers the underlying alarms in each part of the plant. Hence, using process mining methods on the alarm data, a model of the fault behavior (propagation) can be extracted in the form of a process topology. Then, the conformity of the extracted model is measured with respect to the alarm data to evaluate its performance. In the following, using deep learning concepts, two neural networks are proposed; one for fault diagnosis and another for the next alarm prediction. These independent networks both receive a sequence of alarm data in their inputs. However, in the outputs, one network will diagnose the fault, and another will predict the next alarm. Fault diagnosis facilitates removing the fault in addition to referring to the underlying topology illustrating the fault propagation path. The next alarm prediction is of importance; especially, for the random faults following no specific topology, as it helps the operators to take precautionary actions. The whole framework is studied and implemented on the well-known Tennessee Eastman process, and the detailed results are presented. Fault Propagation, Fault Diagnosis, Alarm Management System, Process Mining, Deep Learning