Reliability is a fundamental attribute of industrial systems and can be analytically computed using reliability models. Industrial systems are nowadays complex systems, i.e. they are characterized by a large number of components. This characteristic generally makes the analytical analysis unfeasible. Generally, reliability is expressed, under the assumption of constant failure rate, through the Mean Time Between Failures (MTBF). The reliability analysis performed using the MTBF is generally accurate for time intervals close to the commissioning and to the end-of-life but tends to be inaccurate otherwise. We propose a novel bottom-up layered approach to obtain the reliability curve. The first layer is realized with Stochastic Time Petri Nets (STPNs), the second one with Reliability Block Diagrams (RBDs). This layered approach allows to improve the analysis of system reliability, thus improving maintenance plans and implementing Condition-Based Monitoring prognostics applications.