Diagnostics plays a fundamental role in industrial engineering and nowadays is an essential part of performance requirements since it allows to optimize maintenance policy improving the plant reliability and availability and minimizing the life cycle cost. Internet of Things (IoT) technologies help to implement effective and efficient diagnostic in different fields within the context of Industry 4.0. In this paper a low-cost diagnostic system for condition monitoring of industrial plant is presented. Different sensors technologies are proposed to monitor the health indicators of electric, electronic, mechanic, and hydraulic components. These sensors could be used to estimate the plant conditions and its remaining useful life through direct measurements or by means of indirect assessment, such as statistical tools or artificial intelligence. This paper presents a reliability and availability analysis of a Condition Monitoring system using Reliability Block Diagram and Monte Carlo Simulation. The performances of different architectures have been compared, emphasizing the improvements achieved using standby redundancies in terms of reliability and availability of the proposed diagnostic system.