Industrial plant assets are increasingly being perceived as “Working Capital” of an organization. Therefore vibration monitoring continues to assume strategic significance within a large range of industries, from manufacturing to automotive and aerospace.

The increasing demand on reducing operating costs lead to the conclusion that asset condition monitoring can actually be a medium to long-term cost saving investment rather than an operating expense. Consequently, the condition monitoring market is forecast to reach US$2.1 billion by the year 2015.

Monitoring vibration, however, is only one aspect of the problem. The other aspect has to do with controlling it, in order to reduce the wear and tear.

The control aspect also has a very large market. According to the EC report on the monitoring and control market by 2020, Europe has captured roughly 1/3 of the global market, on par with the USA and Asia, to the amount of 62B€ in 2007. Even in spite of the current economic crisis, the Monitoring and Control sector is expected to continue to grow at 7% per year at a much higher rate than the overall economy, thus pointing towards a figure of 143B€ in 2020.

Current solutions for vibration/condition monitoring, however, lack several important qualities needed to meet the stringent requirements of today’s industries in terms of certain qualitative and quantitative metrics, such as flexibility, reliability, performance, energy efficiency and cost.

One of the main bottlenecks is represented by the exorbitant installation and maintenance cost due to the laying of cables, which themselves are prone to wear and tear. Cables can also impose inconvenient restrictions on the design of monitoring and control systems, in terms of design flexibility, weight and bulk. Thus the use of cables also automatically limits the extent to which monitoring and control can be carried out, as the number of points of measuring and control is limited.

Wireless systems represent an appealing solution to these problems. By leveraging the cabling costs and allowing simple, plug-and-play deployment, wireless sensors have theoretically a huge market opportunity in the industrial monitoring and control arena. However, the market traction is still slow.

There is still skepticism about the performance and reliability of wireless sensors, as opposed to their classical, wired counterparts. Wireless sensors are still seen through the “Smart Dust” vision: small, cheap, even disposable, unreliable, but many.

We argue that a high-performance, versatile and robust wireless sensing system is needed in order to make a leap forward from the “Smart Dust” vision and come closer to the expectations and requirements of the industrial arena. This is precisely the main objective of the SIRIUS project: to research and develop a high-performance, versatile wireless system with self-powered capabilities, used for data acquisition, process monitoring and control, with applications in industrial vibration monitoring, condition monitoring and predictive maintenance. An embedded / deep-embedded solution for wireless monitoring and control will be provided, following the idea of distributed intelligence, which now penetrates into modern production lines and technological plants.

Such a high-performance system with high-speed accurate sensing, on-board data processing, high-throughput, low-delay, real-time wireless communication, and self-powering capabilities from energy harvesting from vibration is the ideal candidate for a large number of industrial applications. Such a system can support sustainable maintenance and advanced monitoring and control, and thus contribute to increased safety, optimized energy consumption, improved risk-based analysis and even reduced environmental pollution in a large range of industries.

The SIRIUS project will research and develop a high-performance, versatile wireless system with self-powered capabilities, used for data acquisition, process monitoring and control, with applications in industrial vibration monitoring, condition monitoring and predictive maintenance.