Achieving Optimised Infrared Thermography in Innovative Asset Management

Optimised Asset Management in recent years has embraced a rapid diffusion of innovation and disruptive technology, especially with reference to infrared thermography. The integration of this technology and associated communication technologies are improving operational aspects of industry, including the prediction of machine and component failures and as a diagnostic tool in medicine. Most machines in the future will be connected to the Internet of Things (IoT) which will be the gateway to communicating with intelligent assets with self-diagnosing capabilities and expert systems. Asset connectivity and predictive analytics will discern patterns and algorithms leading optimised plant production, and enhanced energy efficiency particularly with reference to machines. Machine learning models will indicate future operation on a real-time basis using big data libraries, tabular databases with particular reference to condition monitoring. This paper concentrates on the application of qualitative and quantitative portable infrared thermography. Successful implementation of an intelligent portable infrared thermography system requires an understanding of the industrial process; the machine operation, its surroundings, and the dynamics of infrared radiation. Optimising and integrating infrared into an asset management subsystem requires correct monitoring equipment selection and accurate data collection including; Optimum radiometer wavelength, background characterization, spatial and thermal resolution and emissivity. This paper concludes with an industrial atlas of infrared normal and abnormal images, which are a useful reference in determining the various conditions in medical diagnostics with the primary intention to identify early onset of problems as part of an optimised asset management system