So far, a machine had to stop working until it was repaired. Nowadays, intelligent systems detect a defect before it leads to a shutdown. Predictive Maintenance can save significant operating costs by prolonging maintenance intervals and by avoiding unplanned shutdowns. Predictive Maintenance is very closely
connected to terms such as Internet of Things (IoT). The constant monitoring of important components is realised with sensors located directly at the turbomachine itself. These sensors measure, amongst other data, vibrations, temperatures, and pressures. Data that generally only get collected are analysed and are used to detect changes in the condition of the machinery. Especially for complex turbomachines dedicated solutions are necessary, which require detailed knowledge of the machinery. This approach helps, for example, to protect a turbocompressor against so called surging by analysing the actual performance map under conditions such as the gas composition. Concepts that are based only on machine learning and algorithms are not suitable for such complex tasks.
The application of the TurboMonitor by Industrial Analytics effectively reduces operating costs for the users of turbomachinery. Continuous monitoring of the conditions of the turbomachine and its parts significantly reduces downtime due to surging, oil impurities, bearing faults, and defects of drivers and valves. Early
indications of imminent defects also allow for planned maintenance and to adjust the operation of the plant accordingly. With the gathered process parameters
sophisticated algorithms allow for the determination, tracing, and evaluation of performance data of the monitored turbomachinery. This also leads to the detection of potentials for optimization of the machine’s efficiency for even lower operating costs. The TurboMonitor systems developed by Industrial Analytics use the existing infrastructure and sensors and are adjusted to the individual application and machine.