AI – the future of monitoring?
Published: 22 May, 2019
Indalyz Monitoring & Prognostics GmbH (IM&P), a German start-up company, develops and manages software and algorithms based on artificial intelligence for the control and monitoring of a very wide variety of machines and systems. PWE reports from Hannover Messe where the company was presenting at this year’s event, to find out more about its software.
Discovering damage to a machine before it can have an effect is becoming ever more critical in today’s ultra-competitive business environment.
The software from IM&P claims to promise predictive maintenance based on artificial intelligence. The system can be used for machines, plants and vehicles. It analyses large quantities of process data and machine status data measured by sensors, processes it, and can ultimately give accurate information about the progress of wear on individual parts and the damage to be expected from this. The prediction period for this, according to the company, ranges from seconds to years.
Previously, the estimation of the current machine status was based on its service life statistics. The individual strain on individual machines or systems, on the other hand, was hardly taken into account, if at all, by the analysis of regular, cost and labour-intensive inspections. Prof. Dr. Michael Schulz, Founder and managing director of Indalyz Monitoring & Prognostics GmbH, commented: “The future, however, requires that machines detect and prevent potential damage independently.” The software developed by IM&P for this is self-learning and accompanies the machines to the “end of life”.
Initially, it possesses a pool of basic information, including data based on expert engineering knowledge and the experience of other machines of the same type. Then it starts to gather information that mostly comes from the machine to be monitored and which reflects its specific operating states. The software learns from this, trains itself and draws conclusions about the future progression of wear. These forecasts are extremely accurate: The main damage groups can be predicted with a probability of 96% , only 3.5% are false alarms, and 0.5% of the damage occurs spontaneously and elude the forecast.
Avoiding breakdowns
The software can be used with wind power plants, as well as water or thermal power plants, cement mills, centrifuges, ship’s engines, and mining machinery. IM&P is currently participating in a research project with a chemical works, where engines open and close pipes and valves, through which various substances flow. From permanent testing, one can conclude whether these engines are functioning sufficiently or when they could breakdown. If the unpredicted happens, says Prof. Schulz, this might not only lead to a total breakdown of the system, but also have terrible consequences for the environment.
Second pillar: Control
The remote monitoring of systems is one of the two pillars of IM&P’s business. The second is the development, installation and configuration of system and customer-specific control hardware and software. This is also self-learning but requires no specific training phase. A decisive factor are the signals which it receives from the machine and to which it reacts in a fraction of a second – almost as fast as the electronics of the machine – therefore putting it in a position to intervene and control.
Earlier, says Prof. Schulz, it was largely linear mathematical relations that were used to control a machine. These were manageable for a human being and could be easily understood without much previous knowledge. Today, modern machines are substantially more complicated, and control and monitoring therefore require much more complex relations. This requires high-performance processors and elaborate algorithms. “Humans can no longer detect this on their own. Modern machines are multi-functional systems capable of functions that would previously have been almost inconceivable, thanks to artificial intelligence.”
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Hugo-Junkers Prize
In 2015 Prof. Schulz founded IM&P at the Technology and Start-Up Centre on the Weinberg Campus in Halle (Saale). The company was awarded the Hugo-Junkers Prize of the state of Saxony-Anhalt for its artificially intelligent machine monitoring system CASIS (Cognitives Autonomous Sensory Intelligent System) in 2017.
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