Green Tech Magazine December 2017 en

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14 Digital service excellence The term predictive maintenance describes the anticipatory digital maintenance of environmental technology machines and plants. It prolongs life cycles and reduces the environmental impact. Annually the market grows by 20 to 40 %. “The drive components of all our machines require oil to function properly. We have defined criteria for assessing the condition of power train components by means of components in the oil and their concentration. In the case of gearboxes, we know what is likely to cause bearing damage,” says Komptech’s Managing Director Christian Oberwinkler. Customers can send oil samples to a central laboratory every 250 to 500 operating hours. Should one of the components show a critical concentration, the customer receives suggestions for the necessary measures. “Machine availability is a decisive competitive advantage for us because it is vital to our customers’ business success,” says Oberwinkler and adds that “the next challenge will be to integrate manual models for predictive maintenance into the available digital tools. In this respect, we are dependent on the development of sensor technology and its costs.” Predictive maintenance minimises maintenance effort and costs. 70 % of the total operating costs of machines and systems are incurred during the service phases. Predictive Maintenance is booming “PM has established itself as an important industry trend in the European mechanical engineering sector. Experts worldwide expect the market to grow by 20 to 40 % annually across all industries and fields of application,” Sebastian Feldmann and Vladimir Preveden of Roland Berger Strategy Consultants state. Nevertheless, many companies find it difficult to arrive at a clear strategy and development budgets. “The aviation industry is a pioneer, the automotive industry is following suit, and railways are just beginning to do so,” says Helmut Ritter, Head of Engineering for Bogies at Siemens Mobility in Graz. Siemens is responsible for the construction, maintenance and availability of train fleets. From mid-2018 onwards, data on the condition of certain components will be provided in order to predict and correct errors before they occur, to provide maintenance recommendations or to inform about the remaining service life. For the most part, the system uses vibration sensors. In order to be able to perform PM, sensors, data transfer and data processing must be integrated into a service package. “IT and sensor technology offer many possibilities; the topic has gained new momentum with today’s technical possibilities, because it is now possible to process and evaluate a lot of data,” says Ritter. The results of the analysis can also be used to optimise the logistics for the necessary replacement of components. However: It is particularly important – also with regards to costs – to carry out relevant measurements for a high data quality and to interpret the results. This is usually done by adjusting the error patterns using stochastic algorithms. “The know-how lies in the subsequent processing of raw data. It’s all about knowing what a triggered sensor means: Does it – in the case of trains – show problems with a component or an irregularity on the track, for instance?” Experience values are just as relevant as knowledge of the ageing process of materials. Wolfgang Jilek’s Cartoon – Just in time maintenance New service models Franz Langmayr, Managing Director of Uptime Engineering, points out that “PM can only be used economically under certain conditions: in case of high investment value, high follow-up costs or high availability requirements.” The more diverse component stress is, the more profitable an investment will be.
SEQUENCE AND DECISIONCONTROL Total asset decisions R ICE AND BUSINESSMODELS SERV Automatized working instructions and material for service teams “Closed-loop” quality management Computerized maintenance monitoring system Customised planning of maintenance activities 20 22 20 22 Multi-partner management Full operations outsourcing Optimisation of asset lifecycle management Risk protection Uptime guarantees Pay per X model Fully automatized service workflow Experience models Working instructions / secondments / mobile teams Internal product optimisation Working instructions and material for local servicing Training and education Individualised software / algorithm Enterprise total asset mgmt./TCO Machine learning / artificial intelligence Real time data/image prognosis Software robots Marketplace solution: Platform for different applications Digital design and local 3D spare part printing Service cost optimisation (cont.) Optimised warehouse & supply chain (produce to order) Automatization of service Spare part management Continuous improvement process Focus training and education Trend analysis Correlation analysis Pattern recognition (single state) Pattern recognition (fleet) Edge prediction Automated domain know-how 20 22 20 17 Sensors for “ultra high robustness” SENSORS Autonomous drones for (i.e. thermographic) inspections Novel sensors (design, installation, capturing signals) Intelligent sensors for unstructured data Advanced nondestructive testing (z.B. Ultrasonic) Plug & Play solutions Intelligent sensors for structured data Brownfield dongles etc. Selective visualisation system data of deviations Blockchain Fully automated Data storage root-cause-analysis (clouds etc.) Sensor fusion Short range, low power transmissions (e.g. NFC) Customized UI/ visualisation Diagnostic servers Data structuring / automated index / selection / machine learning / AI Realtime data analysis / big data Supply chain integration Customer decision-making integration Global remote data access Fully automated image analysis Integration external data sources (i.e. weather) Integration of production-, process- and ecological Edge computing/ analytics Local sensor analytics/ in-memory computing Diagnostic fusion (diagnose matching) 20 22 GREEN TECH RADAR. The PM topics of prognosis, process control and service models require comprehensive innovation. 20 22 DA A D T AT AND SIGNA TA N L PROCESSING NA FORECASTING ABILITY Pattern recognition and forecast ( process- and ecological system data) 20 22 A NOSIS CONDITION MONITORINGANDDIAG AG Relevance of devolopment low average high Type of development radical incremental Green Tech Radar Credits: Shutterstock, beigestellt, CARTOON: Wolfgang Jilek In addition, companies would have to deal with possible business cases more intensively than before. Deep learning – self-learning algorithms for relevant results from heterogeneous data – works in market research, but “there are still no convincing solutions in the field of technology.” That’s why Langmayr and his team are pursuing an alternative, engineering-based approach: “We create models that predict system behaviour as a function of stress history and framework conditions and use these models to detect deviations. In a second step, we feed an expert system with the know-how about failure possibilities in order to automate the diagnosis of causes. We use the diagnosis to select damage models we use to calculate the residual life.” The possibilities of data collection are immense while processing remains challenging, Feldmann and Preveden sum up. According to them, PM does not replace physical maintenance and customer orientation, but offers forward-looking ways of differentiating service. www.greentech.at/print Exclusively from the Green Tech Radar: Predictive maintenance opens up growth opportunities for cluster partners.
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