News from the AZT

  •                                                                                                                                                             READ MORE:

    • At the industrial insurance congress "AGCS Expert Days 2019", experts discussed the opportunities and risks of Predictive Maintenance.
    • Analysis by Allianz Global Corporate & Specialty shows that Predictive Maintenance Systems help to prevent damage to industrial plants. However, they are not a guarantee for avoiding maximum loss events.
    • Failures or malfunctions due to misinterpreted data or insufficient data quality have been identified as new risks.

    Munich - October 29, 2019 - In the course of industrial networks (Industry 4.0), Predictive Maintenance is making its way into many factories and industrial plants. In the best case scenario, this allows faults to be predicted and maintenance/repair work to be initiated before failures occur. "Predictive Maintenance will help to reduce many smaller property damage claims on machinery, but it is no guarantee against major losses and creates its own risks," explained Hartmut Mai, member of the Board of Management of Allianz Global Corporate & Specialty (AGCS) at the AGCS Expert Days, an engineering conference held by the industrial insurer in Munich.

    Predictive Maintenance is one of the major new technologies in Industry 4.0 - according to the AGCS Trend Compass, predictive data analysis and automation is one of the most important future technologies across all industrial sectors. Machines and plants are proactively maintained with the help of sensors to keep downtimes low. The main difference to "Condition-based Maintenance", where maintenance is performed based on the condition of components, is the ability to predict the lifetime of machine parts using rule-based models, simulations and artificial neural networks. For this purpose, large amounts of data are collected, stored and analyzed via sensors. The Predictive Maintenance System is designed to automatically detect and interpret anomalies within the machine data. In the best case, this will allow to predict malfunctions before they have negative effects or even failures.

    In an analysis, the Allianz Center for Technology (AZT) at AGCS has taken a thorough look at Predictive Maintenance - and has identified the first concrete effects on opportunities and risks: Predictive Maintenance Systems can help to change technical risks and prevent damage. "In concrete terms, for example, the correct interpretation of vibration characteristics can detect a gradually growing crack in a shaft and, by stopping it in time, prevent a dangerous shaft break with extensive consequential damage," states AZT engineer Thomas Gellermann as an example.

    The risk and extent of business interruptions, which according to the Allianz Risk Barometer is the greatest business risk for companies worldwide, can also be reduced by Predictive Maintenance if certain types of faults are detected at the right time and replacement is planned early on without impairing plant availability. "Predictive Maintenance can minimize downtimes and thus save costs, especially in the case of machines and systems for which the manufacturer has not scheduled overhauls or revisions, and where the causes of failure and mechanisms of action are largely known. Wind turbines are a good example of this," explains Gellermann.

    However, the AZT study also shows that when Predictive Maintenance is used, some risks remain unchanged - and new risks can even arise. Above all, possible spontaneous events cannot be prevented despite the new technology, if no measurable effects can be identified in advance: The bursting of the low-pressure turbine shaft at the Irsching power plant on New Year's Eve in 1987, still one of the largest metallic fractures worldwide, could not have been prevented even with Predictive Maintenance, as the triggering part was not monitored. "Even maximum damage events cannot be ruled out due to the modern maintenance method," explains Gellermann.  In addition, if revision cycles were to be extended as a result of the new maintenance concept, wear and tear and malfunctions of unmonitored machine parts could not be detected at an early stage.

    A further risk of Predictive Maintenance Systems is the quality of the data collected. Data is equally susceptible to failures or malfunctions or could be manipulated by cyber attacks or acts of sabotage. As a result, data could be misinterpreted or potentially harmful control commands could be initiated.


    "As one of the world's leading industrial insurer, we want to play a part in establishing Predictive Maintenance methods. We are convinced that this new technology-based form of maintenance will sooner or later become more widespread in companies. However, due to the complexity of machinery and the error mechanisms involved, it will not be possible in future to carry out maintenance exclusively on the basis of the predictive model, but maintenance measures will continue to be necessary at fixed intervals.

    At the same time, the AGCS wants to raise awareness of the new risks. "Industry and insurers must intensively address the opportunities and risks of maintenance technologies - and cooperate closely with technical providers," says Thomas Meschede, Head of Allianz Risk Consulting in Central and Eastern Europe. Even when taking Predictive Maintenance into account, the risk assessment must not be made in a blanket way, but requires the assessment of an expert, depending on the application, the optimization goal and the execution. It is important not to abandon critical and proven principles, which serve to protect the machinery, , such as separating Predictive Maintenance Systems from the known protective functions of the plant.

  • On 13.02.2020 Thomas Gellermann held a live webinar for the Association of German Engineers (VDI) entitled "Do wind turbines need vibration monitoring and condition monitoring?” 

    Wind turbines provide a significant contribution to renewable electricity generation. In the long term, a further expansion of wind energy can be expected both nationally and internationally, as a result of increases in plant capacity and cost reductions in production. Detailed knowledge of the plant condition is becoming increasingly important for the economic efficiency of technically sophisticated plants as a basis for intelligent maintenance. For this purpose, vibration-based Condition Monitoring Systems (CMS) have been established as standard for some time. They provide important information for condition-based and Predictive Maintenance. However, the systems can also provide important information about the operating behavior and possible harmful vibration conditions. This is the task of Vibration Monitoring, which is used to identify wind turbines with conspicuous behavior in comparison to standardized reference values or the turbine fleet on the basis of standardized assessment quantities.

    In the webinar, Thomas Gellermann presented standardized evaluation parameters for vibration monitoring as a supplement to condition monitoring and its benefits. In a live survey, 95% of the webinar participants decided that the potential synergies from the use of CMS and vibration monitoring were reasonable (Link to slides of Webinar).


    The Allianz Center for Technology (AZT) has been committed to the spreading and use of vibration-based condition monitoring on wind turbines for almost 20 years. These systems help to increase the reliability of the turbines and reduce damage. For this reason, DNV GL has been requiring the use of Condition Monitoring Systems (CMS) as a prerequisite for the type certification of offshore wind turbines for several years now. In the certification guideline, the technical requirements developed by AZT are referred to.

    For onshore wind turbines, on the other hand, there is still no obligation to use condition monitoring systems. For this reason, the systems are only installed as an option at the customer's request, despite the fact that the turbines are continuously getting bigger and more powerful. According to Thomas Gellermann, this is saving money at the wrong end. According to his experience, operators who manage the maintenance of the turbines themselves have been relying on the use of CMS in the majority of cases for years. While vibration-based condition monitoring has become even less common for plants maintained under full service contracts.

    Studies show that the consequential costs for a large proportion of the drive train damages that occur can be reduced by early detection, and that the insurance company can also benefit from this. For many years, vibration monitoring has been a standard feature of turbosets in industrial and power plants, even in the similar power range to wind turbines. Wind turbines, on the other hand, are only entered once or twice a year and only during standstill. It is therefore even more incomprehensible that condition monitoring and vibration monitoring are not yet a requirement for onshore plants. 

    The AZT contributes its extensive loss experience not only within the Allianz (project-related or as lessons learned), but also passes on its knowledge on loss prevention to the industry via conferences, customer events and other forums, such as this VDI webinar.

  • You can find a report from EXPERT DAYS 2019 here