| Monitoring systems
Embracing digital transformation is essential, with the objective to provide real-time access to enterprise data and predictive performance using management tools for improved operations, maintenance, reliability and logistics. The benefits include optimal resource management, fast, accurate data-driven insights, improved efficiency, and agile production. Condition monitoring will have an important role to play in digitalisation for increasing asset utilisation, driving down asset maintenance costs, reducing downtime, and improving operational efficiency. Traditional condition monitoring systems have been typically stand-alone systems with their own proprietary servers and data storage. This is changing, however, due to the hydropower digital transformation needs.
Condition monitoring solutions First and foremost, there is no unique ‘out-of-the-box’
condition monitoring solution that fits all hydropower applications and all current customer needs. This, however, is not a problem by current standards, since there are monitoring solutions today that integrate seamlessly with other data systems in an enterprise- wide platform, and thus provide more value in machine healthcare because of the digital transformation. What is important nowadays is how data is analyzed and managed in a monitoring solution. Although measurement techniques used in
monitoring machine components have not changed much over the years, the way the measurement data is analysed has changed dramatically. Instead of detecting a machine fault based on single vibration symptom that is monitored to fixed alarm limits, several vibration measurements including process parameters can now be automatically analysed together using data-driven AI algorithms, pattern recognition, digital twins, and other means, which ultimately result in more reliable and earlier fault detection. In addition to earlier anomaly detection, AI and ML algorithms can also be used for improving diagnostic insight of a fault, predicting remaining useful life and providing automatic decision support for planning maintenance and optimizing operation. It can be provided by the monitoring system itself or by a remote service. For data-driven AI tools to be successful, however, a lot of data is needed and it must be managed effectively. Therefore, in addition to new analysis techniques, one of the other big changes from traditional monitoring solutions is how the data is managed, i.e. how and where it is stored, processed, analysed and shared. There are several solutions for this, of which all can be combined.
Off-premise data management and service Data is stored in off-premise cybersecure cloud services, and there is no need for a data acquisition unit when wireless sensors are used. This setup allows for fast and easy installation, configuration, and commissioning, and no monitoring system servers must be managed. It is also possible to install wired condition monitoring edge devices, if needed, for a more advanced monitoring solution. No matter which sensors or monitoring devices are installed, the ‘condition monitoring as a service’ solution provides actionable insights – supported by AI and approved by experts – through a remote monitoring and diagnostic service. This is the perfect solution for those end-users who do not have in-house diagnostic expertise, or there is a need to reduce the workload on the specialists who must watch over a lot of machines. In its purest form, an off-premise monitoring solution is typically a stand-alone proprietary solution that is not integrated with an enterprise-wide data management system, but this is sufficient for many small-time operators who are running small hydropower units. Even for larger operators with enterprise data management and large generating units, the off-premise solution can be used for monitoring balance-of-plant pumps and fans, which may not necessarily be integrated in the historian. Of course, it is still possible to save data to the historian while the monitoring and diagnostics are done remotely, off-premise, but this raises a cyber security risk if not properly managed.
On-premises with enterprise-wide data management and service
In this case, the data acquisition unit and sensors are installed on-premise, but the data is stored and
Above: Figure 2. AVEVA PI System trend plot for a given hydropower station and machine component (lower generator guide bearing shown selected). Trends shown are for power (active, reactive, apparent), bearing temperature (for each bearing segment), X-Y relative vibration and displacement and flow. The icon circled in red opens the SETPOINT plots for vibration, such as shown in Figure 4.
Below: Figure 3. In addition to enhanced plots for vibration diagnostics (such as shown in Figure 4), the SETPOINT system also has proprietary plots for air gap. In the far left of the left image, the averaged air gap for each rotor pole is shown from one of the sensors. Next to that plot, the unaveraged time waveform air gap signal is shown. The image on the right shows the circular air gap plot
www.waterpowermagazine.com | August 2025 | 29
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