The combination of choreographed movement and precise machining makes watching a multi-axis machine in action mesmerizing. The beauty comes from the orchestrated yet precise actions and pleasingly perfect results. But this involves complex movements, each impacting axes in different ways. This makes condition monitoring complex too.
Multi-axis machinery has complex failure modes. Alongside the impressive coordinated axes’ pivoting, each is subjected to variable factors, such as payload, operating speed and environmental conditions. And then a multi-axis machine is generally used for multiple products, for example a welding machine in an automotive factory handling multiple car models. And finally, in practice there are often physical sticking points, creating anomalies in sensor data. That’s a lot of factors to apply to condition monitoring data to make it relevant and useful!
Collecting data for Condition monitoring
Condition monitoring data is collected from sensors, historical maintenance data and manufacturer’s machinery data. Senseye’s experience shows that condition monitoring data for multi-axis robots should satisfy these requirements:
- There should be separate measures for the major components in each robot axis; typically motors and gearboxes or reducers
- Data should be captured when the robot is in a known consistent state. For example, at the end of each operating cycle as the robot arm returns to the home position.
For a condition monitoring solution to be successful dealing with the complexity of failure modes in multi-axis machinery, it is not only the data collected but, perhaps more importantly, how the solution manipulates and models collected data to detect abnormal behavior – and go on to predict failures ahead of time, preventing unplanned downtime and reducing potential damage to the asset.
Selecting a solution (or solutions)
Many equipment manufacturers offer dedicated condition monitoring services on a subscription basis. With this approach, each asset would be monitored independently of the rest of the site. The benefits are clear – it’s an easy add-on when purchasing the machine. However, there are limitations to look out for. First, as asset-specific solutions, if you are investing in site-wide predictive maintenance, you will need to manage multiple solutions, vendors and datasets. Second, some solutions operate on a threshold basis; simply counting the number of completed operating cycles, triggering an advisory message when a threshold is reached (in a similar way to preventative maintenance). This doesn’t make use of condition monitoring data, takes no account of the actual condition of the equipment and may result in over-maintenance or – worse – a missed failure resulting in unplanned downtime.
In contrast, Senseye PdM is asset-agnostic, enabling you to benefit from one predictive maintenance solution which is scalable across all asset types and multiple sites.
Benefits of condition monitoring
Implementing a predictive maintenance strategy put you back in control of site-wide asset maintenance. Senseye PdM is cloud based and asset agnostic, meaning you can scale up or down quickly and cost-effectively. Benefits include:
- The ability to monitor as many or a few assets as needed, scaling up or down as required
- Reduce the financial and time burden of preventative maintenance
- Enable the planning of opportunistic corrective maintenance
- Prevent unexpected failures.
Multi-axis, multi-complex, one PdM solution
By definition, multi-axis machinery has complex movements, stressors and failure modes. Condition monitoring can provide a more efficient, cost-effective maintenance approach, extending asset life and reducing maintenance costs. An asset-agnostic solution like Senseye PdM can often use existing data sources for a fast, cost-effective setup, quick meaningful results and rapid return on ROI (which is now guaranteed via ROI Lock™).
Download our white paper: Best Practices in Condition Monitoring for Multi-Axis Equipment to learn more!