Labeling anomalies

What you need to know about anomaly labeling

Labeling anomalies is an integral part of predictive maintenance. Its importance is proven through the complementarity between humans and machines.

Labeling anomalies: a crucial step in predictive maintenance

To successfully implement a maintenance strategy, a plan must be established. Industry 4.0 has brought tools that facilitate maintenance operations. The implementation of sensors connected to machine learning software has allowed companies to make significant strides in productivity.

Thanks to the collaboration between humans and machines, difficult or dangerous operations can be carried out without hindering the production chain. This reveals a certain complementarity between these two entities. On one hand, humans enable machines to improve through the labeling of anomalies. On the other hand, machines relieve humans of certain tasks.

What is anomaly labeling in the context of predictive maintenance?

Machine learning is a valuable asset for predictive maintenance. With this system, the software does not require historical failure data to identify the normal operating mode of equipment. Any deviation from this normal state is detected and translated. The software prescribes the necessary steps to correct the anomaly and prevent future failures. At the same time, the software enriches its dictionary. This allows maintenance experts to assess the relevance of an intervention.

Developed by Amiral Technologies, DiagFit enables industrial professionals to plan these interventions with the objective of increasing productivity and improving production quality.

The collaboration between Man and machines

Predictive maintenance aims to establish a balance between humans and machines. It is not about replacing one or the other, but rather seeking complementarity. Tasks deemed too dangerous or difficult are executed by intelligent machines such as cobots (collaborative robots). Equipped with a programmed arm to perform uninterrupted missions, cobots greatly facilitate human work.

At the same time, these cobots cannot operate optimally without human supervision. This is where the labeling of anomalies becomes crucial. For example, a sensor placed on a robotic arm may indicate a problematic level of wear. It is up to the maintenance expert to confirm or refute the relevance of an intervention that may require a part replacement.

Strengthening predictive maintenance

DiagFit is equipped with a powerful machine learning algorithm that allows companies to become autonomous once the environment is assimilated. However, human control is indispensable. Labeling anomalies aims to strengthen predictive maintenance in competitive enterprises. By supervising the software, maintenance experts enhance its reliability.

Eventually, DiagFit will determine the confidence score associated with the results of its own analyses. Again, the aim is not for the machine to replace humans. Even if the ultimate goal is autonomy in production systems, some interventions will still need to be carried out by humans.

In summary, it all comes down to balance and interdependence. Humans need machines to perform certain tasks, which are supervised by humans to improve asset and maintenance software performance. Predictive maintenance facilitates these processes but requires special attention, especially regarding the labeling of anomalies. Once these anomalies are identified and rated based on their significance, the maintenance expert can then carry out their work with all the information provided by DiagFit.

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