Predictive maintenance: Labeling anomalies

What you need to know about anomaly labeling

Labeling anomalies is a central step in predictive maintenance. Its significance is demonstrated through the complementarity between humans, domain experts, and machines. In this article, explore what anomaly labeling involves and how this crucial step strengthens predictive maintenance.

What is anomaly labeling in the context of predictive maintenance?

A crucial step in predictive maintenance

Thanks to the collaboration between humans, namely business experts, and industrial machines and equipment, operations deemed difficult or dangerous are now easily carried out without disrupting the production chain. A certain complementarity is observed between these two entities. On one hand, humans enable machines to improve through anomaly labeling, and on the other hand, machines relieve humans of certain tasks. This complementarity is essential for enabling companies to enhance their industrial performance.

DiagFit, blind failure prediction software

Machine Learning is a definite asset for industrial applications in the realm of predictive maintenance. It is on the basis of this technology that a predictive maintenance software, such as DiagFit, manages to identify the healthy operational mode of equipment. Furthermore, using our DiagFit software allows for the utilization of the blind mode, our innovation that enables industrial users to not require historical failure data to provide reliable failure predictions.
As a result, any deviation from this normal operating space is instantly detected and translated by our software. DiagFit then prescribes the necessary steps to correct this anomaly to prevent future breakdowns. Simultaneously, the software enriches its dictionary of anomalies for easy identification if the anomaly were to recur. In this sense, it enables maintenance experts to define the relevance and priority of an intervention.
Developed by Amiral Technologies, DiagFit enables industrial entities to plan these interventions with the goal of reconciling increased productivity and improvement with a reduction in maintenance costs.

The collaboration between Man and machines


Predictive maintenance aims to establish a balance between humans and machines. It is not a question of replacing one or the other but rather creating complementarity. Tasks automated by artificial intelligence and software cannot function optimally without the supervision of business experts. This is where the labeling of anomalies, for example, becomes crucial. Thanks to the contributions of business experts, Incremental Learning, meaning dynamic learning, can be utilized throughout the equipment’s lifecycle, thereby enriching its knowledge for future predictions.


As a result, a sensor placed on an electric vehicle battery, for instance, may indicate an abnormal level of wear, requiring analysis by a human. It is the maintenance expert’s responsibility to confirm or deny the relevance of an intervention that could lead to a component change to prevent a vehicle breakdown.


Strengthening predictive maintenance


A predictive maintenance software, such as DiagFit, is equipped with powerful machine learning algorithms that enable businesses to operate independently once the environment is assimilated. However, human oversight is indispensable. Anomaly labeling aims to strengthen predictive maintenance in competitive enterprises. By supervising the software, maintenance experts enhance its reliability.


Over time, DiagFit will autonomously determine the confidence score associated with the results of its own analyses. The ultimate goal is the autonomy of production systems, although certain interventions will still need to be carried out by domain experts.


Lastly, predictive maintenance streamlines these processes but requires special attention, particularly regarding anomaly labeling. Once these anomalies are identified and prioritized, the maintenance expert then performs their role with all the information provided by DiagFit.

DiagFit, our software for failures prediction on industrial time series

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