The failure prediction software that monitors your equipment's health
DiagFit is a blind failure prediction software, an essential step in the predictive maintenance process allowing to anticipate and plan machine downtime.
Based on state-of-the-art unsupervised machine learning algorithms for industrial time series management, DiagFit enables the extremely rapid implementation of a solid and reliable failure prediction solution.
Feature generation, the core invention of the software, extracts particularly rich mathematical properties that allow DiagFit to efficiently detect in real time the weak signals of failure or aging of these equipments.
Based on a unique incremental approach, our DiagFit tool relies on artificial intelligence to enable dynamic learning of equipment operation throughout its life, driven by feedback from business experts.
Based on state-of-the-art unsupervised machine learning algorithms for industrial time series management, DiagFit enables the extremely rapid implementation of a solid and reliable failure prediction solution.
Feature generation, the core invention of the software, extracts particularly rich mathematical properties that allow DiagFit to efficiently detect in real time the weak signals of failure or aging of these equipments.
Based on a unique incremental approach, our DiagFit tool relies on artificial intelligence to enable dynamic learning of equipment operation throughout its life, driven by feedback from business experts.
Advantages of DiagFit in failure prediction :
Speed
its “blind mode” approach allows failure prediction models to be generated in a few hours/days
Accuracy
its high-performance technological core leads to an extremely low false alarm rate
Explicability
its sensor information and failure recognition facilitate efficient and targeted equipment intervention
Universality
DiagFit works with all types of equipment and sensors, as long as they produce time series
Usability
its No-Code interface requires no knowledge of coding or data science
Autonomy
its intuitive handling allows users to use the software independently
Examples of DiagFit interface
DiagFit, an iterative process in 4 steps
The software allows to generate a predictive model dedicated to a device without any knowledge of coding or data science. This step, called “BUILD “*, takes only a few minutes/hours.
Manual modification of some of the model parameters is available for more experienced data science users.
* This step can be performed by our experts, please contact us.
Once generated, the model can be tested on one or several new data sets. This step aims to test the robustness of the model, to refine the normality space previously generated in step 1 and to initialize the fault dictionary which will be used in step 3 and enriched in step 4.
The tested model is then associated with working equipment in this operation step called “RUN”. Technicians and other experts are alerted when operational deviations are observed in the data. They then benefit from the software’s indications to identify the origin of these deviations, called anomalies, and intervene effectively to restore the affected part or equipment to working order.
Maintenance operators, knowing the functional aspects of the equipment involved, can then accept or reject the detected anomalies. This step aims to complete the model’s learning process and enrich the dictionary of anomalies that will enable automatic recognition of failures in the future.
Build
The software allows to generate a predictive model dedicated to a device without any knowledge of coding or data science. This step, called "BUILD "*, takes only a few minutes/hours.
Manual modification of some of the model parameters is available for more experienced data science users.
* This step can be performed by our experts, please contact us.
Test
Once generated, the model can be tested on one or several new data sets. This step aims to test the robustness of the model, to refine the normality space previously generated in step 1 and to initialize the fault dictionary which will be used in step 3 and enriched in step 4.
Operate
The tested model is then associated with working equipment in this operation step called "RUN". Technicians and other experts are alerted when operational deviations are observed in the data. They then benefit from the software's indications to identify the origin of these deviations, called anomalies, and intervene effectively to restore the affected part or equipment to working order.
Enrich
Maintenance operators, knowing the functional aspects of the equipment involved, can then accept or reject the detected anomalies. This step aims to complete the model's learning process and enrich the dictionary of anomalies that will enable automatic recognition of failures in the future.
“A virtuous loop is then set up as the equipment's life goes on.”
Failure prediction software with high deployment flexibility
Public or private cloud
Azure, OVH, AWS or any other proprietary cloud, DiagFit can be integrated to all your environments
On-site
DiagFit is designed to be deployed on the most demanding infrastructures
Integrated with a third party platform
The power of our technology can be redirected to a third party application thanks to our REST APIs
Embedded
Contact us for more information
A wide range of use cases
Thanks to the blind mode and its easy implementation, DiagFit can cover a large number of use cases in our different key markets (transportation, manufacturing, energy):
Rotor blades
Transport
Nuclear pipelines
Energy
Wipers
Transport
Cryopumps
Energy
Industrial PCs
Industry
Train doors
Transport
Welding robots
Industry
Circuit breakers
Energy
Radars
Transport
Injection pumps
Energy
FAQ
Frequently Asked Questions
Yes, the power of our technology can be redirected to a third-party application through our REST APIs. We are setting up a downstream API and an upstream API.
Our ability to avoid failure labels, the speed of model creation and their performance.
Our models are built on the basis of healthy data, so it allows you to start creating models without fault labels. DiagFit is often used as a productivity tool to build many models in a very short time. In addition, the performance of our algorithms allows the identification of weak signals undetectable by conventional approaches, and allowing us to characterize the anticipation of failures
Yes, DiagFit is robust to context changes as long as the data has been seen in training. In addition, our feedback functionality makes it possible to enrich the models previously built.
- When a new anomaly appears, the user has the option of approving or rejecting this anomaly:
- If it is a real anomaly, the user can label it and thus enrich a dictionary of failures. DiagFit is able to calculate the probability of its belonging to one or more categories of failures already seen. It then gives the user a confidence score with regard to this labelling.
- If it is a false anomaly (or false positive), the user can reject it and re-train the model taking this behavior into account
No. We train the models on healthy data, however, failure labels can be useful to evaluate the performance of the models created by DiagFit.
Yes, this eliminates a certain number of FPs and fault detection is only better.
Amiral Technologies takes advantage of the best of both approaches by working on dynamic learning throughout the life of the equipment. This methodology is particularly relevant in the industrial context where occurrences of failures are rare and occur over time.
