BLIND FAILURE PREDICTION

Industry 4.0
Predictive maintenance drastically reduces downtime and promises significant productivity gains.

Unplanned downtime is the weak link in the industrial sector. When production lines and machines stop working, manufacturers lose money. These downtimes lead to costly maintenance times. They can be avoided if the machines are monitored in real time. Predictive maintenance can eliminate maintenance overhead by allowing crews to perform maintenance on the fly to avoid failure, rather than at fixed intervals. Predictive maintenance drastically reduces downtime and promises significant productivity gains.

 

Monitor your equipment effectively: sensors and software

Predictive maintenance is only possible with effective machine condition monitoring, which can reliably predict when each component will fail. Machine condition monitoring, in turn, relies on taking data from machines and using it to create predictive models of how equipment condition will change. Current manufacturers use the data collected by sensors installed on their equipment to be able to retrieve and analyze the data.

Real-time monitoring has recently become an affordable and practical solution for the first time in many industries, thanks to the arrival of techniques and technologies associated with Industry 4.0.

The next generation of AI solutions in predictive maintenance and machine learning enable business experts to automate data analysis, delivering actionable insights without data scientists needing to dig into every piece of data.

Predicting machine failures can now be done automatically and without manual intervention.

Today, being able to monitor in real time is accessible to the entire industry. However, effective monitoring to create a real return on investment requires understanding three issues facing Industry 4.0.

  • there is a wide variety of industrial equipment and sensors
  • the data generated by the sensors measuring the physical quantities of the equipment are mainly industrial time series, by nature complex to analyze
  • there are few or no occurrences of failures in the historical data, thus making the predictive models long to deploy

DiagFit, Amiral Technologies' failure prediction software addresses these three issues.

 

DiagFit, the blind failure prediction software

  • DiagFit is a predictive maintenance software that collects data from maintenance sensors in order to be able to create predictive models and detect anomalies.
  • DiagFit is agnostic: it analyzes data from all types of sensors and equipment
  • DiagFit works in no-code: no data science skills required
  • DiagFit works in blind mode: the software is based on the creation of a space of normality specific to the equipment, created from healthy data from its sensors. It is a process commonly called "anomaly detection" in machine learning which is based on so-called unsupervised artificial intelligence algorithms.

This means in practice that DiagFit can indicate the performance of each piece of equipment in a fleet of machines at a given time by automatically collecting and analyzing data from these machines. DiagFit's powerful prediction algorithms can generate updates and highlight where maintenance teams should focus their efforts in the short term, while helping to optimize future maintenance plans.

DiagFit can cut downtime in half, deliver an increase in productivity, and increase maintenance accuracy.

 

Industrial use cases