Fault prediction

Fayrix Machine Learning solution analyses equipment working conditions and predicts potential failures and downtime

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WHY IS IT IMPORTANT
to Predict Equipment Failures?

Timely prediction of equipment faults and failures helps decrease costs for maintenance and repairs, as well as avoid total failure and unwanted repair and replacement costs. Subsequent financial losses can be not only direct, but also indirect - loss of customer confidence and deterioration of the image can cause a long-term decline in profits and outflow of customers. Using predictive analytics to predict breakdowns avoids such problems.

The predictive model answers two questions: what will break and when will break. Equipment failure prediction is carried out both on the basis of accumulated data and data received in real time.

Roadmap of Building a
FAULT PREDICTION MODEL

Data collection

Noise elimination
(PCA, autoencolder)

Creating attributes

Model balancing
(upsampling, SMOTE)

Model training

Model validation

Building forecasts

SOURCE DATA
for Forecasting

The more data sources are employed in searching for dependencies, the higher is the faults forecasting quality. "Useful" signal can be detected in very unexpected sources.

Specifics of a Fault
PREDICTION TASK