Fraud detection

Aavatar’s Machine Learning solution assesses equipment operating conditions to forecast potential failures and minimize downtime.

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WHY IS IT IMPORTANT

to Predict Equipment Failures?

Timely predictions of equipment faults and failures significantly reduce maintenance and repair costs while preventing total breakdowns and the associated expenses of repairs and replacements. The financial impact of such issues extends beyond direct costs; they can also result in a loss of customer trust and a tarnished reputation, leading to a long-term decline in profits and potential customer attrition. By leveraging predictive analytics to anticipate breakdowns, businesses can mitigate these risks effectively.

The predictive model addresses two critical questions: what equipment is likely to fail and when that failure might occur. It predicts equipment breakdowns by analyzing both historical data and real-time information.

Step 1: Data collection

Step 2: Noise elimination

Step 3: Creating attributes

Step 4: Model balancing

Step 5: Model training

Step 6: Model validation

Step 7:Building forecasts

SOURCE DATA

for Forecasting

The quality of fault forecasting improves significantly when multiple data sources are utilized to identify dependencies. Insights can often be gleaned from unexpected sources, revealing valuable signals that contribute to more accurate predictions.

Specifics of a Fault

PREDICTION TASK

Lack of balance between positive and negative cases

Insufficient of relevant data from equipment sensors (no values or the same values)

Data is of high-dimentionality, but disparse

In how much time is a fault going to occur?

Linear regression

Gradient boosting on decision trees – regression

Neural networks, deep learning

Classification (will/will not break down) during a particular time period

Logistic regressionЛогистическая регрессия

Gradient boosting on decision trees – classification