Fraud Detection
Farix Machine Learning solution analyses equipment working conditions and predicts potential failures and downtime
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.
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 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
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
Neural networks, deep learning
Advantages of Models Based
ON DEEP LEARNING