ReneWind

Renewable energy sources play an increasingly important role in the global energy mix, as the effort to reduce the environmental impact of energy production increases.

Out of all the renewable energy alternatives, wind energy is one of the most developed technologies worldwide. The U.S Department of Energy has put together a guide to achieving operational efficiency using predictive maintenance practices.

Predictive maintenance uses sensor information and analysis methods to measure and predict degradation and future component capability. The idea behind predictive maintenance is that failure patterns are predictable and if component failure can be predicted accurately and the component is replaced before it fails, the costs of operation and maintenance will be much lower.

The sensors fitted across different machines involved in the process of energy generation collect data related to various environmental factors (temperature, humidity, wind speed, etc.) and additional features related to various parts of the wind turbine (gearbox, tower, blades, break, etc.).

Objective

“ReneWind” is a company working on improving the machinery/processes involved in the production of wind energy using machine learning and has collected data of generator failure of wind turbines using sensors. They have shared a ciphered version of the data, as the data collected through sensors is confidential (the type of data collected varies with companies). Data has 40 predictors, 40000 observations in the training set and 10000 in the test set.

The objective is to build various classification models, tune them and find the best one that will help identify failures so that the generator could be repaired before failing/breaking and the overall maintenance cost of the generators can be brought down.

“1” in the target variables should be considered as “failure” and “0” will represent “No failure”.

The nature of predictions made by the classification model will translate as follows:

• True positives (TP) are failures correctly predicted by the model.
• False negatives (FN) are real failures in a wind turbine where there is no detection by model.
• False positives (FP) are detections in a wind turbine where there is no failure.

So, the maintenance cost associated with the model would be:

Maintenance cost = TP*(Repair cost) + FN*(Replacement cost) + FP*(Inspection cost) where,

• Replacement cost = $40,000
• Repair cost = $15,000
• Inspection cost = $5,000

Here the objective is to reduce the maintenance cost so, we want a metric that could reduce the maintenance cost.

• The minimum possible maintenance cost = Actual failures*(Repair cost) = (TP + FN)*(Repair cost)
• The maintenance cost associated with model = TP*(Repair cost) + FN*(Replacement cost) + FP*(Inspection cost)

So, we will try to maximize the ratio of minimum possible maintenance cost and the maintenance cost associated with the model.

The value of this ratio will lie between 0 and 1, the ratio will be 1 only when the maintenance cost associated with the model will be equal to the minimum possible maintenance cost.

Data Description

• The data provided is a transformed version of original data which was collected using sensors.
• Train.csv - To be used for training and tuning of models.
• Test.csv - To be used only for testing the performance of the final best model.
• Both the datasets consist of 40 predictor variables and 1 target variable