The dataset consists of data collected from heavy Scania trucks in everyday usage. The system in focus is the Air Pressure system (APS) which generates pressurised air that are utilized in various functions in a truck, such as braking and gear changes. The datasets' positive class consists of component failures for a specific component of the APS system. The negative class consists of trucks with failures for components not related to the APS. The data consists of a subset of all available data, selected by experts.
The training set contains 60000 examples in total in which 59000 belong to the negative class and 1000 positive class. The test set contains 16000 examples. There are 171 attributes per record.
The attribute names of the data have been anonymized for proprietary reasons. It consists of both single numerical counters and histograms consisting of bins with different conditions. Typically the histograms have open-ended conditions at each end. For example, if we measuring the ambient temperature "T" then the histogram could be defined with 4 bins where:
The attributes are as follows: class, then anonymized operational data. The operational data have an identifier and a bin id, like "Identifier_Bin". In total there are 171 attributes, of which 7 are histogram variables. Missing values are denoted by "na".
The total cost of a prediction model the sum of Cost_1 multiplied by the number of Instances with type 1 failure and Cost_2 with the number of instances with type 2 failure, resulting in a Total_cost. In this case Cost_1 refers to the cost that an unnecessary check needs to be done by an mechanic at an workshop, while Cost_2 refer to the cost of missing a faulty truck, which may cause a breakdown. Cost_1 = 10 and Cost_2 = 500, and , Total_cost = Cost_1 No_Instances + Cost_2 No_Instances.
Predict APS failure accurately and minimizes the cost of failures
I have written detailed blog about this case study.
Blog Link : APS Failure at Scania Trucks Data Set: Predicting if a Truck needs Servicing