Air Pressure System Performance and Failure Metrics

Collection of data for air pressure system (APS) failures in Scania trucks, detailing individual sensor readings and operational data. The dataset encompasses critical metrics related to component failures, allowing for predictive analysis aimed at minimizing maintenance costs and improving vehicle performance.

The dataset consists of data collected from heavy Scania trucks in everyday usage, specifically targeting the air pressure system (APS) responsible for generating pressurized air used in functions such as braking and gear changes. The dataset includes a positive class representing component failures specific to the APS system and a negative class for trucks experiencing failures in unrelated components. This expert-selected subset offers valuable insights into APS performance.

Training Set: Contains 60,000 examples, with 59,000 belonging to the negative class and 1,000 to the positive class. Test Set: Comprises 16,000 examples. Attributes: Each record features 171 attributes, including anonymized operational data and various numerical counters. The data includes both single numerical values and histograms with open-ended bins representing different operational conditions. Missing Values: Denoted as "an."

Timestamp: Date and time of data collection Truck Identifier: Unique ID for each truck Sensor Readings: Data from various sensors monitoring APS performance Failure Indicator: Classification of whether a failure occurred Temperature Bins: Ambient temperature ranges affecting APS performance Cost Metrics: Cost implications associated with misclassifications Operational Metrics: Numerical counters related to truck operation Histogram Variables: Data reflecting conditions categorized into bins Acknowledgements: This dataset is part of the APS Failure and Operational Data for Scania Trucks and was imported from the UCI Machine Learning Repository.

The dataset can be utilized for predictive maintenance modeling, enabling manufacturers and fleet operators to foresee potential failures based on historical sensor data. Researchers may analyze the data to develop algorithms that improve APS reliability, while businesses can leverage these insights to optimize operational costs.

The total cost of a prediction model is calculated as the sum of Cost_1 multiplied by the number of instances with type 1 failures and Cost_2 multiplied by the number of instances with type 2 failures. In this context, Cost_1 represents the expense incurred from unnecessary checks performed by mechanics, while Cost_2 reflects the cost of missing a faulty truck, which could lead to breakdowns. The model encourages exploration of predictive techniques that minimize these costs.

Who can use it: Fleet operators, automotive researchers, and predictive maintenance developers can use this dataset to enhance understanding of APS-related failures and their impacts on operational efficiency.

The dataset can be employed to build predictive models aimed at identifying potential APS failures, thereby reducing downtime and repair costs. Analysts can explore relationships between sensor readings and failure rates to improve truck maintenance strategies and enhance overall performance.