Anomaly-TCM

Predictive Maintenance (PdM) is a strategy that uses advanced data analytics to predict equipment failures and maintain industrial machinery in good condition. Its goals are to minimize downtime, reduce operational costs, and ensure product quality. PdM methods are applicable across various industries, including steel manufacturing.

In steel production, cold rolling is a critical process that reduces the thickness of hot-rolled steel. Developing PdM methods for tandem cold mills (TCM) can significantly improve production efficiency. However, researchers often rely on real manufacturing data, which is typically unavailable, unlabeled, and noisy, making it difficult to validate and compare methods.

To overcome this, we created synthetic datasets for the cold rolling process to identify anomalies based on physical principles. These datasets were generated using a mathematical model of a 5-stand TCM, calculating key process parameters like rolling force, torque, speed, tension, gap, thickness reduction, and motor power. We introduced anomalies related to specific failures in the process.

We produced six diverse datasets, each with varying complexity, to enable benchmarking of machine learning-based PdM methods for the cold rolling process. Four different types of anomalies were introduced, which are related to a physics-based deviations in the process:

1. Anomaly in reduction scheme
2. Anomaly in work roll (increased work roll friction)
3. Anomaly in bearing (increased motor torque)
4. Anomaly in electric motor (decrease efficiency)

 The details of the datasets are provided below.

Each dataset is generated as a data stream, meaning the observations follow a chronological order, represented by increasing work roll mileage (which is reset after a predefined threshold). The table below provides details about the features and labels present in the datasets. Several features are recorded for each rolling stand, totaling 51 features. Apart from the anomaly related to reduction, the other anomalies are specific to individual stands, resulting in 16 anomaly labels in total.