A Novel LSTM Pipeline to Detect Anomalies in Manufacturing Production (Datasets)

This dataset includes the relevant data for the journal article titled 'A Novel LSTM Pipeline to Detect Anomalies in Manufacturing Production'. In this paper, we present a novel anomaly detection method using a semi-supervised LSTM forecasting approach to highlight process anomalies in a complex, real-world dataset in an automotive manufacturing setting. This data includes two time-series subsets, each with 5000 labeled observations. Both subsets were recorded using an inbuilt torque-time sensor within a DC nut runner tool used to fasten nuts onto various parts throughout the assembly line. The resultant torque time data was labeled by a test engineer and domain expert using the methods outlined in the paper. The labels are denoted in column 1, where 1 = Nominal, 2 = Anomaly No Concern, and -1 = True Anomaly.