<p>The <b>"Crimp Force Curve Dataset"</b> is a comprehensive collection of univariate time series data representing crimp force curves recorded during the manufacturing process of crimp connections. This dataset has been designed to support a variety of applications, including anomaly detection, fault diagnosis, and research in data-driven quality assurance.</p> <p>A salient feature of this dataset is the presence of high-quality labels. Each crimp force curve is annotated both by a state-of-the-art crimp force monitoring system - capable of binary anomaly detection - and by domain experts who manually classified the curves into detailed quality classes. The expert annotations provide a valuable ground truth for training and benchmarking machine learning models beyond anomaly detection.</p> <p>The dataset is particularly well-suited for tasks involving time series analysis, such as training and evaluating of machine learning algorithms for quality control and fault detection. It provides a substantial foundation for the development of generalisable, yet domain-specific (crimping), data-driven quality control systems.</p> <p>The data is stored in a Python pickle file <code>crimp_force_curves.pkl</code>, which is a binary format used to serialize and deserialize Python objects. It can be conveniently loaded into a pandas DataFrame for exploration and analysis using the following command:</p> <p><code>df = pd.read_pickle("crimp_force_curves.pkl")</code></p> <p>The DataFrame consists of 2,439 rows (force curves) and 11 columns:</p> <ul> <li><strong>CrimpID:</strong> Unique identifier assigned by the crimp force monitoring system as integer.</li> <li><strong>Wire_cross-section_conductor:</strong> Wire cross-section of the conductor as float [mm²].</li> <li><strong>Force_curve_raw:</strong> Raw force curve with 3,566 datapoints as NumPy array with integer values [Force sensor value].</li> <li><strong>Force_curve_baseline:</strong> Baseline curve for comparability as NumPy array with integer values [Force sensor value].</li> <li><strong>Force_curve_RoI:</strong> Region of interest curve for crimp quality evaluation with 500 datapoints as NumPy array with integer values [Force sensor value].</li> <li><strong>Main_label_string:</strong> Quality class (<em>OK</em>, <em>Missing Strands</em>, or <em>Crimped Insulation</em>) manually assigned by the authors based on the preparation steps, as strings.</li> <li><strong>Main_label_encoded:</strong> Encoded quality classes (<em>OK</em>, <em>Missing Strands</em>, or <em>Crimped Insulation</em>) as integers from 0 to 2.</li> <li><strong>Sub_label_string:</strong> Fine-grained quality class (<em>OK</em>, <em>One Missing Strand</em>, <em>Two Missing Strands</em>, <em>Three Missing Strands</em>, or <em>Crimped Insulation</em>) manually assigned by the authors based on the preparation steps, as strings.</li> <li><strong>Sub_label_encoded:</strong> Encoded sub-classes (<em>OK</em>, <em>One Missing Strand</em>, <em>Two Missing Strands</em>, <em>Three Missing Strands</em>, <em>Crimped Insulation</em>) as integers from 0 to 4.</li> <li><strong>Binary_label_encoded:</strong> Binary encoded quality classes (<em>OK</em>, <em>NOK</em>) for anomaly detection, manually assigned by the authors as integers from 0 to 1.</li> <li><strong>CFM_label_encoded:</strong> Binary encoded quality classes (<em>OK</em>, <em>NOK</em>) assigned by the crimp force monitoring system as integers from 0 to 1.</li> </ul> <p>This dataset is a valuable resource for researchers and practitioners in manufacturing engineering, computer science, and data science who are working at the intersection of quality control in manufacturing and machine learning.</p>